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• A novel water transparency calculation algorithm is introduced, employing deep neural network and image processing technology. This algorithm obviates the need for a water gauge, allowing the calculation of water transparency solely through the analysisof Secchi disk videos. Notably, it demonstrates heightened detection efficiency and success rates. • An enhanced Siamese tracker, termed SiamDCFF (Siamese tracker based on dual correlation feature fusion), is proposed for Secchi disk recognition. Distinguishing features of SiamDCFF include a dual correlation module and a feature fusion module. On the Secchi disk dataset, SiamDCFF achieves the highest success rate, outperforming other methods detailed in this paper. • The simplified 3D-ResNet [41] is leveraged to assess the state of the Secchi disk, transforming the critical position determination problem into an optimization challenge. The objective function involves maximizing the number of visible and invisible Secchi disk images on both sides of the critical position. This method facilitates adaptive critical position determination. • A novel Gated Recurrent Unit based on auto-regression (ARGRU) is proposed, taking into account the motion characteristics of the Secchi disk. ARGRU achieves a higher success detection rate for water transparency compared to the water gauge recognition method. Notably, ARGRU outperforms RNN [42], GRU, and LSTM [43] on the dataset presented in this paper. Water transparency is traditionally assessed through visual inspection using a lowered Secchi disk (SD) into the water, with the disappearance depth of the SD recorded as the measure of transparency. However, manual and visually-dependent frequency measurements of the SD render this process labor-intensive and time-consuming. This paper presents a comprehensive machine-vision-based algorithm designed for the automatic computation of water transparency using Secchi disk videos. The algorithm leverages multiple deep neural networks, including an enhanced Siamese tracking model, a 3D-ResNet, and an improved GRU network. Trained models directly identify the SD in the video, calculating water transparency by processing pixel information. Experimental results demonstrate the algorithm's efficacy in estimating water transparency across diverse natural environments, achieving commendable accuracy (MAE = 3.6 cm MSE = 21.5 cm RMSE = 4.6 cm), rapid processing speed (average 6.87 s), and robust stability. In comparison with the former water-gauge-based algorithm, our proposed algorithm exhibits heightened efficiency and a superior detection success rate. [ABSTRACT FROM AUTHOR]

Multi-exposure image fusion (MEF) is an important area in computer vision and has attracted increasing interests in recent years. Apart from conventional algorithms, deep learning techniques have also been applied to MEF. However, although many efforts have been made on developing MEF algorithms, the lack of benchmarking studies makes it difficult to perform fair and comprehensive performance comparison among MEF algorithms, thus hindering the development of this field significantly. In this paper, we fill this gap by proposing a benchmark of multi-exposure image fusion (MEFB), which consists of a test set of 100 image pairs, a code library of 21 algorithms, 20 evaluation metrics, 2100 fused images, and a software toolkit. To the best of our knowledge, this is the first benchmarking study in the field of MEF. This paper also gives a literature review on MEF methods with a focus on deep learning-based algorithms. Extensive experiments have been conducted using MEFB for comprehensive performance evaluation and for identifying effective algorithms. We expect that MEFB will serve as an effective platform for researchers to compare the performance of MEF algorithms. • A literature review on multi-exposure image fusion is presented. • A multi-exposure image fusion benchmark is proposed. • The benchmark integrates a test set, algorithms, evaluation metrics, and a toolkit. • Comprehensive performance comparison is conducted and presented. [ABSTRACT FROM AUTHOR]

The analysis of two-dimensional images enables the detection of structures, objects and their further classification. In the Internet of Things, images are data obtained from cameras or projections of signals. In this paper, we propose a hybridization of the classic RANSAC approach with a selected heuristic algorithm like Grey Wolf Optimizer. This solution allows for increasing the ability to adjust images by analyzing the detected key points of the image. Such hybridization is interesting in terms of the possibility of obtaining stitching accuracy. Moreover, we have introduced the automation of parameter selection to increase the accuracy and speed of the real-time algorithm. For this purpose, an analysis of the results of the algorithm for combining video frames into one large image allowed for a significant reduction in the amount of data processing time. The proposed algorithm was tested on various heuristic algorithms, where their differences and impact on the operation of the method were indicated and discussed. Moreover, the proposition was tested in practical application by automatically creating an image based on video frames recorded by the drone. • Hybridization of RANSAC with a heuristic solution for image stitching purposes. • A method of automating the selection of parameters in the algorithm. • Framework for minimizing the number of calculations in smart devices. • Practical application of the proposed method in IoT solution using a drone. [ABSTRACT FROM AUTHOR]

• The two change detection algorithms proposed in this paper use KFCM gradient clustering algorithm. • KFCM algorithm is a classic clustering algorithm in unsupervised clustering, which can effectively cluster most of the data. • We will study how to use the special features of the image to complete the clustering of the difference maps. In recent years, computer vision, especially deep learning, has been widely used in various fields. Through the deep learning aerial image detection gradient clustering algorithm automatic recognition, it can solve the limitations of manual shooting by humans, can shoot from a high altitude to a panoramic view of a specific area, and provide a more comprehensive solution. The traditional forest resource management and management work is mainly carried out by forestry personnel to carry out a large number of investigations and investigations on the forest. This method not only consumes a lot of manpower and material resources, but also does not have real-time nature. It is difficult to deal with all kinds of forest management. Problems, causing unnecessary losses. In this regard, this paper proposes an aerial image change detection algorithm based on H-KFCM, and designs related experiments to verify and demonstrate the performance of the algorithm. In this paper, we conduct a parallel study based on deep learning on the gradient clustering algorithm of deep learning in aerial image processing. By using CUDA (Compute Unified Device Architecture) to perform large-scale parallel processing of aerial data. Can greatly shorten the time to obtain results, improve the efficiency of relevant personnel. Experiment analysis. It can be seen from the results that the deep learning parallelization program implemented in this paper has a faster calculation speed and uses less time in high-resolution images, and has a good acceleration ratio compared to the CPU. [ABSTRACT FROM AUTHOR]

