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This paper proposes a robust deblurring algorithm for noisy images with just noticeable blur (JNB). Natural images are usually accompanied by JNB and noise when viewed under pixel scale resolution, but are easily neglected. In this paper, we describe the JNB phenomenon and briefly analyze its causes. An image-gradient-related constrained factor is introduced in our algorithm based on the prior of image noise’s low gradient distribution. Richardson-Lucy method is also adopted to reduce algorithm’s time cost. For large-size cellphone images, we put forward an effective image segmentation model suitable for space-variant blur. Experiments show that our method obtains high-quality deblurring images and are competitive to state-of-the-art deblurring algorithms. [ABSTRACT FROM AUTHOR]

Quantum edge detection is one of the important part of quantum image processing. In this paper, a quantum edge detection algorithm is designed for the quantum representation of multi-wavelength image (QRMW) model. The algorithm includes all stages of filtering, enhancement and detection. The proposed algorithm is also designed to apply any filtering operation to QRMW images, not only for a particular filtering operation. The proposed algorithm aims to solve the problems that quantum edge detection algorithms in the literature have processing only for a particular operator and noise reduction. Moreover, the algorithm aims to perform operations more efficiently by using less resources. Low-pass filter (LPF) smoothing operators are applied in the filtering stage for the noise reduction problem. In order to apply all filtering operations to the image, arithmetic operators that can operate with all signed integers are used in the algorithm. The operators Sobel, Prewitt and Scharr in the enhancement stage and the gradient method in the detection stage are used for both verification of the proposed algorithm and comparisons with the existing algorithms. A method with quantitative outcomes is shown to evaluate the performance of the edge detection algorithms. Analysis of the simulations performed on sample images with different operators. The circuit complexity of the algorithm is presented and the comparisons are made with the existing studies. The superiority of the proposed algorithm and its flexibility to be used in other studies are clearly demonstrated by analysis. [ABSTRACT FROM AUTHOR]

With the development of quantum computing, the application of it to image processing has lots of advantages compared to classical image processing. In this paper, we propose a scheme to extract the interest point in quantum images. Interest point is a kind of feature point which can help to identify the target object in the image. Our scheme is based on the idea of Luminance Contrast (LC) algorithm. The scheme computes the absolute value of gray level differences between a pixel and the others, and then adds all these differences together. The sum is defined as a saliency. After computing the saliency of every pixel, we label the pixels with the maximal saliency as the interest points. The algorithm has pretty good performance and its time complexity is much better than the classical algorithm in same conditions, which provides a new idea for the extraction of image interest point. [ABSTRACT FROM AUTHOR]

Rail fastener status recognition and detection are key steps in the inspection of the rail area status and function of real engineering projects. With the development of and widespread interest in image processing techniques and deep learning theory, detection methods that combine the two have yielded promising results in practical detection applications. In this paper, a semantic-segmentation-based algorithm for the state recognition of rail fasteners is proposed. On the one hand, we propose a functional area location and annotation method based on a salient detection model and construct a novel slab-fastclip-type rail fastener dataset. On the other hand, we propose a semantic-segmentation-framework-based model for rail fastener detection, where we detect and classify rail fastener states by combining the pyramid scene analysis network (PSPNet) and vector geometry measurements. Experimental results prove the validity and superiority of the proposed method, which can be introduced into practical engineering projects. [ABSTRACT FROM AUTHOR]

This review paper systematizes a detailed, didactical and own construction of the singular value decomposition (SVD). The theory is complemented with relevant applications to image processing. Also, the algorithms of those applications and numerical examples are shown. The way how the document is organized allows it to be useful for students and researchers with interest in the SVD and its applications to image processing. [ABSTRACT FROM AUTHOR]

Personal identification has become one of the most important terms in our society regarding access control, crime and forensic identification, banking and also computer system. The fingerprint is the most used biometric feature caused by its unique, universality and stability. The fingerprint is widely used as a security feature for forensic recognition, building access, automatic teller machine (ATM) authentication or payment. Fingerprint recognition could be grouped in two various forms, verification and identification. Verification compares one on one fingerprint data. Identification is matching input fingerprint with data that saved in the database. In this paper, we measure the performance of the memetic algorithm to process the image fingerprints dataset. Before we run this algorithm, we divide our fingerprints into four groups according to its characteristics and make 15 specimens of data, do four partial tests and at the last of work we measure all computation time. [ABSTRACT FROM AUTHOR]

• Performance analysis of different video-based eye blinking detection. • Development of a low-cost imaging acquisition system. • Quality metrics analysis of proposed algorithms. • Performance comparison between RGB and Grayscale images. • The red channel of the RGB camera achieves in our experiments a 100 % eye blink detection rate for event detection. The eye is considered a rich source for gathering information on our daily lives. In addition, the necessity to classify the eye as open or closed and detect eye blinks is increasing in various fields. For instance, it has been proven that eye blinks can be related to different ophthalmological conditions. Nevertheless, accurate, and robust blink estimation poses significant challenges to the efficacy of real-time applications due to the variability of eye images and light conditions. In this paper eye blinking has been monitored using a chin rest and an iOS smartphone attached to a cartesian machinery. Five video-based eye blink algorithms to classify frames have been analyzed and compared in terms of performance. To evaluate the results, different widely used statistical metrics have been employed. The results show that the proposed techniques successfully detect eye blinks, whereas the frame classifier shows different scores depending on the algorithm used. [ABSTRACT FROM AUTHOR]

[Display omitted] The unstable statuses in machining, such as chatter and severe forced vibration, limit manufacturing productivity and quality. Although there have been plentiful studies on the on-line sensing signals and off-line surface images to identify these statuses qualitatively, the quantification of chatter severity, which is of great significance on machining stability, has been rarely reported. Workpiece surface morphology is the direct result and thus the objective criteria of unstable machining. This paper proposes a new algorithm to quantify the chatter status based on the milled surface image analysis of end-milling. Besides, the proposed method can also identify the stable, forced vibration, and chatter statuses. Specifically, this proposed algorithm extracts two indexes, called Index_Dmean and Index_Emean , via processing the milled surface image. The definitions and calculations of these two indexes are given in Section 2. Index_Dmean is used to differentiate stable and unstable surfaces and quantify the severity of chatter status, and Index_Emean can distinguish between forced vibration and chatter surfaces. Experimental results demonstrate the availability of the proposed algorithm on the chatter quantification and milling instability classification performance. In addition, an applicable on-line vision system with satisfying performance based on the algorithm is developed. [ABSTRACT FROM AUTHOR]

• A layout plane is a closed loop surrounded by layout keypoints. • The wall structure can be parametrization as an undirected graph. • Layout keypoint prediction is relative to human pose estimation. • Cuboid room assumption is limited in real life. Room layout estimation aims to predict the spatial structure of a room from a single image. Most existing methods relying on 2D cues are suitable for cuboid rooms, but non-cuboid rooms. 3D layout estimation methods can reconstruct 3D models of general rooms, but they are trained with depth information on high collection costs, consume large computation resources, and run slowly. This paper considers an undirected graph representation method of the general room layouts, which includes cuboid and non-cuboid rooms consisting of a single ceiling, a single floor, and multiple walls. To this end, we first predict the positions of the layout vertices and then use the network automatically learn the connection relationship between the vertices. The final layout is obtained through a simple layout inference post-processing algorithm. The experimental results both on cuboid and non-cuboid datasets validate the effectiveness and efficiency of our method. The code is available at https://github.com/Hui-Yao/2D-graph-layout-estimation. [ABSTRACT FROM AUTHOR]

