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Single image super-resolution (SR) aims at generating a plausible and visually pleasing high-resolution (HR) image from a low-resolution (LR) input. In this paper, we propose an effective single image SR algorithm by using collaborative representation and exploiting non-local self-similarity of natural images. In particular, the collaborative-representation-based method is applied to build the so-called self-projection matrices from a training set of HR images. Then the learned self-projection matrices are used to establish the collaborative-representation-based regularization (CRR), which is responsible for introducing the external HR information. Furthermore, to guarantee a reliable estimation of the HR image, the non-local low-rank regularization (NLR) which exploits internal prior of images is also taken into consideration. Since the CRR term and NLR term are complementary, they are assembled together to form a new reconstruction-based framework for SR recovery. Finally, to implement the proposed framework, an iterative algorithm is designed to gradually improve the quality of the SR results. Extensive experimental results indicate that the proposed approach is capable of delivering higher quality of SR results than several state-of-the-art SR methods. [ABSTRACT FROM AUTHOR]

Deciding a rational number of clusters for the problems to be solved and determining the parameters associated with the clustering algorithms are two critical issues in the configuration of data clustering. Usually, a manual trial-and-error manner is used to induce a feasible configuration. This paper presents an automated clustering method, which determines the clustering configuration automatically. The proposed method is based on the techniques of support-vector machines with a local-search strategy. It starts with running one-class support-vector machines (OCSVM) to partition input data into a random number of clusters. When a result is obtained, the “local-search” mechanism launches several rounds of OCSVM each of which works with a new clustering configuration. Each new configuration is from the current configuration with incrementally modifications. The clustering results obtained from the local searches are post-evaluated by specific clustering validity index and the best one is retained. The clustering configuration of the best result is used by OCSVM for the clustering afterwards. Such a clustering process iterates until no better result can be obtained. This paper describes the clustering algorithm and compares three clustering validity indices, i.e. distance-based index, Davies–Bouldin index, and Xie–Beni index, on their effectiveness. The performance of the proposed method is demonstrated on the segmentation of several aerial images. Experimental results show that the proposed approach is feasible and effective for image segmentation. [ABSTRACT FROM AUTHOR]

This paper introduces the wavelet image two-line (Wi2l) coding algorithm for low complexity compression of images. The algorithm recursively encodes an image backwards reading only two lines of a wavelet subband, which are read in blocks of 512 bytes from flash memory. It thus only requires very little memory, i.e., a memory array for two wavelet subband lines, an array to store intermediate tree level data, and an array for writing binary data. A picture of 256×256 pixels would require 1152 bytes of memory. Computation time for the coding is derived analytically and measured on a real system. The times on a low-cost microcontroller for 256×256 grayscale pictures are measured as 0.25–0.6 s for encoding and 0.22–0.77 s for decoding. The algorithm can thus realize a low complexity system for compression of images when combined with a customized scheme for the wavelet transform; low complexity here refers to low memory, minimum write access to flash memory, usage of integer operations only, and low conceptual complexity (ease of implementation). As demonstrated in this paper, a compression performance similar to JPEG 2000 and the more recent Google WebP picture compression is achieved. The compression system uses flash memory (SD or MMC card) and a small camera sensor thus building an image communication system. It is also suitable for mobile devices or satellite communication. The underlying C source code is made publicly available. [ABSTRACT FROM AUTHOR]

Multi-level image thresholding is a common approach to image segmentation where an image is divided into several regions based on its histogram. Otsu's method is the most popular method for this purpose, and is based on seeking for threshold values that maximise the between-class variance. This requires an exhaustive search to find the optimal set of threshold values, making image thresholding a time-consuming process. This is especially the case with increasing numbers of thresholds since, due to the curse of dimensionality, the search space enlarges exponentially with the number of thresholds. Population-based metaheuristic algorithms are efficient and effective problem-independent methods to tackle hard optimisation problems. Over the years, a variety of such algorithms, often based on bio-inspired paradigms, have been proposed. In this paper, we formulate multi-level image thresholding as an optimisation problem and perform an extensive evaluation of 23 population-based metaheuristics, including both state-of-the-art and recently introduced algorithms, for this purpose. We benchmark the algorithms on a set of commonly used images and based on various measures, including objective function value, peak signal-to-noise ratio, feature similarity index, and structural similarity index. In addition, we carry out a stability analysis as well as a statistical analysis to judge if there are significant differences between algorithms. Our experimental results indicate that recently introduced algorithms do not necessarily achieve acceptable performance in multi-level image thresholding, while some established algorithms are demonstrated to work better. [ABSTRACT FROM AUTHOR]