In order to satisfy the requirement of precise pin-point landing mission in Mars exploration, this paper proposes an innovative visual navigation algorithm based on feature lines extraction and matching to estimate the attitude and position of the lander during descent phase. Currently, common navigation landmarks are feature points and craters. But, the number of feature points extracted from landing image is so large that the image process is complex and time-consuming, and they do not usually have absolute coordinate information to absolute navigation. Meanwhile, crater is matched difficultly. Hence, ridges, fissures and other surface textures similar to lines are most common visual features of Mars and can be used as navigation landmarks for visual absolute and relative navigation. Firstly, that feature lines matching based on the EDLine algorithm remains invariant to scaling, rotation, and illumination, etc. provides the basis for visual navigation by using feature line. Secondly, feature lines selected as navigation landmarks can be used to absolute navigation and relative navigation respectively, which can implement integrated navigation. In this paper, a novel integrated visual navigation has been designed to finish the precise pin-point landing and overcome the accumulated errors of relative navigation and the limited application range of absolute navigation. Experimental simulation results show that the integrated navigation algorithm solves the limitation of single navigation mode and improves the effectiveness and stability of the algorithm. • A navigation algorithm using feature line for pinpoint landing is proposed. • The lander's absolute and relative pose are estimated during descent phase. • Integrated navigation overcomes the limitation of single navigation. [ABSTRACT FROM AUTHOR]

The method based on machine vision image processing is used to detect the surface defects of Si 3 N 4 bearing roller. Owing to the variety of defects, small area and low contrast, it is easy to miss or error detection. In this paper, an adaptive update template defect enhancement algorithm based on Gaussian model is proposed. First, a large number of surface images of Si 3 N 4 bearing roller are collected to obtain the non-defect background statistical feature, and the background characteristic curve is fitted by Gaussian model. Further, the initial background template is gained according to the Gaussian curve. Then, combined with the gray distribute of defect images and initial background template, unique adaptive update template can be established. Finally, subtraction operation and nonlinear enhancement are used to improve the comparison of defect information and background. Through inverse sorting, adaptive threshold segmentation and Canny operation, the precise positioning of defects is realized. The enhancement algorithm can effectively enhance the contrast and eliminate the influence of noise. The average detection time is 0.84s, and the detection accuracy is 96.2%. [ABSTRACT FROM AUTHOR]

• Optimized incremental bit-quads count in component trees. • The intuition of incremental bit-quad count in component trees correctness. • A Fast method to compute area, perimeter and Euler Number in grayscale images. • A Fast algorithm to compute continuous perimeter approximation in grayscale images. • Software applications were rewritten using attributes computed by bit-quads. Component tree is a full image representation which encodes all connected components from upper (resp. lower) level sets of a given image through the inclusion relation. Information from this representation can be used in many image processing and computational vision applications, e.g. connected filtering, image segmentation, feature extraction, among others. In general, each node of a component tree represents a connected component of a level set and stores attributes which describes features of this connected component. This paper presents a review of a previously published method to compute attributes such as area, perimeter, and number of Euler by incrementally counting patterns while traversing nodes of a component tree. This method foundation is further detailed in this paper by presenting a novel theoretical background and algorithm correctness intuition. We also present a novel approach for this algorithm showing improvements for run-time execution and precision analysis. [ABSTRACT FROM AUTHOR]

Highlights • In this paper, a camera calibration method is proposed to acquire the checkerboard image by adding polarizer before the camera lens. • By introducing the Stokes variable feedback to obtain the optimal polarization angle of the checkerboard image to remove or suppress the high optical region influence in the image acquisition, the optimal polarization angle chessboard image can be used to improve the coordinate precision of corner point. • The accuracy of sub-pixel coordinate extraction makes the result of camera calibration more accurate. • Compared with the previous methods to improve the accuracy of corner extraction considers the idea of improving angle extraction accuracy from the algorithm level. • This paper focuses on the image processing of corner image acquisition, and has the relative innovation in the way and method. Abstract The accurate estimation of camera internal and external parameters is one of the keys to high-precision camera calibration. At present, the mainstream Zhang's calibration method requires the use of a checkerboard to achieve high-precision calibration result. Therefore, the precise extraction of the pixel coordinates at the corner points on the checkerboard is one of the key technologies. The traditional methods are to improve the corner extraction algorithm, but ignore the imaging process of the calibration images. It is difficult to detect and eliminate the high-light regions in the process of calibrating image, and the existence of high-light regions will cause low accuracy of camera calibration. Therefore, a camera calibration method based on optimal polarization information is proposed in this paper. The method first uses a Stokes variable linear feedback through a CCD camera equipped with a polarizer to obtain a checkerboard calibration image at an optimal polarization angle in different spatial positions. Then use the sub-pixel level detection algorithm and Gaussian fitting method to locate the corner points in the image and to solve the sub-pixel coordinates of the corner points precisely. At last, the two-dimensional pixel coordinates of the corner points in each checkerboard image are extracted to perform camera calibration. The experimental results show that the coordinates of corner points of calibration plate image based on the optimal polarization angle have about 1 pixel difference compared with the coordinates of the calibration plate corner points under natural illumination, and the slopes between the parallel lines of the fitted are closer, meet the high precision calibration requirement of camera. [ABSTRACT FROM AUTHOR]