Due to the ill-posed nature of image restoration, seeking a meaningful image prior is still a great challenge in the field of image processing. The total variation with overlapping group sparsity (OGS-TV) has been successfully applied for image denoising/deblurring. In this paper, we further study the overlapping group sparsity of the image gradient. The sparsity is measured by the ℓ q quasi-norm (0 < q < 1). The proposed regularizer comes down to the well-known hyper-Laplacian prior if the overlapping group size is 1. Although it seems to be a simple extensive study compared with the previous works, its regularization capability and corresponding mathematical problems are still in demand for imaging science. To solve the non-convex and non-smooth minimization problem, we use the alternating direction method of multipliers as the main algorithm framework. The difficult inner subproblem is tackled by the majorization-minimization method with the sophisticatedly derived majorizer. We carry out some numerical experiments to demonstrate the effectiveness of the proposed regularizer in terms of PSNR and SSIM values. [ABSTRACT FROM AUTHOR]

• A linear time algorithm is given to find relative orthogonal convex hull. • The algorithm is combinatorial in nature. • The algorithm is useful for image processing algorithms and shape analysis of digital objects. A linear time combinatorial algorithm to construct the relative orthogonal convex hull of the inner isothetic cover of a digital object (hole-free) with respect to its outer isothetic cover is presented in this paper. The algorithm first constructs the inner and the outer isothetic covers of the digital object which is the input to the algorithm and then constructs the relative orthogonal convex hull from the inner isothetic cover and the outer isothetic cover. The algorithm can also be used to construct the relative orthogonal convex hull of one orthogonal polygon with respect to another orthogonal polygon which encloses the first polygon entirely where the input will be two orthogonal polygons one containing the other instead of the digital object. The proposed algorithm first finds the concavities of the inner polygon and checks for intruded concavities of the outer polygon. A combinatorial technique is used to obtain the relative orthogonal convex hull. The efficiency and the correctness of the algorithm are verified as it is tested on various types of digital objects. [ABSTRACT FROM AUTHOR]

In this paper, we present a new evolution-based algorithm that optimizes cell detection image processing workflows in a self-adaptive fashion. We use evolution strategies to optimize the parameters for all steps of the image processing pipeline and improve cell detection results. The algorithm reliably produces good cell detection results without the need for extensive domain knowledge. Our algorithm also needs no labeled data to produce good cell detection results compared to the state-of-the-art neural network approaches. Furthermore, the algorithm can easily be adapted to different applications by modifying the processing steps in the pipeline and has high scalability since it supports multithreading and computation on graphical processing units (GPUs). [ABSTRACT FROM AUTHOR]

The paper focuses on the opportunity to use star sensors to help space situational awareness and space surveillance. Catalogs of orbiting satellites around Earth are usually established on ground-based measurements that rely on optical or radar data provided by instruments on Earth. However, space-based observations offer new opportunities, as they are unrelated to the weather and the circadian rhythm to which the ground system is subjected. Consequently, space-based optical observation systems can provide data without atmospheric distortion from different observer-to-target distances, enhancing the possibility to detect and catalog resident space objects, i.e. satellites and space debris. This work deals with the feasibility study of an innovative strategy, which consists in the use of a star sensor with a dedicated algorithm to run directly on-board. This approach minimizes the impact on the platform so that, from a theoretical point of view, every satellite with a star sensor can be used as a space surveillance observer. The output of the algorithm consists of a database of pre-processed objects, which are then post-processed on ground. Numerical tests on synthetic images provided by a high-fidelity simulator demonstrate the applicability of the proposed approach. The study has been conducted within the framework of the Italian Space Agency project SPOT - Star sensor image Processing for orbiting Objects deTection. • A space-based detection of resident space objects (RSOs) is proposed using star sensors. • A novel on-board algorithm for RSOs detection is described. • An analysis of the performances is reported using a high fidelity simulator. • Radial, tangential and orthogonal observations have been compared. • Results show the feasibility of the described approach. [ABSTRACT FROM AUTHOR]

Image registration is a common task in remote sensing, consisting in aligning different images of the same scene. In the particular case of hyperspectral images, the exploitation not only of the spatial information contained in the image but also of the spectral information helps to improve the registration. An example of registration method exploiting all the information contained in the images is HSI–KAZE, which is based on feature detection and detects keypoints using nonlinear diffusion filtering. The algorithm is oriented toward extreme situations in which the images are very different in terms of scale, rotation and displacement. In this paper, an efficient implementation of the HSI–KAZE algorithm on GPU using CUDA is proposed. A detailed analysis of the implementation as well as a performance comparison to an OpenMP multicore implementation is also presented. The resulting algorithm is suitable for on-board processing of high-resolution images. [ABSTRACT FROM AUTHOR]

In this paper, we present a new algorithm for the detection of distorted and overlapping circlelike objects in noisy grayscale images. Its main step is an edge detection using rotated difference kernel estimators. To the resulting estimated edge points, circles are fitted in an iterative manner using a circular clustering algorithm. A new measure of similarity can assess the performance of algorithms for the detection of circlelike objects, even if the number of detected circles does not coincide with the number of true circles. We apply the algorithm to scanning electron microscope images of a high‐velocity oxygen fuel (HVOF) spray process, which is a popular coating technique. There, a metal powder is fed into a jet, gets accelerated and heated up by means of a mixture of oxygen and fuel, and finally deposits as coating upon a substrate. If the process is stopped before a continuous layer is formed, the molten metal powder solidifies in form of small, almost circular so‐called splats, which vary with regard to their shape, size, and structure and can overlap each other. As these properties are challenging for existing image processing algorithms, engineers analyze splat images manually up to now. We further compare our new algorithm with a baseline approach that uses the Laplacian of Gaussian blob detection. It turns out that our algorithm performs better on a set of test images of round, spattered, and overlapping circles. [ABSTRACT FROM AUTHOR]

In this paper, we have proposed a computationally efficient algorithm to detect splicing (copy-create) forgery, our proposed method is developed by using differential evolution and wavelet decomposition, the differential evolution algorithm automatically generates customized parameter values of tampered images, and wavelet decomposition is used to process large-size images under block-based framework. Our proposed method is resilient to distortions, such as the addition of Gaussian noise, scaling and compression of the forged images. [ABSTRACT FROM AUTHOR]

DELPHINUS is a camera system mounted on EQUULEUS, which is planned to be launched using NASA's Space Launch System EM-1 in 2021. DELPHINUS aims to investigate size distribution, influx ratio, and daily variation of meteoroids in the cislunar space through observations of lunar impact flashes (LIFs) from the far side of the moon. DELPHINUS will observe the moon's surface with the 60-fps camera modules to capture the flashes that are short duration phenomena. All image data cannot be downlinked due to constraints in memory size and communication capability. Therefore, an on-board image processing algorithm was developed to reduce downlinked data size by extracting only necessary pixel data including LIFs. Three experiments using three simulators were demonstrated to verify the real-time processing performance and detection capability. This paper reports the details of the proposed algorithm and the verification results. [ABSTRACT FROM AUTHOR]

Face recognition (FR) is a hotspot in pattern recognition and image processing for its wide applications in real life. One of the most challenging problems in FR is single sample face recognition (SSFR). In this paper, we proposed a novel algorithm based on nonnegative sparse representation, collaborative presentation, and probabilistic graph estimation to address SSFR. The proposed algorithm is named as Nonnegative Sparse Probabilistic Estimation (NNSPE). To extract the variation information from the generic training set, we first select some neighbor samples from the generic training set for each sample in the gallery set and the generic training set can be partitioned into some reference subsets. To make more meaningful reconstruction, the proposed method adopts nonnegative sparse representation to reconstruct training samples, and according to the reconstruction coefficients, NNSPE computes the probabilistic label estimation for the samples of the generic training set. Then, for a given test sample, collaborative representation (CR) is used to acquire an adaptive variation subset. Finally, the NNSPE classifies the test sample with the adaptive variation subset and probabilistic label estimation. The experiments on the AR and PIE verify the effectiveness of the proposed method both in recognition rates and time cost. [ABSTRACT FROM AUTHOR]