Image processing methods for automated concrete crack detection are often challenged by binary noise. Noise removal methods decrease the false positive pixels of crack detection results, often at the cost of a reduction in true positives. This paper proposes a novel method for binary noise removal and segmentation of noisy concrete crack images. The method applies an area threshold before reducing the pixel groups in the image to a skeleton. Each skeleton is connected to its nearest neighbour before the remaining short skeletons in the image are removed using a length threshold. A morphological reconstruction follows to remove all elements in the original noisy image that do not intersect with the skeleton. Finally, pixel groups in close proximity to the endpoints of the pixel groups in the resulting image are reinstated. Testing was conducted on a dataset of noisy binary crack images; the proposed method (Skele-Marker) obtained recall, precision, intersection over union, and F1 score results of 77%, 91%, 72%, and 84%, respectively. Skele-Marker was compared to other methods found in literature and was found to outperform other methods in terms of precision, intersection over union and F1 score. The proposed method is used to make crack detection results more reliable, supporting the ever-growing demand for automated inspections of concrete structures. • We use image processing techniques to remove noise in binary concrete crack images. • False positives in binary concrete crack images can be reduced using our method. • We compare our concrete crack noise removal method to others found in literature. • Our method performed better than other concrete crack segmentation methods. [ABSTRACT FROM AUTHOR]

A new Infrared (IR) image analysis system will be deployed for real-time divertor protection during the upcoming Operational Phase (OP) 2.1 in the Wendelstein 7-X (W7-X) stellarator. Its primary objective is to prevent thermal overloads from permanently damaging Plasma-Facing Components (PFCs), resulting in machine downtimes and repair costs. The real-time constraint for this system is 110 ms, which is the maximum allowed delay entailing the acquisition, calibration, processing, and interlock, while all processing steps have to complete within 10 ms allowing for processing longer than the acquisition time of 10 ms at 100 Hz. This paper describes the implementation, real-time processing performance and detection effectiveness of Thermal Overload Detection (TOD). The implemented and evaluated TOD system fulfils real-time constraints. It reduces the total system delay to 50 ms and provides high detection sensitivity of 0.97 for archived discharge sequences from the OP1.2 campaign. The attained acceleration is significant, i.e., a 95% and 99% decrease in runtime for the sequential Central Processing Unit (CPU) and parallel Graphics Processing Unit (GPU) implementations, respectively, compared to the initial Python prototype. For the first time, the presented results confirm the feasibility of protecting W7-X in real-time comprising fundamentals for further advanced protection and control. • The first real-time protection of the plasma-facing components in Wendelstein 7-X. • The primary OP2.1 system to provide safe operation with the water-cooled components. • Accelerated full-frame processing of high-resolution infrared images with GPU and CPU. • Highly sensitive overload prediction algorithm for effective machine protection. • Evaluation and analysis of the algorithm on the archived discharges from OP1.2. [ABSTRACT FROM AUTHOR]

This paper introduces a novel generalized multifrequency fusion method for defect detection in Eddy Current NDE. The proposed technique provides an ability to highlight small defect signals in presence of noise or larger signals from structural irregularities. This paper contains a complete mathematical formulation of the method and its applications to simulated and experimental data arising from two typical ECT problems: detection of the small cracks in the tube wall near tube support region of steam generator tubes, and detection of small cracks in the bottom layer of two-layer aluminum panels riveted with steel fasteners. [ABSTRACT FROM AUTHOR]

A stencil is a thin sheet of material, such as paper, plastic, or metal, with certain patterns cut from it. Applying a pigment through the cut-out holes produces a design on an underlying surface. Using multiple overlapping stencil layers, artists can create intricate, yet reproducible imagery on a variety of surfaces. Traditionally, artists have to design not only the final appearance, but also each individual stencil layer. A stencil layer needs to be connected, geometrically simple, and physically stable. Taking all these constraints into account during the design process is difficult and unintuitive even for skilled artists. In this paper, we propose a system which separates the artistic design stage from the complex and tedious task of stencil creation. For a given user design, our algorithm automatically generates a set of stencil layers satisfying all required properties. The task is formulated as a constrained energy optimization problem and solved efficiently. Experiments, including a user study, are carried out to examine the complete algorithm as well as each individual step. [ABSTRACT FROM AUTHOR]