Highlights • Latent sharp image and the blur PSF are estimated jointly via a multi-functional optimization problem addressed by a first-order primal-dual solver. • This approach generates a multi-resolution pyramid of the input blurred image. • We introduce a PSF initiation method that benefits from the salient structure obtained via an l 0 gradient smoothing of blurred image. • A first-order primal-dual solution to the l 0 gradient smoothing problem is introduced in this paper. Abstract In this paper, a blind deblurring algorithm that is well converged towards the latent sharp image and the blur PSF is proposed. The proposed algorithm employs our recently proposed ringing suppression regularizer in a solution to the blind deblurring problem. Latent sharp image and the blur PSF are estimated jointly via a multi-functional optimization problem addressed by a first-order primal-dual solver. This approach generates a multi-resolution pyramid of the input blurred image and propagates the estimation results of the lower resolution levels to the higher resolution ones. By considering the PSF initiation as an important step in the blind deblurring process, we introduce a PSF initiation method that benefits from the salient structure obtained via an l 0 -gradient smoothing of blurred image. A first-order primal-dual solution to the l 0 -gradient smoothing problem is introduced in this paper. Experimental results show that the proposed deblurring algorithm significantly enhances the results favorably against other state-of- the-art image deblurring methods. [ABSTRACT FROM AUTHOR]

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]

• 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]

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]

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 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]

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]

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]

The task of segmenting small infrared targets, which have few pixels and weak features, has been a difficult problem in the field of small target image processing. Small targets not only exist in general images, but also widely exist in drone cameras, communication base station cameras, rescue cameras, and vehicle cameras. Research on small target segmentation algorithm is very important for analyzing and utilizing these images, and it also has important application value in security, traffic, and rescue. Traditional small target segmentation algorithms are able to segment objects with simple target contour edges and large differences in signal strength. The traditional algorithm often has a high false detection rate and missed detection rate when facing several targets with weak signal strength. It does not perform well in complex scenes. In this paper, we introduce an infrared small target segmentation scheme facing multiple types and numbers of targets. This paper also produces an infrared UAV and pedestrian dataset for validation. Experiments show that the optimized algorithm has a better detection effect on targets in complex scenes. [ABSTRACT FROM AUTHOR]

• Automated selection of image segmentation algorithms. • Computational cost reduction in image segmentation tasks. • Meta-learning outperformed any single algorithm explored over the whole dataset. • Machine Learning and Gradient-based image segmentation algorithms were studied. • Experiments were conducted over a widely known image dataset with 2100 examples. Image segmentation is a key issue in image processing. New image segmentation algorithms have been proposed in the last years. However, there is no optimal algorithm for every image processing task. The selection of the most suitable algorithm usually occurs by testing every possible algorithm or using knowledge from previous problems. These processes can have a high computational cost. Meta-learning has been successfully used in the machine learning research community for the recommendation of the most suitable machine learning algorithm for a new dataset. We believe that meta-learning can also be useful to select the most suitable image segmentation algorithm. This hypothesis is investigated in this paper. For such, we perform experiments with eight segmentation algorithms from two approaches using a segmentation benchmark of 300 images and 2100 augmented images. The experimental results showed that meta-learning can recommend the most suitable segmentation algorithm with more than 80% of accuracy for one group of algorithms and with 69% for the other group, overcoming the baselines used regarding recommendation and segmentation performance. [ABSTRACT FROM AUTHOR]

In this paper, we propose two algorithms of the Nonnegative Tucker decomposition that contain manifold regularization terms used for capturing the underlying geometric structures, which are applied in clustering of image databases. In general, based on Tucker model in the form of least square estimation, that is to find interpretable basis factors, and achieve the compressed of original data. It usually supposes the structures of the processing databases are global linearity. However, in practical applications, such as the processing arguments of image, video sequence and Electroencephalography data, they are usually within richness structures of nonlinear. Therefore, it is necessary to uncover the underlying structures for achieving an effective application. Hence, in this article, we incorporate Laplacian Eigenmaps i.e., LE and Locally Linear Embedding i.e., LLE as the manifold regularization terms into the least square form of Tucker model. In addition, for the physical meaning of reasonable interpretation consistent with original data, thus, the basis factors and core tensor are constrained to be nonnegative. Finally, the numerical results have demonstrated both two our proposed algorithms can achieve an effective performance among state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Underwater images acquired in scattering environments are generally of poor quality because of the attenuation and backscattering of light when it passes through water with scattering particles. Polarimetric de-scattering methods can be used to significantly enhance the imaging quality in such a so-called turbid water. However, due to the complexity of polarimetric de-scattering algorithms, it is hard to achieve real-time de-scattering output from a polarimetric camera. In this paper, in order to efficiently increase the computational efficiency, the polarimetric de-scattering algorithm is optimized and a multi-threading framework is developed that enables the algorithm to run in real-time on ordinary laptops for the same polarimetric camera. We demonstrate the imaging performance by using the underwater polarimetric de-scattering system we proposed. We analyze the algorithm under different scattering conditions and discuss its optimal parameters. We find that there is a significant increase in the number of SIFT (i.e., scale invariant feature transform) feature points extracted from the processed image compared to the original image, showing that our system has potential applications in pattern recognition, computer vision, etc. [ABSTRACT FROM AUTHOR]

• Adaptive grayscale image correction based on RAW prior knowledge is provided. • Adaptive power correction and compensation is provided. • Adaptive brightness and power correction based on HIS prior knowledge is provided. • An adaptive enhancement algorithm based on prior knowledge and HIS space. • The algorithm can achieve the best performance on various multi-modal data sets. • The best performance in the task of high grayscale infrared image target detection. Gray-scale images are widely used in many fields, including satellite remote sensing, autonomous driving, medical CT, and X-ray detection, but gray-scale images have many shortcomings, such as high bit, low contrast, and high dynamic ranges. The visual analysis of these images is complicated and limited by people's psychology and physiology, and pseudo-color technology is an effective way to solve this problem. To address the weak adaptive capability, poor robustness, and insufficient generalization of existing pseudo-color enhancement methods, an adaptive enhancement algorithm for high grayscale images based on prior knowledge and HIS space, called APKHHIS, was proposed. Firstly, an adaptive gray-scale image correction mapping algorithm based on original prior knowledge was designed, and the quantization of images with different bit depths is realized. Secondly, to ensure the effectiveness of the enhanced data, an adaptive power compensation method was designed to compensate for the brightness of the gray-scale corrected images. Finally, an adaptive brightness and power correction algorithm was innovatively designed for the first time, which can adaptively adjust the color saturation and spatial brightness of the image more reasonably. To verify the effectiveness of the algorithm proposed in this paper, adaptive enhancement experiments were carried out on multi-modal data sets. The subjective and objective experimental results show that the APKHHIS algorithm can achieve the best performance on various data sets, and has strong robustness and universality, which provides an effective solution for the adaptive enhancement of multi-modal low-quality images. [ABSTRACT FROM AUTHOR]