This paper proposes a Hilbert stereo reconstruction algorithm based on depth feature and stereo matching to solve the problem of occlusive region matching errors, namely, the Hilbert stereo network. The traditional stereo network pays more attention to disparity itself, leading to the inaccuracy of disparity estimation. Our design network studies the effective disparity matching and refinement through reconstruction representation of Hilbert's disparity coefficient. Since the Hilbert coefficient is not affected by the occlusion and texture in the image, stereo disparity matching can conducted effectively. Our network includes three sub-modules, namely, depth feature representation, Hilbert cost volume fusion, and Hilbert refinement reconstruction. Separately, texture features of different depth levels of the image were extracted through Hilbert filtering operation. Next, stereoscopic disparity fusion was performed, and then Hilbert designed to refine the difference regression stereo matching solution was used. Based on the end-to-end design, the structure is refined by combining the depth feature extraction module and Hilbert coefficient disparity. Finally, the Hilbert stereo matching algorithm achieves excellent performance on standard big data set and is compared with other advanced stereo networks. Experiments show that our network has high accuracy and high performance. [ABSTRACT FROM AUTHOR]

In this paper, a bilateral spectrogram filtering (BSF)-based optimally modified log-spectral amplitude (OMLSA) estimator for single-channel speech enhancement is proposed, which can significantly improve the performance of OMLSA, especially in highly non-stationary noise environments, by taking advantage of bilateral filtering (BF), a widely used technology in image and visual processing, to preprocess the spectrogram of the noisy speech. BSF is capable of not only sharpening details, removing unwanted textures or background noise from the noisy speech spectrogram, but also preserving edges when considering a speech spectrogram as an image. The a posteriori signal-to-noise ratio (SNR) of OMLSA algorithm is estimated after applying BSF to the noisy speech. Besides, in order to reduce computing costs, a fast and accurate BF is adopted to reduce the algorithm complexity O(1) for each time-frequency bin. Finally, the proposed algorithm is compared with the original OMLSA and other classic denoising methods using various types of noise with different signal-to-noise ratios in terms of objective evaluation metrics such as segmental signal-to-noise ratio improvement and perceptual evaluation of speech quality. The results show the validity of the improved BSF-based OMLSA algorithm. [ABSTRACT FROM AUTHOR]

Segmentation is considered as one of the most significant tasks in image processing. It consists of separating the pixels into different segments based on their intensity level according to threshold values. Selecting the optimal threshold value is the key to best quality segmentation. Multilevel thresholding (MT) is an essential approach for image segmentation, and it has become very popular during the past few years, but while increasing the level of thresholds, computational complexity also increases exponentially. In order to overcome this drawback, several metaheuristics-based algorithms have been used for determining the optimal MT levels. Learning enthusiasm-based teaching–learning-based optimization (LebTLBO) is a recently developed efficient, simple-to-implement and computationally inexpensive algorithm. It simulates the behaviors of the teaching and learning process in a classroom and gives the probability of getting the amount of information by the learner (student) from the educator. In this paper, LebTLBO is applied on ten standard test images having a diverse histogram, which are taken from Berkeley Segmentation Dataset 500 (BSDS500) (Martin et al. in a database of human segmented natural images and its application to evaluate segmentation algorithms and measure ecological statistics, 2001) benchmark image set for segmentation. The search capability of the algorithm is combined with Otsu and Kapur's entropy MT objective functions for image segmentation. The proposed approach is compared with the existing state-of-the-art optimization algorithms such as MTEMO, GA, PSO and BF for both Otsu and Kapur's entropy methods. Qualitative experimental outcomes demonstrate that LebTLBO is highly efficient in terms of performance metrics such as PSNR, mean, threshold values, number of iterations taken to converge and image segmentation quality. [ABSTRACT FROM AUTHOR]

Semi-global matching (SGM) is favored for high accuracy real-time stereo matching design as it achieves a good trade-off between disparity image quality and computational complexity. Nevertheless, most of previous SGM designs so far are restricted to the real-time processing of small image resolution and disparity range, or achieve high throughput by simplifying the original algorithm at the penalty of significant disparity image quality degradation. We analyze that the major challenge to efficient SGM design is its memory architecture, including both on-chip memory cost and off-chip memory bandwidth. We address the memory architecture challenge by algorithm and architecture co-design. Based on two observed features of SGM algorithm, i.e. incompleteness and inaccuracy, this paper proposes several efficient techniques to reduce on-chip memory cost and compress off-chip memory bandwidth respectively. Moreover, we also design high throughput and pipelined architecture to implement the proposed techniques. The disparity image quality and hardware efficiency of the proposed SGM design are evaluated on both KITTI2015 and Middlebury V3 stereo datasets. Evaluation results demonstrate that, the throughput of the proposed circuit designs can easily achieve 1080P@30fps at the disparity range of 128, and can reduce the on-chip memory cost and off-chip memory bandwidth by up to $4\times $ and $2\times $ respectively while achieving better or the same disparity image quality, compared with the best reference design techniques. [ABSTRACT FROM AUTHOR]

Image synthesis is currently one of the most addressed image processing topic in computer vision and deep learning fields of study. Researchers have tackled this problem focusing their efforts on its several challenging problems, e.g. image quality and size, domain and pose changing, architecture of the networks, and so on. Above all, producing images belonging to different domains by using a single architecture is a very relevant goal for image generation. In fact, a single multi-domain network would allow greater flexibility and robustness in the image synthesis task than other approaches. This paper proposes a novel architecture and a training algorithm, which are able to produce multi-domain outputs using a single network. A small portion of a dataset is intentionally used, and there are no hard-coded labels (or classes). This is achieved by combining a conditional Generative Adversarial Network (cGAN) for image generation and a Meta-Learning algorithm for domain switch, and we called our approach MetalGAN. The approach has proved to be appropriate for solving the multi-domain label-less problem and it is validated on facial attribute transfer, using CelebA dataset. [ABSTRACT FROM AUTHOR]

Aiming at the problem of edge point defocusing of arbitrary configuration bistatic SAR based on traditional Taylor series expansion slant range method, this paper developed a 2D frequency domain imaging algorithm based on Legendre orthogonal polynomial approximation. This algorithm derived the 2D spectrum of the point target based on the Legendre polynomial expansion of the slant range function, which eliminated the coupling of range and azimuth of the echo signal and made the imaging processing more efficient. 2D frequency domain imaging algorithm imaged the point target of scene region in 2D frequency domain. The algorithm improved the focusing effect of scene edge points and increased the focusing depth of scene edge points. The theoretical derivation and simulation results verify the effectiveness and feasibility of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Image inpainting is an effective method to repair damaged image. In this paper, we mainly studied some typical image inpainting models and analyzed these corresponding repair qualities. Firstly, we studied three typical algorithms based on PDE (Partial Differential Equation) model to repair images whose damaged areas are relative small. And then we studied the image inpainting algorithm based on texture synthesis. This kind of inpainting algorithm is suitable for image with large damaged area. Finally, we studied Criminisi inpainting algorithm based on image segmentation in detail, on which we proposed an improved priority formula image repair algorithm, the simulation results indicate that the improved algorithm of this paper showed better performance in repair quality from both subjective and objective aspects. [ABSTRACT FROM AUTHOR]