In this paper, we propose a multiple-parameter fractional Fourier transform with its transform order being a real vector, based on which a high-security image encryption scheme is also given. This novel fractional Fourier transform has removed the restriction on the dimension of transform order and highly enhances the security of image encryption scheme proposed in this paper without increasing the computational complexity and hardware cost. The numerical results verify the efficacy and security of this image encryption method. The vector power multi-parameter fractional Fourier transform is a generalized form of the classical fractional Fourier transform with all the previous fractional Fourier transform as its special cases and has theoretical significance in information processing and information security. [ABSTRACT FROM AUTHOR]

Abstract: This paper proposes food image classification methods exploiting both local appearance and global structural information of food objects. The contribution of the paper is threefold. First, non-redundant local binary pattern (NRLBP) is used to describe the local appearance information of food objects. Second, the structural information of food objects is represented by the spatial relationship between interest points and encoded using a shape context descriptor formed from those interest points. Third, we propose two methods of integrating appearance and structural information for the description and classification of food images. We evaluated the proposed methods on two datasets. Experimental results verified that the combination of local appearance and structural features can improve classification performance. [Copyright &y& Elsevier]

Abstract: Similarity measure plays an important role in many image processing fields. This paper introduces the medium mathematic systems, and establishes a novel image medium similarity measure between two pixels and that of between two image sets based on the measure of medium truth degree. Moreover, an image edge detection algorithm, an image matching algorithm and an image medium fidelity measure method governed by the medium similarity measure are discussed in this paper. Experimental results show that the proposed similarity measure is effective. [Copyright &y& Elsevier]

Discrete fracture networks (DFN) are widely used to build refined geological models. However, validating whether a refined model can match to reality is a crucial problem, concerning whether the model can be used for analysis. The current validation methods include numerical validation and graphical validation. However, the graphical validation, aiming at estimating the similarity between a simulated trace map and the real trace map by visual observation, is subjective. In this paper, an algorithm for the graphical validation of DFN is set up. Four main indicators, including total gray, gray grade curve, characteristic direction and gray density distribution curve, are presented to assess the similarity between two trace maps. A modified Radon transform and loop cosine similarity are presented based on Radon transform and cosine similarity respectively. Besides, how to use Bézier curve to reduce the edge effect is described. Finally, a case study shows that the new algorithm can effectively distinguish which simulated trace map is more similar to the real trace map. [ABSTRACT FROM AUTHOR]

Eye fixation models, which try to quantitatively predict human eye attended areas in visual fields, have received increasing interest in recent years. In this paper, a novel framework is proposed for the detection of eye fixations. First, a multi-channel detection module, which extracts information of color contrast, salient object proposals and center bias from input image, is conducted to introduce various useful information into the subsequent fixations detection. In salient object detection channel, we employ the multi-instance learning (MIL) algorithm to determine which object proposal can attract attention, which avoids the fuzzyness of positive sample selection. Second, an adaptive weighted fusion method achieved by deep learning framework is proposed to fuse the multi-level information (i.e., contrast, objective, center bias) together for the detection of fixations, so that the integration of information between each level becomes more scientific. Finally, the detection result is optimized by embedding semantic information. Experimental results show that the algorithm has achieved competitive results in MIT1003, MIT300 and Toronto120 dataset. [ABSTRACT FROM AUTHOR]

In recent years face hallucination or super-resolution (SR) is getting much attention due to its wide applicability in real world scenarios. The existing SR methods and models perform well for noise free or small camera/atmospheric noisy faces. However, when suffering from mixed Impulse-Gaussian (MIG) noise, face hallucination becomes a challenging task. To address this problem, a novel error shrunk nearest neighbors representation (ESNNR) based face hallucination algorithm is proposed in this paper. Here, local content prior is incorporated to identify the high variance content (HVC) in the input images. The proposed algorithm suppresses the identified HVC in the input face to minimize the squared error. Moreover, the similarity matching between the input and training images is improved to achieve the locality and sparsity in the presence of MIG noise. Simulation results performed on public FEI, CAS-PEAL, CMU+MIT face databases, and locally captured surveillance video frames show that the proposed algorithm is computationally efficient, suitable for practical applications and give better performance than the existing face SR methods. [ABSTRACT FROM AUTHOR]