Digital image processing (DIP) has great application values in many fields, especially in remote sensing image processing, which represents the acquisition, enhancement, analysis, encoding, transmission, and storage of remote sensing images. With the development of chip technology and parallel computing technology, various digital image processing technologies have been successfully applied to satellite applications to help researchers exploit reliable information from remote-sensing images. However, the huge amount of images generated by ultra-high resolution optical remote sensing satellites put great pressure on existing transmission, storage, and processing technologies. Therefore, this paper proposes a spatio-temporal compression pipeline for remote sensing images based on lossy compression methods with ultra-high compression ratios to reduce the overhead required for the transmission and storage of remote sensing images while maintaining the quality of the compressed images. The experimental results show that the proposed method outperforms the classical image compression such as JPEG-2000. [Display omitted] [ABSTRACT FROM AUTHOR]

The Circle Hough Transform (CHT) is one of the popular circle detection algorithm in image processing and machine vision application, favored for its tolerance to noise. Nevertheless, it involves huge computation and excessive memory requirements. Because of its drawbacks, various modifications have been suggested to increase its performances. In this paper, we present a new modification of the CHT method developed for an automatic biometric iris recognition system. The novelty of this method resides on the use of the incremental property in order to reduce the resources requirement and the parallel property for decreasing the computation time. The incremental property is obtained using the approximation of the used trigonometric functions, while the parallel property is achieved by calculating, at the same time, several point coordinates of the circle. The software implementation and the validation have been done in C++ and MATLAB on real images. The errors analysis and the performances of the proposed method against the basic CHT method are presented in this paper. [ABSTRACT FROM AUTHOR]

This paper presents a new algorithmic framework for some basic problems on binary images. Algorithms for binary images such as one of extracting a connected component containing a query pixel and that of connected components labeling play basic roles in image processing. Those algorithms usually use linear work space for efficient implementation. In this paper we propose algorithms for several basic problems on binary images which are efficient in time and space, using space-efficient algorithms for grid graphs. More exactly, some of them run in O ( n log n ) time using O ( 1 ) work space and the others run in O ( n ) or O ( n log n ) time using O ( n ) work space for a binary image of n pixels stored in a read-only array. [ABSTRACT FROM AUTHOR]

• A novel non-rigid registration method for medical images is proposed by introducing the image field into Demons algorithm. • Introduce the orientation field for registration and make use of the directional information. • There is no extraction of feature points and manual intervention in the proposed method. • Experiments on BrainWeb and FIRE databases show the effectiveness of the proposed registration method for medical images. In medical imaging application, non-rigid registration is an important step that requires both accuracy and efficiency to align the deformed query image with the reference image. For non-rigid registration, Demons is generally used as an effective algorithm. However, since the computational model of Demons uses only the gradient information, the directional information of the image has not been fully utilized. In this paper, we proposed a novel non-rigid registration method for processing medical images by introducing the image field to the Demons algorithm. As direction information is very important for spatial transformation in registration and the image fields contain the direction of the image, by combing image fields with the traditional model based registration algorithm, we introduce the orientation field directly to the model for estimating the distortion, and thus make a better use of the direction information of images and simplify the deformation model. Different performance measures are used to evaluate the qualitative measures of the proposed scheme. Experiments on brain images and fundus images show the improvement of the proposed algorithm compared to the other existing state-of-the-art methods. [ABSTRACT FROM AUTHOR]

In recent decades, methods to find invariant points in digital images, called fiducial points, have gained great attention, mainly due to the demands of several applications in computer vision and image processing, such as the geometric matching of global structures, objects or specific regions. Among the most well known approaches are algorithms like SIFT, HOG, SURF, and their variations as A-SIFT, PCA-SIFT, surrounded by many others. Though the number of researches demonstrating the efficiency of such methods is undoubtedly enormous, the vast majority compares their performances only on pairs of images with little changes in view perspectives, objects or specific regions of the scenes. Thereby, the study of this type of technique under large viewpoint changes, called here LVC, has received little attention from the researchers. On the other hand, with the aim of filtering points of interest, most techniques have used the traditional extensive statistics. However, methods for image processing based on a new type of statistics, called non-extensive statistics, have shown to be efficient in several applications. In this paper, we present a new method, called q -SIFT, based on the non-extensive Tsallis statistics, to find fiducial points in a sequence of frames of videos under large viewpoints changes. We experimentally show the efficiency of the proposed method in video databases and propose new measurement metrics for this type of algorithm. • We present a method for Fiducial Point Description based on non-extensive physical systems. • We test the proposed method under what we call severe conditions, little tested in the literature. • We compare the proposed method with the main methods found in the literature, showing that the proposed method surpasses all others. • We present a new methodology to measure the performance of the studied algorithms. • We show that the database used represents non-extensive physical systems. [ABSTRACT FROM AUTHOR]