Analyzing geological drilling hole images acquired by Axial View Panoramic Borehole Televiewer (APBT) is a key step to explore the geological structure in a geological exploration. Conventionally, the borehole images are examined by technicians, which is inefficient and subjective. In this paper, three dominant types of borehole-wall images on coal-rock mass structure, namely, border images, fracture images and intact rock mass images are mainly studied. The traditional image classification methods based on unified feature extraction algorithm and single classifier is not effect for the borehole images. Therefore, this paper proposes a novel two-stage classification approach to improve the classification performance of borehole images. In the first-stage classification, the border images are identified from three kinds of images based on texture features and gray-scale histograms features. For the remaining two types of images, in the second-stage classification, Gabor filter is first applied to segment the region of interest (ROI) (such as microfracture, absciss layer and horizontal cracks, etc.) and the central interference region. Then, using the same feature vector after eliminating the central interference region, fracture images are separated from intact rock mass images. We test our two-stage classification system with real borehole images. The results of experimental show that the two-stage classification method can effectively classify three major borehole-wall images with the correction rate of 95.55% in the first stage and 95% in the second stage. [ABSTRACT FROM AUTHOR]

This paper aims to present a Decentralised Cooperative Simultaneous Localization and Mapping (DCSLAM) solution based on 2D laser data using an Adaptive Covariance Intersection (ACI). The ACI-DCSLAM algorithm will be validated on a swarm of Unmanned Ground Vehicles (UGVs) receiving features to estimate the position and covariance of shared features before adding them to the global map. With the proposed solution, a group of (UGVs) will be able to construct a large reliable map and localise themselves within this map without any user intervention. The most popular solutions to this problem are the EKF-SLAM, Nonlinear H-infinity SLAM and the FAST-SLAM. The former suffers from two important problems which are the poor consistency caused by the linearization problem and the calculation of Jacobian. The second solution is the which is a very promising filter because it doesn’t make any assumption about noise characteristics, while the latter is not suitable for real time implementation. Therefore, a new alternative solution based on the smooth variable structure filter (SVSF) is adopted. Cooperative adaptive SVSF-SLAM algorithm is proposed in this paper to solve the UGVs SLAM problem. Our main contribution consists in adapting the SVSF filter to solve the Decentralised Cooperative SLAM problem for multiple UGVs. The algorithms developed in this paper were implemented using two mobile robots Pioneer , equiped with 2D laser telemetry sensors. Good results are obtained by the Cooperative adaptive SVSF-SLAM algorithm compared to the Cooperative EKF/-SLAM algorithms, especially when the noise is colored or affected by a variable bias. Simulation results confirm and show the efficiency of the proposed algorithm which is more robust, stable and adapted to real time applications. [ABSTRACT FROM AUTHOR]

Video quality assessment (VQA) is becoming increasingly important as a comprehensive measure of video quality. This paper proposes a full-reference VQA (FR-VQA) algorithm based on the motion structure partition similarity of spatiotemporal slice (STS) images. To achieve this objective, a number of FR-image quality assessment algorithms were applied slice by slice to video STS images to compare their performance of detecting structure similarity of STS images. The algorithm that performed the best was selected to detect the similarity between motion-partitioning STS images. Next, as motion objects in the video sequence were found to have different influences on the prediction performance in terms of moving speed and track, the STS images were divided into simple and complex motion regions, and their contributions to the VQA task determined. Consequently, a promising effective and efficient VQA model, called STS-MSPS, is also proposed. Experimental evaluations conducted based on various annotated VQA databases indicate that the proposed STS-MSPS achieves state-of-the-art prediction performances in terms of correlations with subjective evaluation and statistical significance tests. This paper also shows that STS images by themselves provide sufficient information for VQA tasks and that the proposed complex motion region of an STS image is predominantly responsible for yielding a high-precision model. © 2018 SPIE and IS&T [DOI: 10.1117/1.JEI.27.3.033019] [ABSTRACT FROM AUTHOR]

A large amount of data, provided in the form of video data, is acquired during manned inspections flights of electric power lines. This data is analyzed by expert human inspectors to detect faults in the power lines infrastructure and prepare the inspection reports. This process is extremely time consuming, very expensive and prone to human error. In this paper, we present PoLIS: the Power Line Inspection Software, which has been developed with the objective of assisting the analysis of the data acquired during inspection flights. PoLIS is based on the cooperation between computer vision and machine learning techniques to automatically process video sequences acquired during inspection flights, resulting in a set of representative images per electric tower which we call Key Frames. These representative images can then be used for inspection purposes, leading to a drastic reduction of the human operators’ workload. At the core of the strategy lies an electric tower detector, which is in charge of estimating the location of the towers within the images based on the combination of a sliding window search technique and a supervised classifier. The location of the tower is then tracked using a tracking-by-registration algorithm based on direct methods, estimating the position of the tower in different images. Finally, different criteria are applied for defining whether the image corresponds to a Key Frame image or not. Extensive evaluation of the proposed strategy is conducted using videos acquired during manned helicopter inspections. The videos constituting this database contain several thousand frames representing both medium and high voltage power transmission lines in the infra-red (IR) and visible spectra. The obtained results show that the proposed strategy can reduce the large amount of data present in the inspection videos to a few Key Frames for each tower. It is also demonstrated that the learning-based approach proposed in PoLIS is appropriate for detecting electric towers, a process which is made faster and more robust by coupling it with a tower tracking algorithm. A Graphical User Interface allowing the application of PoLIS to user-provided videos is also presented in this paper, illustrating the whole process and the automated generation of an inspection report. [ABSTRACT FROM AUTHOR]

Ochotona curzoniae is an endemic species and key species in the alpine meadow of the Tibetan Plateau and also it is a main kind of organism that destroys the grassland ecology of the plateau. In order to prevent the dangers of the ochotona curzoniae, we need to study the living habits of ochotona curzoniae and investigate the degree of harm of ochotona curzoniae, and then we can control the number of ochotona curzoniae through the effective preventive measures. With the development of sensing technology and image processing, we can provide an objective basis through intelligent monitoring system to control the damage of ochotona curzoniae. The object detection of ochotona curzoniae is a key technology in the intelligent monitoring equipment because it can provide the object contour feature for behavior analysis of ochotona curzoniae. The object detection of ochotona curzoniae is very difficult, because the ochotona curzoniae video possesses the characteristics of complex background, low contrast, the object color with intensity inhomogeneity, diversity and mutability. The traditional object detection method cannot extract the object contours accurately. This paper presents a fast object detection method based on space-time domain. Firstly, the centroid position of the object in the current frame image is determined by the background subtraction, and then the rough segmented image and the initial contour are obtained based on the centroid position. The rough segmented image is segmented by the improved Chan-Vese model, and then we can obtain the object contours. In view of the fact that the level set function needs to be initialized in the process of improved Chan-Vese model, and the initialized computation is enhanced with the increase of the image scale, the centroid of the object is taken as the center to intercept the image containing the object as the roughly segmented image. Then, the improved Chan-Vese model is used to segment the roughly segmented image, so as to reduce the time consumption of Chan-Vese model segmentation. In addition, as Chan-Vese model can’t fully segment the image of object whose color is diverse and mutable, we use the improved Chan-Vese model to segment the roughly segmented image. The internal pixels of image evolution contours were processed by K-means clustering, and the clustering center point values were obtained. The internal fitting values of Chan-Vese model were constructed by the clustering center point values and the image mean filtered intensity information, thereby improving the adaptability of Chan-Vese model for complex object image segmentation．In addition, rectangular Dirac function was used to replace regularized Dirac function in the energy function of Chan-Vese model, and the calculation of level set evolution equation could be limited to the zero level set so as to avoid the influence of the image background disturbance on the segmentation result. In this paper, the video processing with 50 frames of images shows that the time consumption of this method is only 15.25 s, the average value of Dice similarity coefficient is 0.852 929, and the average value of Jaccard index is 0.744 57. In summary, the object detection method proposed in this paper can accurately extract the object contour and has a high real-time performance. [ABSTRACT FROM AUTHOR]