Most of the available pan-sharpening methods use vector or matrix based detail injection to enhance the resolution of MultiSpectral (MS) image, which may result in unavoidable spectral and spatial distortions. In this paper we explore the intrinsic tensor structure and local sparsity of MS images, to develop a novel Sparse Tensor Neighbor Embedding (STNE) based pan-sharpening method that reduces the distortions in the fused images. First, MS images are formulated as some spectral tensors, and each tensor and its nearest neighbor tensors are assumed to lie in a low-dimensional manifold. Then the tensor is sparsely coded under its neighbor tensors, and a joint sparse coding assumption is cast on bands to develop an N-way Block Pursuit algorithm for solving sparse tensor coefficients. Finally high resolution MS tensor can be obtained by weighting Panchromatic image with the sparse tensor coefficients. Tensors are higher order generalizations of vectors and matrices, and taking advantage of high-order structure of multi-dimensional data can help us understand them. The proposed method first combines a sparse tensor with neighbor embedding, to construct a new high-dimensional sparse tensor embedding for efficient pan-sharpening. Because tensor formulation can exploit the structural correlations in high-dimensional MS data, the proposed method can well preserve spectral correlation among different bands simultaneously and capture the underlying high-order statistical properties of MS image. Some experiments are performed on several real QuickBird and GeoEye datasets, and experimental results show that STNE is superior to its counterparts in reducing spectral and spatial distortions. [ABSTRACT FROM AUTHOR]

This paper proposes a hierarchical multi-label matcher for patch-based face recognition. In signature generation, a face image is iteratively divided into multi-level patches. Two different types of patch divisions and signatures are introduced for 2D facial image and texture-lifted image, respectively. The matcher training consists of three steps. First, local classifiers are built to learn the local matching of each patch. Second, the hierarchical relationships defined between local patches are used to learn the global matching of each patch. Three ways are introduced to learn the global matching: majority voting, ℓ 1 -regularized weighting, and decision rule. Last, the global matchings of different levels are combined as the final matching. Experimental results on different face recognition tasks demonstrate the effectiveness of the proposed matcher at the cost of gallery generalization. Compared with the UR2D system, the proposed matcher improves the Rank-1 accuracy significantly by 3% and 0.18% on the UHDB31 dataset and IJB-A dataset, respectively. [ABSTRACT FROM AUTHOR]

Image reconstruction from discrete electrical responses pose a number of computational and mathematical challenges. Application of smoothness constrained regularized inversion from limited measurements may fail to detect resistivity anomalies and sharp interfaces separated by hydro stratigraphic units. Under favourable conditions, compressed sensing (CS) can be thought of an alternative to reconstruct the image features by finding sparse solutions to highly underdetermined linear systems. This paper deals with the development of a CS assisted, 3-D resistivity inversion algorithm for use with hydrogeologists and groundwater scientists. CS based l 1 -regularized least square algorithm was applied to solve the resistivity inversion problem. Sparseness in the model update vector is introduced through block oriented discrete cosine transformation, with recovery of the signal achieved through convex optimization. The equivalent quadratic program was solved using primal-dual interior point method. Applicability of the proposed algorithm was demonstrated using synthetic and field examples drawn from hydrogeology. The proposed algorithm has outperformed the conventional (smoothness constrained) least square method in recovering the model parameters with much fewer data, yet preserving the sharp resistivity fronts separated by geologic layers. Resistivity anomalies represented by discrete homogeneous blocks embedded in contrasting geologic layers were better imaged using the proposed algorithm. In comparison to conventional algorithm, CS has resulted in an efficient (an increase in R 2 from 0.62 to 0.78; a decrease in RMSE from 125.14 Ω-m to 72.46 Ω-m), reliable, and fast converging (run time decreased by about 25%) solution. [ABSTRACT FROM AUTHOR]

To obtain size distribution of nanoparticles, scanning electron microscope (SEM) and transmission electron microscopy (TEM) have been widely adopted, but manual measurement of statistical size distributions from the SEM or TEM images is time-consuming and labor-intensive. Therefore, automatic detection methods are desirable. This paper proposes an automatic image processing algorithm which is mainly based on local adaptive Canny edge detection and modified circular Hough transform. The proposed algorithm can utilize the local thresholds to detect particles from the images with different degrees of complexity. Compared with the results produced by applying global thresholds, our algorithm performs much better. The robustness and reliability of this method have been verified by comparing its results with manual measurement, and an excellent agreement has been found. The proposed method can accurately recognize the particles with high efficiency. [ABSTRACT FROM AUTHOR]

We propose a novel fully convolutional network architecture for shapes, denoted by Shape Fully Convolutional Networks (SFCN) . 3D shapes are represented as graph structures in the SFCN architecture, based on novel graph convolution and pooling operations , which are similar to convolution and pooling operations used on images. Meanwhile, to build our SFCN architecture in the original image segmentation fully convolutional network (FCN) architecture, we also design and implement a generating operation with bridging function. This ensures that the convolution and pooling operation we have designed can be successfully applied in the original FCN architecture. In this paper, we also present a new shape segmentation approach based on SFCN. Furthermore, we allow more general and challenging input, such as mixed datasets of different categories of shapes which can prove the ability of our generalisation. In our approach, SFCNs are trained triangles-to-triangles by using three low-level geometric features as input. Finally, the feature voting-based multi-label graph cuts is adopted to optimise the segmentation results obtained by SFCN prediction. The experiment results show that our method can effectively learn and predict mixed shape datasets of either similar or different characteristics, and achieve excellent segmentation results. [ABSTRACT FROM AUTHOR]