Highlights • Propose indirect diffusion model for extraction of deeply concave edges and sharp corners. • Model is defined as two PDEs, one driving level set motion and one smoothing indirectly level set. • Level set function can be initialized to zero function, so allowing for flexible initialization. • It is easy to set a termination criterion for the proposed algorithm. Abstract In this paper, we put forward an idea of indirect diffusion and further develope an indirect diffusion-based level set model for image segmentation. This model is based on the dynamic process of diffusion that is posed indirectly on level set function by way of auxiliary function, coupled with a transition region-based force that exhibits the desired sign-changing property. It is formulated as a coupled system of two evolution equations, in which the first equation drives the motion of zero level set toward the object edges and makes it possible to set a termination criterion on the algorithm, while the second equation (indirect diffusion) smoothens the auxiliary function and keeps the auxiliary function as close to the level set function as possible. The derived model can effectively be solved purely by the simplest explicit finite difference. Experimental results show that the proposed model not only has the strong capability of noise immunity, but it also can much better conduce to extraction of deeply concave edges and preservation of sharp corners, compared with the direct diffusion-based counterpart. [ABSTRACT FROM AUTHOR]

Highlights • We present a sparse tensor denoising model for magnetic resonance images. • The convergence of proposed scheme is proved. • The optional solution is proved. • Numerical algorithm is developed. • Comparison with experimental results is covered. Abstract In recent years, image denoising based on sparse tensors has been one promising technique for denoising magnetic resonance images or video processing. This paper aims at developing a new sparse tensor model based on reweighted regularization of factor matrices for magnetic resonance images denoising. An improved Split-Bregman scheme is proposed which is simple in implementation and efficient in computation. Additionally, the convergence of proposed scheme is proved. Experiments show that the proposed algorithm is efficient, and the denoising results are better than the state-of-the-art image denoising methods. The average computational time of our method is slightly longer than the others under the same iteration, except LPGPCA and model in Ruru and Zhixun (2018) [22]. [ABSTRACT FROM AUTHOR]

Highlights • Speckle noise reduction algorithm for parallel phase-shift digital holography (PPSDH) is proposed. • It enables reducing speckle noise in on-line. • Principle of speckle noise reduction is demonstrated. • It is based on characteristic of PPSDH and novel interpolation is adapted. • Experimental results show that speckle noise is suppressed. Abstract Digital holography is the best tool for measuring object information data. Parallel phase-shift digital holography (PPSDH) is particularly suitable for measuring moving objects. This method generates images of objects, but the image quality is diminished because they contain abundant speckle noise. In this paper, we propose an interpolation-based algorithm to reduce the levels of speckle noise in images generated by PPSDH, thus enhancing their quality. [ABSTRACT FROM AUTHOR]

Highlights • The proposed framework is an effective and flexible tool for structure-texture decomposition smoothing. • Exhibits good response to the image which has low-contrast details. • The computational time is competitive to the others state-of-the-art algorithms. Graphical abstract A comparison of the proposed algorithm's results using different guided edge-preserving filters. Abstract Structure-texture decomposition smoothing has been extensively studied due to its wide range of applications in computational photography and image processing. In this paper, we propose a new structure-texture decomposition algorithm which is based on two fundamental ideas: (1) guidance image and (2) iterative smoothing. The guidance image is generated by mitigating high-frequency oscillatory components in the original image. The result is then incorporated in a new generic iterative framework which makes use of well-known guided edge-reserving filters such as bilateral filter (BF), guided filter (GF), domain transform filter (DTF), and the extended Bayesian model averaging filter (BMA) called guided Bayesian model averaging filter (GBMA) to achieve texture smoothing. We have presented a detailed study of the proposed algorithm including: guidance image generation, an evaluation of the guided edge-preserving filters which are incorporated in the proposed iterative framework, the number of iterations for the proposed iterative structure, and the selection of guided edge-preserving filter. We demonstrate that the proposed method is a flexible and effective tool for a wide range of image editing applications including: image abstraction, color pencil drawing, content-aware image resizing, and texture editing. In particular, the proposed approach has the best performance in structure-texture decomposition for an image with low-contrast features. [ABSTRACT FROM AUTHOR]

Highlights • The images are fused simply based on a large and small-scale focus maps. • A better focus map can be estimated using the notion of random walks. • The above algorithm can be boosted by considering an equivalent objective function. • The fusion is computationally efficient yet robust to mis-registration and noise. Abstract In conventional fusion methods for multi-focus images, the focus map generated by a focus measure would usually be sensitive to mis-registration and noise, or produce badly-aligned boundaries. While many state-of-the-art algorithms use more complex strategies or procedures to address this problem, in this paper we propose to estimate a focus map directly from the two-scale imperfect observations (focus maps) obtained using a small and large-scale focus measures. This would contribute to a more robust fusion by taking advantage of the complementary properties of the two-scale observed focus maps, i.e., robustness to mis-registration (and noise) and the better aligned boundaries. The estimation is firstly modeled in a probabilistic perspective using random walks-based algorithm, in which we try to solve the probabilities that each pixel of the focus map is associated with the observed ones. Then we found that this method is equivalent to solving an alternate objective function, enabling a great boost both in computational efficiency and estimation result. Experimental results demonstrate that the proposed method is robust yet efficient compared with state-of-the-art fusion methods. [ABSTRACT FROM AUTHOR]

Abstract Due to absence of quantitative and evolutionary view on flame-front's wrinkling morphonology, descriptions upon instability onset of premixed expanding spherical flames in previous literatures are implicit and inconsistent. This paper utilized shadowgraphy and high-magnification lens in the optical configuration, which clearly manifested cracks in wrinkled flame images and facilitated precise segmentation of them. A simple algorithm was developed to reconstruct real lengths of cracks from these planar projected images. Furthermore, a meaningful parameter, the averaged cell radius, was deducted from the total crack length. Application of this novel methodology on the near-equidiffusive C 2 H 2 -air flame reveals similar growth speeds of flame cracks under different pressures before the flame front transits into cellular appearance. Before this transition, the averaged cell radius kept fluctuating around a roughly stable level which was intimately dependent on initial disturbances. Once the transition started, the effect of inherent instability dominates over initial disturbances and averaged cell radii of repeated tests gradually converged to a similar value. The above methodology builds up an automatic way to quantitatively characterizing cracks and cells' developing history, which extracts more useful and precise information from image series than merely manual estimation of cell sizes at individual shoots in previous studies. Highlights • Establish a new algorithm to detect wrinkling cracks in spherical flame images. • Two new parameters deduced to indicate length of cracks and size of flame cells. • Quantitative evolving features of crack development and cellular transition. [ABSTRACT FROM AUTHOR]