White Agaricus bisporus is an excellent source of the B vitamins, riboflavin, niacin, and pantothenic acid and also a good source of the dietary mineral phosphorus. It is one of the most commonly and widely consumed mushrooms in the world. The production of white Agaricus bisporus has been industrialized in China. However, during the last production chain, it needs a lot of labors to sort and grade the white Agaricus bisporus. The manual sorting and grading mode has many disadvantages such as larger error, low productivity, non-uniform standard, and so on. With the development of machine vision technology, it has been successfully used for automatic inspection and sorting, especially in agricultural industry due to its nondestructive characteristic. An automatic sorting and grading system based on machine vision was designed in this paper. Firstly, an automatic sorting and grading hardware system was designed. It included conveying mechanism, image acquiring system, control module and actuator. The conveying mechanism consists of the fixed support, conveyor, roller, driving unit, tension unit, cleaner and guide plate. The image acquiring system consists of a line scan camera, lens, light source and its controller. The control module consists of photoelectric sensor controlling the camera, electromagnetic valve and relay starter controlling the actuator. The actuator consists of the air compressor, duplex pieces, muffler, air cylinder, joints, gas channel, guide rod, slider, connector, flap, baffle, and so on. Secondly, it put forward an image algorithm based on the watershed method, Canny operator, OR operation and closed operation to determine the diameter of white Agaricus bisporus. The first watershed algorithm combining the global threshold segmentation method and maximum entropy threshold segmentation method is used to remove the shadow of image. The second watershed algorithm based on Canny operator, OR operator and closed operation is used to remove the disturbance of petiole. The minimum enclosing rectangle method is used to get the diameter of white button mushroom. Thirdly, a precise control strategy based on the conveyor speed, distance between trigger and flap piece, trigger time and algorithm processing time was designed. Finally, the software based on OpenCV 2.4.10 and Visual Studio 2010 was developed in this paper to acquire, analyze and process the image and output the control instruction to control the valve by USB-4761 module. It also has the visual parameter configuration function for camera, control module and grading standard. When the whole system starts at the first time, the light source of image acquiring system is adjusted by the light controller to make the image clear and stable. The air compressor is powered on and pressurized to the rated pressure of 0.3 MPa. The motor of conveyor is started and the speed of the conveyor belt is adjusted according to the actual production requirement. The white Agaricus bisporus goes into the guide bar with the conveyor belt. When the white Agaricus bisporus goes into the region of image acquiring, it triggers the industrial camera to scan. The image data are transmitted to the industrial computer by image capture card. The software analyzes the image on line. The analyzed result is sent to the related solenoid valve through the digital control module. When the white Agaricus bisporus arrives near the container of related grade, it is sorted into the container by the related flap piece driven by the solenoid valve. In order to validate the applicability and reliability, the test of grading performance and effect was carried out with the prototype of sorting and grading system at 12.7 m/min conveying speed and 1900 Hz line frequency. The results showed that the average maximum grading speed was 102.41 pieces/min, the accuracy of grading was 97.42%, the damage rate was 0.05% and the undetected rate was 0.96%. The grading speed improved by 38. 86%, the accuracy improved by 6.84% and the damage rate reduced by 0.13% compared to the manual grading. The system can stably and continuously operate. For long time grading, the advantage of intelligent system is more obvious due to the fatigue of labor. The whole system realizes the on-line automatically sorting and grading for fresh white Agaricus bisporus with the minimum destruction. [ABSTRACT FROM AUTHOR]

Abstract: This paper reports the latest developments for the photogrammetric open‐source tool called GRAPHOS (inteGRAted PHOtogrammetric Suite). GRAPHOS includes some recent innovations in the image‐based 3D reconstruction pipeline, from automatic feature detection/description and network orientation to dense image matching and quality control. GRAPHOS also has a strong educational component beyond its automated processing functions, reinforced with tutorials and didactic explanations about algorithms and performance. The paper highlights recent developments carried out at different levels: graphical user interface (GUI), didactic simulators for image processing, photogrammetric processing with weight parameters, dataset creation and system evaluation. [ABSTRACT FROM AUTHOR]

This paper presents low-cost compressors for video display systems, which compress video data to reduce the size of the frame memory in a display panel in a television or a smartphone. The hardware solution exploits the discrete wavelet transform (DWT) and 1-D set partitioning in hierarchical trees (SPIHT) to support the raster-scan processing order in a display system and meet the fixed target compression ratio (CR). However, the compression efficiency and hardware cost often increase in proportion to the block size and thus, designing a compressor with low cost but high fidelity is challenging. This paper proposes a generic partitioned SPIHT algorithm, which achieves a low-cost design for mobile devices by allowing various sizes of partitioned sub-blocks. By taking advantage of DWT decomposition properties, the proposed algorithm partitions a coding block into sub-blocks, each of which can be processed independently. The independent coding operations among multiple sub-blocks allow the size of the hardware buffer to be decreased, by storing the temporary results only for an individual sub-block. However, the proposed sub-block coding slightly decreases the compression efficiency when each sub-block is compressed with the same target CR. To reduce the drop-off in the compression efficiency, the boundary-pixel handling in DWT is exploited and the proposed partitioned SPIHT method adjusts the target CR for each sub-block depending on the potential quality loss. When compared with the previous 1-D design, experimental results show that the hardware gate count and internal memory are reduced by 59.34% and 75%, respectively. Furthermore, the proposed boundary handling and bit-allocation schemes mitigate the PSNR degradation due to the sub-block coding by 1.11 dB. [ABSTRACT FROM PUBLISHER]

Dictionary learning has played an important role in the success of sparse representation. Although several dictionary learning approaches have been developed for image classification, discriminative dictionary pair learning, i.e., jointly learning a synthesis dictionary and an analysis dictionary, is still in its infant stage. In this paper, we proposed a novel model of Fisher discrimination dictionary pair learning (FDDPL), in which Fisher discrimination information is embedded into analysis representation, analysis dictionary, and synthesis dictionary representation. With the proposed Fisher-like discrimination term, discrimination of both synthesis dictionary representation and analysis dictionary representation is introduced into the dictionary pair learning model. An iterative algorithm to efficiently solve the proposed FDDPL and a FDDPL based classifier are also presented in this paper. The experiments on face recognition, scene categorization, gender classification, and action recognition clearly show the advantage of the proposed FDDPL. [ABSTRACT FROM AUTHOR]

The term “iris” refers to the highly textured annular portion of the human eye that is externally visible. An iris recognition system exploits the richness of these textural patterns to distinguish individuals. Iris recognition systems are being used in a number of human recognition applications such as access control, national ID schemes, border control, etc. To capture the rich textural information of the iris pattern regardless of the eye color, traditional iris recognition systems utilize near-infrared (NIR) sensors to acquire images of the iris. This, however, restricts the iris image acquisition distance to close quarters (less than 1 m). Over the last several years, there have been numerous attempts to design and implement iris recognition systems that operate at longer standoff distances ranging from 1 m to 60 m. Such long range iris acquisition and recognition systems can provide high user convenience and improved throughput. This paper reviews the state-of-the-art design and implementation of iris-recognition-at-a-distance (IAAD) systems. In this regard, the design of such a system from both the image acquisition (hardware) and image processing (algorithms) perspectives are presented. The major contributions of this paper include: (1) discussing the significance and applications of IAAD systems in the context of human recognition, (2) providing a review of existing IAAD systems, (3) presenting a complete solution to the design problem of an IAAD system, from both hardware and algorithmic perspectives, (4) discussing the use of additional ocular information, along with iris, for improving IAAD accuracy, and (5) discussing the current research challenges and providing recommendations for future research in IAAD. [ABSTRACT FROM AUTHOR]