This paper presents an effective colour enhancement framework for statistical and logarithmic image processing (LIP)-based enhancement algorithms. The proposed approach utilizes the fusion of partial, multiple computed luminance channels with colour image channel statistics obtained from the input colour image for adaptive colour enhancement. The proposed scheme does not modify the image intensity channel, avoiding colour fading typically observed in colour images processed with conventional algorithms. The colour enhancement scheme compensates for the weaknesses of greyscale-based contrast enhancement and illumination normalization algorithms by focusing on preserving/restoring or enhancing colour. The proposed system avoids the conversion to complex, non-linear colour spaces such as HSI and HSV while producing similar results without manual adjustment of parameters. Additionally, an adaptive scheme for detection of images with unbalanced colour and uneven illumination is combined with the proposed system. Results show that the proposed scheme augments colour results of greyscale-based contrast enhancement algorithms and is relatively less complex compared to most algorithms in the literature. [ABSTRACT FROM AUTHOR]

During the last two decades, MRI has been increasingly used for providing valuable quantitative information about spinal cord morphometry, such as quantification of the spinal cord atrophy in various diseases. However, despite the significant improvement of MR sequences adapted to the spinal cord, automatic image processing tools for spinal cord MRI data are not yet as developed as for the brain. There is nonetheless great interest in fully automatic and fast processing methods to be able to propose quantitative analysis pipelines on large datasets without user bias. The first step of most of these analysis pipelines is to detect the spinal cord, which is challenging to achieve automatically across the broad range of MRI contrasts, field of view, resolutions and pathologies. In this paper, a fully automated, robust and fast method for detecting the spinal cord centerline on MRI volumes is introduced. The algorithm uses a global optimization scheme that attempts to strike a balance between a probabilistic localization map of the spinal cord center point and the overall spatial consistency of the spinal cord centerline (i.e. the rostro-caudal continuity of the spinal cord). Additionally, a new post-processing feature, which aims to automatically split brain and spine regions is introduced, to be able to detect a consistent spinal cord centerline, independently from the field of view. We present data on the validation of the proposed algorithm, known as “OptiC”, from a large dataset involving 20 centers, 4 contrasts (T 2 -weighted n = 287, T 1 -weighted n = 120, T 2 ∗ -weighted n = 307, diffusion-weighted n = 90), 501 subjects including 173 patients with a variety of neurologic diseases. Validation involved the gold-standard centerline coverage, the mean square error between the true and predicted centerlines and the ability to accurately separate brain and spine regions. Overall, OptiC was able to cover 98.77% of the gold-standard centerline, with a mean square error of 1.02 mm. OptiC achieved superior results compared to a state-of-the-art spinal cord localization technique based on the Hough transform, especially on pathological cases with an averaged mean square error of 1.08 mm vs. 13.16 mm (Wilcoxon signed-rank test p-value < .01). Images containing brain regions were identified with a 99% precision, on which brain and spine regions were separated with a distance error of 9.37 mm compared to ground-truth. Validation results on a challenging dataset suggest that OptiC could reliably be used for subsequent quantitative analyses tasks, opening the door to more robust analysis on pathological cases. [ABSTRACT FROM AUTHOR]

Nonnegative matrix factorization (NMF) is a very popular unsupervised or semi-supervised learning method useful in various applications including data clustering, image processing, and semantic analysis of documents. This study focuses on Symmetric NMF (SNMF), which is a special case of NMF and can be useful in network analysis. Although there exist several algorithms for SNMF in literature, their convergence and initialization have not been well addressed. In this paper, we first discuss the convergence and initialization of existing algorithms for SNMF. We then propose a Converged Algorithm for SNMF (called CASNMF) which minimizes the Euclidean distance between a symmetrical matrix and its approximation of SNMF. Based on the optimization principle and the local auxiliary function method, we prove that our presented CASNMF does not only converge to a stationary point, but also could be applied to the wider range of SNMF problems. In addition, CASNMF does not require that the initial values are nonzero. To verify our theoretical results, experiments on three data sets are conducted by comparing our proposed CASNMF with other existing methods. [ABSTRACT FROM AUTHOR]