• The image Hash authentication algorithm for orthogonal moments of fractional order chaotic is proposed. • The image information is authenticated by the Hamming distance. • The robustness and tampering recognition accuracy of the proposed method is tested. In this paper, the image Hash authentication algorithm for orthogonal moments of fractional order chaotic scrambling coupling hyper-complex number Tchebichef is proposed. The improved PDE function and Ring segmentation mechanism are used to process the initial image and output the secondary image to improve the robustness of Hash's scaling and rotation; and by taking full account into the color information of the image, the image R, G and B position is regarded as its imaginary part. In addition, the invariant moment of the hyper-complex number Tchebichef is desgined, and the robust characteristics of the secondary image is fully extracted to greatly improve its ability to recognize the rotation tampering. At the same time, in order to improve the security of the Hash sequence, the fractional order Logistic mapping and chaotic scrambling technique is constructed to encrypt the eigenvector; and the image information is authenticated by the Hamming distance. Finally, the robustness and tampering recognition accuracy of the proposed Hash algorithm are tested. [ABSTRACT FROM AUTHOR]

Highlights • We proposed a weighted heterogeneous distance metric (WHDM). • We presented WHDM and Dempster–Shafer theory based k NN algorithm. • We proposed a multimodal perturbation method (RRSB) for k NN ensemble. • The effectiveness of our algorithms was shown on multiple UCI data sets and a KDD data set. Abstract In this paper, to address the issue that ensembling k -nearest neighbor (k NN) classifiers with resampling approaches cannot generate component classifiers with a large diversity, we consider ensembling k NN through a multimodal perturbation-based method. Since k NN is sensitive to the input attributes, we propose a weighted heterogeneous distance Metric (WHDM). By using a WHDM and evidence theory, a progressive k NN classifier is developed. Based on a progressive k NN, the random subspace method, attribute reduction, and Bagging, a novel algorithm termed RRSB (reduced random subspace-based Bagging) is proposed for construct ensemble classifier, which can increase the diversity of component classifiers without damaging the accuracy of the component classifiers. In detail, RRSB adopts the perturbation on the learning parameter with a weighted heterogeneous distance metric, the perturbation on the input space with random subspace and attribute reduction, the perturbation on the training data with Bagging, and the perturbation on the output target of k neighbors with evidence theory. In the experimental stage, the value of k , the different perturbations on RRSB and the ensemble size are analyzed. In addition, RRSB is compared with other multimodal perturbation-based ensemble algorithms on multiple UCI data sets and a KDD data set. The results from the experiments demonstrate the effectiveness of RRSB for k NN ensembling. [ABSTRACT FROM AUTHOR]

Highlights • The thermal response fringes excited by the line laser are distorted by the specimen topography. • Laser fringes are converted into temperature fringes by thermal response information. • Some algorithms are proposed to greatly improve the measurement accuracy. Abstract Linear laser fast scanning thermography technology has a good application prospect in the nondestructive detection of defects. Meanwhile, due to the modulation of the surface topography, the thermal response fringes excited by the line laser are distorted in the scanning process, whereby the three-dimensional topography of the specimen can be reconstructed. However, the accuracy of this method is currently limited by unfavorable factors, such as background noise of the specimen surface and the extraction error of the deformed temperature fringes. In this paper, by optimizing the initial position difference during line laser scanning and converting the laser fringes with specific position difference into temperature fringes with specific phase difference, the phase shift technique is combined with the temperature fringe method. What's more, some post-processing algorithms were developed to further develop the temperature fringe method and greatly improve the accuracy of the method. By measuring some specimens with different shapes and different colors, the results show that the technology and algorithms developed here can obtain the three-dimensional shape of the measured specimen with high accuracy while having a non-destructive testing function, without interference from the surface color of the specimen. [ABSTRACT FROM AUTHOR]

Highlights • A MRF-based inpainting algorithm for maintaining structure coherence is proposed. • Selecting proper offsets for energy function determines the inpainting results. • Appropriate direction structure feature is helpful to improve the inpainting performance and efficiency. Abstract Image inpainting provides a means for restoration of an image with some damaged portions and has been widely applied to many fields. However, many existing inpainting methods still suffer the difficulties of effectively and efficiently maintaining structure coherence. To tackle the problems, this paper proposes a direction structure distribution analysis strategy for Markov Random Field (MRF) -based image inpainting algorithms. In the strategy, the desired direction structures (DDS) are selected and applied to guide the inpainting process. Given a degraded image, four kinds of direction edge information are extracted by super-wavelet transform, canny and dilation operators. Then local direction edge gradient magnitude is applied for direction structure distribution analysis and DDS are selected according to gradient variance. Afterwards, offsets of similar patches are calculated and only a few dominated offsets are chosen as candidate offsets. Finally, graph-cut optimization technology is utilized to solve energy function. Experimental results show that the proposed method achieves generally better performance than nine state-of-the-art methods in terms of the abilities of maintaining structure coherence and the computational cost on inpainting different kinds of degraded images. [ABSTRACT FROM AUTHOR]

Abstract Most of existing image categorizations assume that the given datasets have a good resolution and quality. However, the assumption is often violated in real applications. In this paper, we study the low-resolution (LR) image categorization. By utilizing labeled high-resolution (HR) images as auxiliary information, we formulate the problem as a heterogeneous domain adaptation problem and propose a Discriminative Joint Distribution Adaptation (DJDA) model to solve it. The DJDA model projects both LR and HR images into an intermediate subspace with a well-designed objective function, where the distance between classes is expected to be enlarged and the distribution divergence to be reduced. As a result, the discriminative knowledge for HR images can be transferred effectively to LR images. Experimental results demonstrate the proposed DJDA method produces significantly superior categorization accuracies against state-of-the-art competitors. [ABSTRACT FROM AUTHOR]