In developing nations, many expanding cities are facing challenges that result from the overwhelming numbers of people and vehicles. Collecting real-time, reliable and precise traffic flow information is crucial for urban traffic management. The main purpose of this paper is to develop an adaptive model that can assess the real-time vehicle counts on urban roads using computer vision technologies. This paper proposes an automatic real-time background update algorithm for vehicle detection and an adaptive pattern for vehicle counting based on the virtual loop and detection line methods. In addition, a new robust detection method is introduced to monitor the real-time traffic congestion state of road section. A prototype system has been developed and installed on an urban road for testing. The results show that the system is robust, with a real-time counting accuracy exceeding 99% in most field scenarios. [ABSTRACT FROM AUTHOR]

Accurate crop-load estimation is very important for efficient management of nutrients and harvest operations. Current machine vision techniques for crop-load estimation have achieved only limited success mostly due to partial occlusion, shape irregularity, varying illumination and multiple sizes. Detecting immature green fruit is a more challenging task for similar color of fruit and background. The key starting point of this paper for detecting immature citrus fruit was the observation that the light distribution on citrus fruit follows a general pattern in which the light intensity decreases with the distance from a local maximum due to specular reflection. Immature citrus fruit detection was achieved by detecting this pattern with concentric circles or parts of circles. This pattern was proposed with the maximally stable extremal region (MSER) method and validated by hierarchical contour analysis (HCA), and was the first proposed in this paper. The images were captured by a color camera under low natural light conditions with a flashlight, and the green component of the color images was used for further analysis. After smoothing the whole image by Weiner filter, the regions of interest (ROIs) in the image were extracted by the method of MSER. The ROIs detected by MSER were those whose support was nearly the same over a range of thresholds, so the regions on citrus fruit were detected by MSER for the pattern that the light intensity decreases stably and gently with the distance from a local maximum. However, many regions on leaves and background were also detected as ROIs and should be excluded in the next step. A novel algorithmic technique was proposed to remove these regions on background, and this method was named as the HCA. Firstly, shape analysis was used for each ROI and only those ROIs were considered as valid if the shape was nearly circular. Secondly, multiple levels of contours around each valid ROI were extracted and fitted with the circular Hough transform (CHT). Lastly, multiple fitted circles would be merged into one if their most parts were overlapped together, this step was called circle merging and the merged circles were considered as the last detected citrus fruits. The algorithm was tested on a testing dataset with 20 images and achieved the recall rate of 81.2% and the precision rate of 83.5%. The harmonic average of them was 82.3%. The result showed that 24.8 fruits were correctly detected, 5.7 fruits were not detected and 4.9 ROIs were wrong detected as fruit in each image on average. The processing time of the proposed method was 3.70 s totally on each image, on average, in which 0.57 s was used for MSER detection and 3.13 s was used for HCA. The result showed that the proposed method can detect green citrus fruit in a very difficult and challenging scene with so many fruits in one image and extensive partial occlusion. The good performance of partial occlusion tolerance of the proposed method in this paper is mainly due to that the proposed HCA doesn't use the shape of outer contour of fruit, but uses multiple concentric contours which come from the pattern of light intensity distribution on fruit surface. The research framework in this paper can give a novel thought on other green fruit detection besides citrus fruit. [ABSTRACT FROM AUTHOR]

Harris algorithm is a classical corner detection algorithm. It can extract corners of image quickly and has a certain degree of anti-noise ability, but it has corner location error to some extent. It needs to artificially set 2 threshold parameters, and it can not easily eliminate false corners such as edge points, so it has somewhat lower accuracy of corner detection. For above-mentioned reasons, a modified Harris corner detection algorithm based on auto-correlation matrix of image pixel was proposed in this paper, and the purpose was not only to solve the problem of the variability and randomness of setting thresholds for corner response function (CRF) and non-maximum suppression in Harris algorithm, but also to improve the accuracy of corner location. In our paper, the most important innovation is embodied in 2 aspects: One is avoiding to set 2 thresholds of traditional Harris corner detection algorithm artificially, the other is locating corner more accurately by modified non-maximum suppression method. Firstly, original image was filtered by directional filtering and Gaussian low-pass filtering, and feature corner image was constructed by calculating determinant of every pixel's auto-correlation matrix. Potential corners of image could be heightened effectively, which had more significant intensity than other surrounding pixels, and could be recognized easily in feature corner image. Secondly, in order to improve intelligent level of the modified algorithm, we selected adaptive OTSU algorithm to determine segmentation threshold. The segmentation threshold of feature corner image could be calculated by OTSU algorithm, and the pre-selected regions were obtained. So the search range of corner detection was significantly decreased. On the basis, an optimized non-maximum suppression method was adopted in our research, which could divide each pre-selected region into several 3×3 square subranges, and correct corners were extracted from potential corners of each square subrange, false corners were eliminated effectively. Finally, in order to validate the efficiency and reliability of the modified algorithm, 5 groups of comparison experiments were performed in our research. Five images, including generic image format (jpg, bmp), and multi-band remote sensing image format (GF-2 data), were selected to test performance of the modified algorithm and Harris algorithm, which contained the total of detection corners, the number of correct corners, the number of false corners, the number of omissive corners, and the detection rate of correct corners. According 5 groups of comparison experiments, the accuracy of corner detection in different types of images is improved, for crop vegetation remote sensing image, the accuracy of corner detection can reach 27.06 percentage points. We can draw a conclusion that the improved algorithm can not only calculate the optimal threshold automatically, but also locate the corners more accurately. Therefore, our modified algorithm can greatly improve the precision of corner detection. The proposed algorithm is more accurate and efficient than traditional algorithm, its adaptive characteristic makes it easy to be integrated in an image processing system or image registration module, and it has higher feasibility and application value. Experiments also show that there is some insufficiency to be improved in our research, for example, some corners in picture of cubes could not be detected correctly with either our modified algorithm or Harris algorithm. In our future research, we propose to partition an image into several sub-image blocks, and independently determine each sub-image block's segmentation threshold by OTSU algorithm, so that the corners not prominent in full image can be significantly strengthened in sub-image blocks, and can be detected correctly. The research could provide reference for agricultural remote sensing image data detection. [ABSTRACT FROM AUTHOR]