Local feature detectors are widely used in many photogrammetry and remote sensing applications. The quantity and distribution of the local features play a critical role in the quality of the image matching process, particularly for multi-sensor high resolution remote sensing image registration. However, conventional local feature detectors cannot extract desirable matched features either in terms of the number of correct matches or the spatial and scale distribution in multi-sensor remote sensing images. To address this problem, this paper proposes a novel method for uniform and robust local feature extraction for remote sensing images, which is based on a novel competency criterion and scale and location distribution constraints. The proposed method, called uniform competency (UC) local feature extraction, can be easily applied to any local feature detector for various kinds of applications. The proposed competency criterion is based on a weighted ranking process using three quality measures, including robustness, spatial saliency and scale parameters, which is performed in a multi-layer gridding schema. For evaluation, five state-of-the-art local feature detector approaches, namely, scale-invariant feature transform (SIFT), speeded up robust features (SURF), scale-invariant feature operator (SFOP), maximally stable extremal region (MSER) and hessian-affine, are used. The proposed UC-based feature extraction algorithms were successfully applied to match various synthetic and real satellite image pairs, and the results demonstrate its capability to increase matching performance and to improve the spatial distribution. The code to carry out the UC feature extraction is available from https://www.researchgate.net/publication/317956777_UC-Feature_Extraction . [ABSTRACT FROM AUTHOR]

The use of an image detector to receive grating images and measure angle displacement via image processing is a relatively new technique, which yields higher resolution and better precision than the traditional moiré fringe method. To improve the robustness of image-type angle measurement, this paper proposes a robust sub-pixel subdivision algorithm based on the least square method. Firstly, by analyzing the characteristic of grating image, a new subdivision algorithm is established based on the least square method. Secondly, the simulations of robustness are completed to prove the performance in theoretically. Lastly, the proposed algorithm is used in a typical image-type angle sensor to test the performance in real case. By test, the proposed method is shown to be more accurate and with better robust than the traditional algorithm (centroid algorithm). In a typical image-type angle sensor, it successfully achieves a resolution of 0.62″ (21-bit), 2 13 -fold subdivision resolution, and precision of 12.85″. The results presented here may provide a theoretical and technological foundation for further research on small-size, high-resolution photographic rotary encoders. [ABSTRACT FROM AUTHOR]

Hyperspectral unmixing is an important technique for remote sensing image exploitation. It aims to decompose a mixed pixel into a collection of spectrally pure components (called endmembers ), and their corresponding proportions (called fractional abundances ). In recent years, many studies have revealed that unmixing using spectral information alone does not sufficiently incorporate the spatial information in the remotely sensed hyperspectral image, as the pixels are treated as isolated entities without taking into account the existing local correlation among them. To address this issue, several spatial preprocessing methods have been developed to include spatial information in the spectral unmixing process. In this paper, we present a new spatial preprocessing method which presents several advantages over existing methods. The proposed method is derived from the Simple Linear Iterative Clustering (SLIC) method, which adapts the global search scope of the clustering into local regions. As a result, the spatial correlation and the spectral similarity are intrinsically incorporated at the clustering step, which results in O ( N ) computational complexity of the clustering procedure with N being the number of pixels in the image. First, a regional clustering is iteratively performed by using spatial and spectral information simultaneously. The obtained result is a set of clustered partitions that exhibit both spectral similarity and spatial correlation. Then, for each partition we select a subset of candidate pixels with high spectral purity. Finally, the obtained candidate pixels are gathered together and fed to a spectral-based endmember extraction method to extract the final endmembers and their corresponding fractional abundances. Our newly developed method naturally integrates the spatial and the spectral information to retain the most relevant endmember candidates. Our experimental results, conducted using both synthetic and real hyperspectral scenes, indicate that the proposed method can obtain accurate unmixing results with less than 0.5% of the number of pixels used by other state-of-the-art methods. This confirms the advantages of integrating spatial and spectral information for hyperspectral unmixing purposes. [ABSTRACT FROM AUTHOR]

This paper introduces a new optical image encryption system based on Fresnel diffraction and phase iterative algorithm, which can realize the conversion between different images. The method is based on the optical system of free space transmission, and uses the iterative phase retrieval algorithm to encode an image into two phase masks and a ciphertext. Unlike the existed methods, the ciphertext is a visible image, which can be used to achieve the conversion of one image to another image. In order to enhance the security, two phase masks are combined into a wide-scale phase mask by the double image cross pixel scrambling approach. In the decryption process, the wide-scale phase mask is re-decrypted into two random phase masks using a random shift matrix. The ciphertext and the first phase mask are placed on the input plane and the second random phase mask is placed on the transformation plane. The Fresnel diffraction principle can be used to obtain the plaintext information on the output plane. Theoretical analysis and simulation results show that the encryption system is feasible and quite safe. [ABSTRACT FROM AUTHOR]