Abstract The purpose of image fusion is to integrate useful information from multiple images and produce a more reliable image. The key problem of multi-focus image fusion is how to determine the focused regions of the source images. As an effective and excellent fusion algorithm, the focused regions in the source images should be preserved as much as possible into the fused image. To accomplish this goal, a novel multi-focus image fusion method based on a focused regions boundary finding and multi-scale transform (MST) is proposed in this paper. The Meanshift algorithm is used to determine the focused regions first. Then, an edge detection method and morphological method are used to find the boundaries of the focused regions in the source images. For the focused boundary regions, the combination of pulse coupled neural network (PCNN) and Gaussian fuzzy method is used to produce the fused boundary region in nonsubsampled contourlet transform (NSCT) domain. Finally, the fused boundary region and the focused region of the source images are fused directly. The experimental results demonstrate that the proposed algorithm can accurately determine the focused regions, and at the same time, a better fused boundary region can be obtained; this algorithm is superior to conventional methods with respect to both objective quality evaluations and visual inspection. [ABSTRACT FROM AUTHOR]

Abstract The electrical capacitance tomography (ECT) technology is a promising tomography technology that can image the distribution information of permittivity in a measurement region. The imaging algorithms play crucial roles in practical ECT image reconstruction problems. Different from previous numerical methods, this study proposes a novel cost function to model the ECT inverse problem, in which the L1-norm is used as the data fidelity to weaken the influence of the outliers contained in the capacitance data, the L1 regularization is introduced to enhance the sparseness of reconstructed objects, and the second order total variation (STV) regularization is used to weaken the staircasing effects caused by the first order total variation (FTV) regularization. The split Bregman iteration (SBI) algorithm that splits a complicated ECT imaging problem into several simpler sub-problems is developed to solve the proposed cost function effectively. The numerical simulation results show that the imaging algorithm proposed in this paper can reconstruct satisfactory results. Highlights • A new cost function is proposed to model the ECT imaging problem. • A new iterative algorithm is developed to effectively solve the cost function. • The feasibility and effectiveness of the algorithm are numerically validated. [ABSTRACT FROM AUTHOR]

Based on compressed sensing theory and TDI CCD driving mode, a collaborative coding method founded on TDI CCD charge transfer and random exposure is proposed in this paper, which breaks through the limitation of pixel size and achieves the improvement of one-dimensional image resolution. By extending this method, an orthogonal dual detector super-resolution imaging system is established, which realizes the improvement of two-dimensional image resolution. Further, the convex optimization algorithm and the proportional fusion algorithm are proposed as the two-dimensional image fusion algorithm. Theoretical modeling and simulation results demonstrate the effectiveness of the proposed imaging method and system, which provide an innovative approach of system implementation for super-resolution imaging based on compressed sensing theory. [ABSTRACT FROM AUTHOR]

In this work we describe a fast and stable algorithm for the computation of the orthogonal moments of an image. Indeed, orthogonal moments are characterized by a high discriminative power, but some of their possible formulations are characterized by a large computational complexity, which limits their real-time application. This paper describes in detail an approach based on recurrence relations, and proposes an optimized Matlab implementation of the corresponding computational procedure, aiming to solve the above limitations and put at the community’s disposal an efficient and easy to use software. In our experiments we evaluate the effectiveness of the recurrence formulation, as well as its performance for the reconstruction task, in comparison to the closed form representation, often used in the literature. The results show a sensible reduction in the computational complexity, together with a greater accuracy in reconstruction. In order to assess and compare the accuracy of the computed moments in texture analysis, we perform classification experiments on six well-known databases of texture images. Again, the recurrence formulation performs better in classification than the closed form representation. More importantly, if computed from the GLCM of the image using the proposed stable procedure, the orthogonal moments outperform in some situations some of the most diffused state-of-the-art descriptors for texture classification. [ABSTRACT FROM AUTHOR]

Abstract Detection of underground objects remains an important task today, particularly when attempting to recover landmines. A Ground Penetrating Radar (GPR) sends short pulses in time domain to detect underground objects by recording the reflected signal. GPR traces acquired at different positions in space are combined together to form B-scan images. The presence of objects produce hyperbolic fluctuations in B-scan images that depend on the shape, dielectric properties, and depth of the sensed object, and also on the properties of the medium. Dielectric properties of some objects, like plastic objects, create very small fluctuations in GPR traces. To analyze the recorded patterns, efficient algorithms need to be developed and improved based on the inherent variations of the overall conditions. For detecting underground objects under noise, this paper compares the results of two algorithms: Histograms of Oriented Gradients (HOG), and n-Row Average Subtraction (nRAS). For tests, five different scenarios from two test configurations have been used. According to the results for all images in the first test configuration, HOG and 3RAS algorithms both increase Object Detection Ratio (ODR) from 88% to 93% while decreasing the False Alarm Rate (FAR) considerably. The accuracy is also tested for different image sizes. And for certain algorithms, lower resolution images result in better accuracy. [ABSTRACT FROM AUTHOR]

This paper presents systematic image processing methods for investigating hydrodynamic characteristics in a rectangular spouted bed (30.2 mm × 101.6 mm). The objective is to provide a non-intrusive technique to determine the bed expansion ratio, the jet height and the fountain height due to their significant influence on the hydrodynamics of the entire bed. A high-speed video camera is used in the experiment with Geldart D particles as the bed material. After the image acquisition, image denoising, thresholding, logical and mask operations are applied correspondingly for the boundary discrimination, based on which an algorithm has been developed to determine the bed height successfully. With the discriminated boundary, morphological operations viz. erosion and dilation have been used to accurately determine the jet height and the fountain height. The feasibility of the technique is proven through profiles of bed expansion ratio, jet height and fountain height under three initial bed heights (0.0762 m, 0.102 m, and 0.127 m) for two different nozzle sizes (0.0096 m and 0.0127 m). Depending on the determination of the hydrodynamic characteristics, different flow regimes can be distinguished comprehensively through profiles of the bed expansion ratio and bed pressure drop in combination with the standard deviation of jet/fountain height. The proposed flow regime discrimination verifies the feasibility and effectiveness of the image processing methods in assessing the solids behavior in the spouted beds. Results about the variation of the jet height and the fountain height under current operating conditions as determined by the new technique are also discussed. This work has been selected by the Editors as a Featured Cover Article for this issue. [ABSTRACT FROM AUTHOR]