The breeding environment plays an important role in healthy growth and development of the pigs, and it is also a guarantee of the excellent traits. The growth of pig is the integrated result of their own traits and external environmental factors. For different environments, pigs will show different behaviors. It is found that the appropriate growth environment can be reflected by their degree of dispersion in the pens. When the environment is suitable, the pig group will lie down together. When the temperature is low, they will huddle together; and when the environmental temperature is high, the pigs will scatter around. Image segmentation is the important link to analyze pig behavior status based on computer vision technology, and pig image segmentation is an image representation that provides easy-to-understand and analysis for the behavior feature extraction, parameter measurement, image analysis, pattern recognition of individual pig. The accurate and effective image segmentation algorithm is for pig behavioral intelligent analysis, understanding and environmental intelligent decision. The traditional Graph Cut algorithm uses the energy function to compute all the pixels in the image, it takes a lot of time. It has the low segmentation efficiency and it cannot accurately segment specific pig. Aiming at the limitation of traditional Graph Cut algorithm and the characteristic of image itself, on the basis of the interactive watershed algorithm, in this paper, we proposed an interactive image segmentation method based on improved Graph Cut algorithm aiming at better use of interaction information provided by the user and effectively control of the number of super pixels. To some extent, the algorithm could also avoid the problems of over segmentation or under segmentation. The image was divided into regions based on the interactive watershed algorithm, and the foreground and background were specified. Each region block was regarded as the super pixel, the gray scale of super pixel was used as the vertex. Establish edges between adjacent vertices and the new network map was constructed instead of the traditional weighted graph. In order to reconstruct the energy function to complete the effective segmentation of the foreground and background, the maximum flow, and minimization cut algorithm were used to cut the image. Results of experiments showed that the method can effectively segment pig target, it had high segmentation quality and high efficiency. In this paper, the peak signal to noise ratio (PSNR) and structural similarity (SSIM) were used as objective evaluation standard. The range of the PSNR was [30, 40] and the range of the SSIM was [0.9, 1] in this method, which was the most accurate segmentation algorithm in the shown segmentation algorithm. It indicated that two evaluation criterions were consistent with the subjective evaluation, and the stability and the reliability of this method were improved. The average time consumed in this algorithm was only 33.7% of in the traditional Graph Cut algorithm. It greatly improved the efficiency of segmentation, and it had highly robust for the segmentation of specific target pigs. The results of this paper can provide technical support for subsequent target identification, detection, tracking and monitoring, evaluation of external environments, such as alarms. [ABSTRACT FROM AUTHOR]

Image filtering can change or enhance an image by emphasizing or removing certain features of the image. An image is a system in which some information is known and some information is unknown. Grey system theory is an important method for dealing with this kind of system, and grey correlation analysis and grey prediction modeling are important components of this method. In this paper, a fractional grey prediction model based on a filtering algorithm by combining a grey correlation model and a fractional prediction model is proposed. In this model, first, noise points are identified by comparing the grey correlation and the threshold value of each pixel in the filter window, and then, through the resolution coefficient of the important factor in image processing, a variety of grey correlation methods are compared. Second, the image noise points are used as the original sequence by the filter pane. The grey level of the middle point is predicted by the values of the surrounding pixel points combined with the fractional prediction model, replacing the original noise value to effectively eliminate the noise. Finally, an empirical analysis shows that the PSNR and MSE of the new model are approximately 27 and 140, respectively; these values are better than those of the comparison models and achieve good processing effects. [ABSTRACT FROM AUTHOR]

A design method of aspheric fisheye lens has been proposed in this paper, based on the requirements of automobile surround view system. The study has designed a kind of ultra-wide-angle fisheye lens, which only consists of a spherical glass lens and three aspherical plastic lenses. The maximum diameter of imaging aperture is 15. 3 mm; the working distance behind is 2.158 mm; the total length of system is 11.44 mm; the focal length is 0.97 mm; the viewing angle is 210°, and the modulation transfer function (MTF) curve is 0.35 at 60 lp/mm. Furthermore, a kind of a distortion correction algorithm for fisheye lens has been created, which calculates the position of the ideal image point with the actual image point and the obtained distortion curve and distortion model. The algorithm can correct the distorted image taken by a fisheye lens to an image without distortion, which is suitable for the human eye. The algorithm, which is simple and effective, has been applied to the automobile surround view system. It has been verified to be accurate and reasonable, after the comparison is made between the real image taken by a fisheye lens and the corrected image. [ABSTRACT FROM AUTHOR]

In this paper, a parameterized automatic programming solution whose advantage depends on the automatic feature recognition of digital image is proposed and applied to the development of automatic programming software for composite grinding. This solution can overcome the difficulty of recognizing intersecting feature of complicated rotational part. And a 7-layer CNN classifier is utilized to decide whether the part has the internal features or not, which makes the proposed feature recognition method more intelligent than retrieving the unknown objects blindly. The emphasis of the research is the geometric data extraction algorithm which is the synthesis of border following algorithm, corner detection algorithm and a variety of morphological processing. Under the condition of 12000 × 12000 pixel dimension and 200-dpi resolution of input image, the relative errors between the extracted and actual values of various geometric data are all less than 0.05% for the rotational parts of maximum diameter 500 mm and maximum length of 1500 mm. And all the extracted values of geometric data rounded to integers can fully meet the requirements of NC programming. The automatic programming software based on the proposed solution has excellent portability and practicability, which is independent of any CAD tools or data exchange standards. After the programs generated by the automatic programming software are validated in simulation software NCSIMUL, the software is integrated into HNC-848 CNC system and applied in the prototype of H377 composite grinding center. [ABSTRACT FROM AUTHOR]

Shadow removal is a challenging problem due to its sensitivity to lighting and material conditions. In this paper, we propose a shading-aware shadow processing algorithm, which can automatically detect and remove complex shadows from a single color image. Our framework consists of two key steps. We firstly conduct a shadow-preserving filter upon the image which will effectively remove the image texture while preserving the shadow and shading information. Shadow regions are estimated by establishing a confidence map from the filtered image incorporating depth cue. We then develop a shading-aware optimization framework to remove shadows and recover shading in these regions. The extensive experimental results show that the proposed algorithm produces visually compelling results in a series of challenging images and it can handle complex shadows in both indoor and outdoor scenes. Quantitative and qualitative comparisons with current state-of-the-art methods strongly demonstrate the efficacy of our proposed approach. [ABSTRACT FROM AUTHOR]

The classification methods based on minimum spanning forest (MSF) have yielded impressive results for hyperspectral image. However, previous methods exist several drawbacks, i.e., marker selection methods are easily affected by boundary noise pixels, dissimilarity measure methods between pixels are inaccurate, and also image segmentation process is not robust, since they have not effectively utilized spatial information. To this end, in this paper, novel gradient-based marker selection technique, dissimilarity measures, and adaptive connection weighting method are proposed by making full use of spatial information in hyperspectral image. Concretely, for a given hyperspectral image, a pixel-wise classification is firstly performed, and meanwhile the gradient map is generated by a morphology-based algorithm. Secondly, the most reliable pixels are selected as the markers from the classification map, and then the boundary noise pixels are excluded from the marker map by using the gradient map. Thirdly, several new dissimilarity measures are proposed by incorporating gradient information or probability information of pixels. Furthermore, in the growth procedure of MSF, the connection weighting between pixels is adjusted adaptively to improve the robustness of the MSF algorithm. Finally, when building the final classification map by using the majority voting rule, the labels of the training samples are used to dominate the label prediction. Experimental results are performed on two hyperspectral image sets Indian Pines and University of Pavia with different resolutions and contexts. The proposed approach yields higher classification accuracies compared to previously proposed classification methods, and provides accurate segmentation maps. [ABSTRACT FROM AUTHOR]