With advantages of portability, low-cost and noninvasive detection, electrical impedance tomography (EIT) has great potential application value as a bedside monitor. Because image reconstruction in EIT involves addressing serious ill-posed inverse problems, realizing EIT online dynamic display still remains a challenging work. In this paper, an EIT imaging system with the function of online dynamic display was designed. Split augmented Lagrangian shrinkage algorithm (SALSA), which transforms an unconstrained optimization EIT inverse problem into an equivalent constrained optimization problem, was adopted in the proposed system. On the basis of EIT imaging simulation, a high-precise Compact PCI-based EIT system was implemented with FPGA. And experiments of EIT cylinder imaging and in-vivo monitoring were conducted to verify the feasibility of the system. The results indicated that the online dynamic display frame rate of the system, which was centered with a computer (Intel i7 2.2 GHz, 4 GB RAM), was up to 32 fps. [ABSTRACT FROM AUTHOR]

Fatigue can lead to decreased work performance and poorer safety and health condition. Fatigue is ubiquitous in production and in life, while the research on it is mainly concentrated in the automotive driving, aircraft piloting and other fields, and it is insufficient to study on the fatigue of fixed-position staff. This paper puts forward a non-contact visual image method, which can monitor the extent of fatigue of fixed-position staff. Fatigue threshold used in judgment is obtained by processing the recorded data of visual images of the experimental subjects when fatiguing and by analyzing eye closure time, percentage of eyelid closure (PERCLOS) value, frequency and number of blinks. The results show that there is significant difference among the four indicators before and after experiment subjects undergo fatigue. The fatigue of experimental subjects is obvious when eye closure time is 3.5 s/min, PERCLOS value 6%, and blink frequency 0.4 times/s. This provides a reference for a wider range of detection of fatigue and a method for avoiding mistakes and accidents. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new distance called the k -max distance that is intended for graphs and images. The length of a path is defined as the sum of the k maximum arc weights along the path. The distance between two nodes is the length of the shortest path between them. We show that the k -max distance is a metric. The algorithm for computing the k -max distance is presented. Certain positive properties of the k -max distance are shown, namely in the context of measuring the distances for image segmentation. The comparison with the geodesic distance, the max-arc distance, the minimum barrier distance, and the random walker technique is carried out in the segmentation of real-life images. [ABSTRACT FROM AUTHOR]

Stereo 3D reconstruction of ocean waves is gaining more and more popularity in the oceanographic community and industry. Indeed, recent advances of both computer vision algorithms and computer processing power now allow the study of the spatio-temporal wave field with unprecedented accuracy, especially at small scales. Even if simple in theory, multiple details are difficult to be mastered for a practitioner, so that the implementation of a sea-waves 3D reconstruction pipeline is in general considered a complex task. For instance, camera calibration, reliable stereo feature matching and mean sea-plane estimation are all factors for which a well designed implementation can make the difference to obtain valuable results. For this reason, we believe that the open availability of a well tested software package that automates the reconstruction process from stereo images to a 3D point cloud would be a valuable addition for future researches in this area. We present WASS ( http://www.dais.unive.it/wass ), an Open-Source stereo processing pipeline for sea waves 3D reconstruction. Our tool completely automates all the steps required to estimate dense point clouds from stereo images. Namely, it computes the extrinsic parameters of the stereo rig so that no delicate calibration has to be performed on the field. It implements a fast 3D dense stereo reconstruction procedure based on the consolidated OpenCV library and, lastly, it includes set of filtering techniques both on the disparity map and the produced point cloud to remove the vast majority of erroneous points that can naturally arise while analyzing the optically complex nature of the water surface. In this paper, we describe the architecture of WASS and the internal algorithms involved. The pipeline workflow is shown step-by-step and demonstrated on real datasets acquired at sea. [ABSTRACT FROM AUTHOR]