In this paper, we present a scalable three dimensional parallel Delaunay image-to-mesh conversion algorithm. A nested master–worker model is used to simultaneously explore process- and thread-level parallelization. The mesh generation includes two stages: coarse and fine meshing. First, a coarse mesh is constructed in parallel by the threads of the master process. Then the coarse mesh is partitioned. Finally, the fine mesh refinement procedure is executed until all the elements in the mesh satisfy the quality and fidelity criteria. The communication and computation are separated during the fine mesh refinement procedure. The master thread of each process that initializes the MPI environment is in charge of the inter-node MPI communication for data (submesh) movement while the worker threads of each process are responsible for the local mesh refinement within the node. We conducted a set of experiments to test the performance of the algorithm on distributed memory clusters and observed that the granularity of coarse level data decomposition, which affects the coarse level concurrency, has a significant influence on the performance of the algorithm. With the proper value of granularity, the algorithm is scalable to 45 distributed memory compute nodes (900 cores). [ABSTRACT FROM AUTHOR]

Abstract Face detection plays an important role in many computer vision applications. In recent years, much research has focused on extending the well-established Adaboost face detector algorithm for multi-view face detection. However, detecting in-plane and out-of-plane rotated faces simultaneously is still a challenging task today. In this paper, a very robust multi-view face detection algorithm, with its core functionality based on frontal face detection, is proposed to simultaneously detect in-plane and out-of-plane rotated faces. Moreover, only the training data in the frontal face is needed and we do not require the training data from different in-plane or out-of-plane rotation angles. In the proposed algorithm, first, techniques such as the skin filter and entropy rate superpixels (ERSs) are applied to obtain face candidates. Then, angle compensation and refinement are applied to improve the accuracy of face detection in the in-plane case. Moreover, the symmetry extension technique, i.e., extending the face candidate with its flipped version to create a face similar to the frontal one, is applied to detect out-of-plane faces without the need of training data. Simulations on the FEI, Pointing'04, BaoFace, Group, Utrecht, and WWW datasets demonstrate the proposed algorithm's effectiveness and superior performance as compared to state-of-the-art face detection methods. Graphical abstract Unlabelled Image Highlights • We detect in-plane and out-of-plane rotated faces by only the frontal face detector. • Training data from different in and out-of-plane rotation angles are not required. • Skin filters and ERSs can preserve the faces from different illumination and races. • Angle compensation computes the rotation angle for each rotated face. • Symmetry extension creates pseudo faces to well detect out-of-plane rotated faces. [ABSTRACT FROM AUTHOR]

Automatic identification and localization of rebar from ground penetrating radar (GPR) data has been a research topic of great interest. This paper presents an automated rebar picking algorithm for GPR data of concrete bridge decks. The algorithm is based on the Limited and Simplified Hyperbolic Summation (LSHS) technique in which the width of migration is limited and a counter is used to check if a hyperbolic signature exist in a sub-region of GPR image. More specifically, after a time-zero correction, each pixel in the raw GPR image with a positive amplitude will be migrated, as it is usually done with the conventional hyperbolic summation (HS) technique. However, for each pixel to be migrated, the width of migration is limited in the horizontal direction and a unit value will be used for migration, instead of the true amplitude values of the pixels. In the obtained image, the pixels containing rebar peaks will normally have the intensity values close to the number of pixels corresponding to the width of migration and, therefore, can be picked. Whereas the method is rather straightforward and simple to develop, its implementation on GPR data of two concrete bridge decks has shown good results. First, the GPR condition maps obtained from the proposed algorithm were in a good agreement with those developed by the manual method of rebar picking. Second, the accuracy of the picking algorithm was 98.09% and 99.21% for the two decks, respectively. [ABSTRACT FROM AUTHOR]

Image segmentation is the most important operation in image processing that changes the representation of an image to be useful in much computer vision applications. During the last years, several image segmentation algorithms have been developed, consequently, a great number of techniques for evaluating segmentation results have been proposed. Unfortunately, the majority of these methods is subjective and cannot be used to judge the performance of different segmentation algorithms. In this paper, we propose a novel evaluation criterion based on the Gini index and the entropy calculation. This new method permits evaluating the conjunction of the regions arrangement in the segmented image, and measuring the pixels homogeneity within each region. In experiments, benchmark images segmented by the multilevel thresholding technique based on particle swarm optimization are used to conclude the strength, the effectiveness and the rapidity of the proposed evaluation criterion. [ABSTRACT FROM AUTHOR]

The example-based super-resolution (SR) methods can be mainly categorized into two classes: the internal SR methods and the external SR methods. The internal SR methods only use samples obtained from a single low resolution (LR) input, while the external SR methods only utilize an external database. The complementary information included in internal and external samples is rarely taken into account. This paper presents a novel extraction and learning method about the complementary information between external samples and internal samples, and then the learned complementary information is used to improve the single image SR performance. Firstly, we construct an initial high resolution (HR) image via sparse coding over the learned dictionary pair with external samples. Secondly, we propose an adaptive sample selection scheme (ASSS) to acquire the mixed samples. Thirdly, we present a novel adaptive mixed samples ridge regression (AMSRR) model to effectively learn the complementary information included in the mixed samples. Finally, we optimize the SR image. Extensive experimental results validate the effectiveness of the proposed algorithm comparing with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]