The existing pig counting system based on image processing are seriously affected by light conditions, and the counting accuracy is poor when pigs are crowded and obscured.To realize the intelligent pig counting, the paper proposed a counting scheme based on the improved instance segmentation algorithm was proposed in this study. Aiming at the problems of image illumination and target edge blur, Laplace operator was used to preprocess the images. The feature extraction network of MASK R-CNN network was improved by using Resnet-152 as the Mask R-CNN feature extraction network, and the original Feature Pyramid Network(FPN) was followed by a bottom-up enhancement path, which directly fused the low-level edge features with the high-level features to improve the recognition ability of the target edge bluring. The non maximum suppression process and loss function were optimized and improved to improve the segmentation accuracy. The experiments were carried out at three different real pig farms to verify the counting accuracy, respectively in Hebei province, Jilin province and Inner Mongolia. The size of the cage in Hebei pig farm was 5.5 m × 1.8 m, with an average of 12 pigs in a single pen, with a feeding density of 1.21 pigs/m²; the size of the cage in Jilin pig farm was 5.5 m × 3.9 m, with an average of 22 pigs in a single pen, and the average rearing density was 1.03 pigs/m²; the size of the cage in Inner Mongolia pig farm was 11.4 m × 5.28 m, with an average of 80 pigs in a single pen, the average feeding density was 1.32 pigs/m². The RGB camera is was installed on the top of the pen and acquired the image in daytime. 2 400 images were collected in total, and 2000 images were selected after image preprocessing, and 1 250 images of three pig farms were selected as the original data set according to the ratio of 2:2:1. The training set and verification set were enhanced to 1 500 and 150 images and 250 images for the test set, The experimental results showed that 98 images could realize exact counting and 2 images missed 1 pig in the Jilin pig farm, the accuracy of pig counting was 98%. In Hebei pig farm, 99 images could realize the exact counting and the accuracy of pig counting was 99%. For Inner Mongolia pig farms with high feeding density, the accuracy of pig counting was 86%, among the 50 test images, 7 images missed detection, 4 images missed 1 target, 3 images missed 2 targets, The results can provide the application of the artificial intelligent in agriculture field. [ABSTRACT FROM AUTHOR]

Modified Gram–Schmidt (MGS) algorithm is one of the most-known forms of QR decomposition (QRD) algorithms. It has been used in many signal and image processing applications to solve least square problem and linear equations or to invert matrices. However, QRD is well-thought-out as a computationally expensive technique, and its sequential implementation fails to meet the requirements of many real-time applications. In this paper, we suggest a new parallel version of MGS algorithm that uses VLIW (Very Long Instruction Word) resources in an efficient way to get more performance. The presented parallel MGS is based on compact VLIW kernels that have been designed for each algorithm step taking into account architectural and algorithmic constraints. Based on instruction scheduling and software pipelining techniques, the proposed kernels exploit efficiently data, instruction and loop levels parallelism. Additionally, cache memory properties were used efficiently to enhance parallel memory access and to avoid cache misses. The robustness, accuracy and rapidity of the introduced parallel MGS implementation on VLIW enhance significantly the performance of systems under severe rea-time and low power constraints. Experimental results show great improvements over the optimized vendor QRD implementation and the state of art. [ABSTRACT FROM AUTHOR]

Modern high-efficiency engines utilize direct injection for charge preparation at extremely high pressures. At these conditions, the scales of atomization become challenging to measure, as primary breakup occurs on the micrometer and nanosecond scales. As such, fuel sprays at these conditions have proven difficult to study via direct imaging. While high-speed cameras now exist that can shutter at tens to hundreds of nanoseconds, and long-range microscopes can be coupled to these cameras to provide high-resolution images, the resolving power of these systems is typically limited by pixel size and field of view (FOV). The large pixel sizes make the realization of the diffraction-limited optical resolution quite challenging. On the other hand, limited data throughput under high repetition rate operation limits the FOV due to reduced sensor area. Therefore, a novel measurement technique is critical to study fuel spray formation at engine-relevant conditions. In this work, we demonstrate a new high-resolution imaging technique, spectral microscopy, which aims to realize diffraction-limited imaging at effective framerates sufficient for capturing primary breakup in engine-relevant sprays. A spectral microscopy system utilizing a consumer-grade DSLR allows for significantly wider FOV with improved resolving power compared to high-speed cameras. Temporal shuttering is accomplished via separate and independently triggered back illumination sources, with wavelengths selected to overlap with the detection bands of the camera sensor's RGB filter array. The RGB detection channels act as filters to capture independently timed red, green, and blue light pulses, enabling the capture of a three consecutive images at effective framerates exceeding 20 × 106 fps. To optimize system performance, a backlit illumination system is designed to maximize light throughput, a multilens setup is created, and an image-processing algorithm is demonstrated that formulates a three-frame image from the camera sensor. The system capabilities are then demonstrated by imaging engine relevant diesel sprays. The spectral microscopy system detailed in this paper allows for micron-scale feature recognition at framerates exceeding 20 × 106 fps, thus expanding the capability for experimental research on primary breakup in fuel sprays for modern direct-injection engines. [ABSTRACT FROM AUTHOR]

Pulse-coupled neural network (PCNN) is a powerful unsupervised learning model with many parameters to be determined empirically. In particular, the weight matrix is invariable in the iterative process, which is inconsistent with the actual biological system. Based on the existing research foundation of biology and neural network, we propose a spike-synchronization-dependent plasticity (SSDP) rule. In this paper, the mathematical model and algorithm of SSDP are presented. Furthermore, a novel memristor-based circuit model of SSDP is designed. Finally, experimental results demonstrate that SSDP has greatly improved the image processing capabilities of PCNN. [ABSTRACT FROM AUTHOR]

Even though numerous segmentation methods exist, the requirement of prior knowledge or parameter tuning makes them restricted to limited image domains. Without predefining solution models, genetic programming (GP) is able to solve complex problems by evolving computer programs automatically. In this paper, three new GP-based methods are designed to evolve segmentation algorithms automatically from images and primitive image processing operators (e.g., filters and histogram equalization). Specifically, a strongly typed representation, the cooperative coevolution technique and a two-stage evolution are introduced in GP, respectively, to form three new methods that can evolve solutions to conduct image preprocessing, segmentation and postprocessing automatically. The new methods are termed as StronglyGP, CoevoGP and TwostageGP, and standard GP-based algorithm (StandardGP) is employed as a reference method. The proposed methods are tested on two complicated datasets (i.e., Weizmann and Pascal datasets), which contain high variations in both objects and backgrounds. The results show that StronglyGP and StandardGP can evolve effective segmentors for the given complex segmentation tasks, while CoevoGP and TwostageGP perform worse than StronglyGP and StandardGP, which may be caused by the overfitting problem in deriving postprocessing solutions. In addition, compared with StandardGP, StronglyGP achieves better segmentation performance with smaller solution sizes. Moreover, compared with four widely used segmentation methods, StronglyGP and StandardGP can produce satisfactory results consistently on both Weizmann and Pascal datasets. [ABSTRACT FROM AUTHOR]

The paper describes an algorithm developed to enhance the analysis (manual or automatic) of radiographic images acquired for samples of complex geometry (such as those enabled by additive manufacturing techniques), where the imprint of the sample's geometric complexity in the radiograph is likely to undermine the ability to identify defect indications. The underlying premise is that, assuming the sample geometry is known (at least the CAD specification), a simulation of the experimental radiograph can be used to essentially subtract out the geometric complexity from an experimental image, revealing the deviations from the expected inspection output. The approach is especially relevant when the uniqueness of the sample (for example due to personal customisation) limits the availability of comparable experimental data. However, in practice, this technique requires the simulation to be accurately calibrated to the experimental configuration, necessitating the use of a numerical optimisation to fit the simulation parameters. As a by-product, the parameters of an imperfectly specified experimental set-up are recovered. The algorithm architecture described can operate on multiple input radiographs simultaneously, and is readily adaptable to other image-based inspection modalities. Results for several test inputs are presented, starting with synthetic test cases and ending with a set of three experimental radiographs. The results are convincing, as a difference image enables a substantial reduction in image bit-depth, making deviations of interest more apparent and demonstrating the value of the approach. [ABSTRACT FROM AUTHOR]