There have been a lot of methods to address the recognition of complete face images. However, in real applications, the images to be recognized are usually incomplete, and it is more difficult to realize such a recognition. In this paper, a novel convolution neural network frame, named a low-rank-recovery network (LRRNet), is proposed to conquer the difficulty effectively inspired by matrix completion and deep learning techniques. The proposed LRRNet first recovers the incomplete face images via an approach of matrix completion with the truncated nuclear norm regularization solution, and then extracts some low-rank parts of the recovered images as the filters. With these filters, some important features are obtained by means of the binaryzation and histogram algorithms. Finally, these features are classified with the classical support vector machines (SVMs). The proposed LRRNet method has high face recognition rate for the heavily corrupted images, especially for the images in the large databases. The proposed LRRNet performs well and efficiently for the images with heavily corrupted, especially in the case of large databases. Extensive experiments on several benchmark databases demonstrate that the proposed LRRNet performs better than some other excellent robust face recognition methods. [ABSTRACT FROM AUTHOR]

Many methods have recently been proposed for subspace clustering, but they are often unable to handle incomplete data because of missing entries. Using matrix completion methods to recover missing entries is a common way to solve the problem. Conventional matrix completion methods require that the matrix should be of low-rank intrinsically, but most matrices are of high-rank or even full-rank in practice, especially when the number of subspaces is large. In this paper, a new method called Sparse Representation with Missing Entries and Matrix Completion is proposed to solve the problems of incomplete-data subspace clustering and high-rank matrix completion. The proposed algorithm alternately computes the matrix of sparse representation coefficients and recovers the missing entries of a data matrix. The proposed algorithm recovers missing entries through minimizing the representation coefficients, representation errors, and matrix rank. Thorough experimental study and comparative analysis based on synthetic data and natural images were conducted. The presented results demonstrate that the proposed algorithm is more effective in subspace clustering and matrix completion compared with other existing methods. [ABSTRACT FROM AUTHOR]

Background subtraction is a widely used technique for detecting moving objects in image sequences. Very often background subtraction approaches assume the availability of one or more clear (i.e., without foreground objects) frames at the beginning of the sequence in input. However, this assumption is not always true, especially when dealing with dynamic background or crowded scenes. In this paper, we present the results of a multi-modal background modeling method that is able to generate a reliable initial background model even if no clear frames are available. The proposed algorithm runs in real-time on HD images. Quantitative experiments have been conducted taking into account six different quality metrics on a set of 14 publicly available image sequences. The obtained results demonstrate a high-accuracy in generating the background model in comparison with several other methods. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel bottom-up saliency detection algorithm from the perspective of covariance matrices on a Riemannian manifold. Each superpixel is described by a region covariance matrix on Riemannian Manifolds. We carry out a two-stage sparse coding scheme via Log-Euclidean kernels to extract salient objects efficiently. In the first stage, given background dictionary on image borders, sparse coding of each region covariance via Log-Euclidean kernels is performed. The reconstruction error on the background dictionary is regarded as the initial saliency of each superpixel. In the second stage, an improvement of the initial result is achieved by calculating reconstruction errors of the superpixels on foreground dictionary, which is extracted from the first stage saliency map. The sparse coding in the second stage is similar to the first stage, but is able to effectively highlight the salient objects uniformly from the background. Finally, three post-processing methods–highlight-inhibition function, context-based saliency weighting, and the graph cut–are adopted to further refine the saliency map. Experiments on four public benchmark datasets show that the proposed algorithm outperforms the state-of-the-art methods in terms of precision, recall and mean absolute error, and demonstrate the robustness and efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel 2D homography computation method based on two real points. The homography is thus decomposed into three parts. The two real points and their images can be utilized to compute the first and the last parts respectively, while other primitives (could be point(s), line(s) and conic) can be utilized to compute the middle part which is a hyperbolic similarity transformation. We introduce the proposed method in a 2D pattern with a conic and a coplanar line, and apply the method in various other geometric patterns. Subsequently, many plane-based vision tasks, such as camera calibration, pose estimation and metric rectification can be solved in a unified way as polynomial systems. The experiments with simulated and real data verify the correctness and the versatility of our algorithm. [ABSTRACT FROM AUTHOR]

The alternating direction method of multipliers (ADMM) is an efficient splitting method for solving separable optimization with linear constraints. In this paper, an inertial proximal partially symmetric ADMM is proposed for solving linearly constrained multi-block nonconvex separable optimization, which can improve the computational efficiency by considering the ideas of inertial proximal point and linearization technique. Meanwhile, the proposed method updates the Lagrange multiplier twice and considers different relaxation factors in each iteration. Under the assumption that the generated sequence is bounded and the auxiliary function satisfies the Kurdyka-Łojasiewicz property, the global convergence of the proposed method with a more relaxed parameter range is analyzed. Moreover, some numerical results on SCAD (for smoothly clipped absolute deviation), image processing and robust PCA nonconvex problems are reported to demonstrate the efficiency and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

• This paper presents a future prediction technique for using in S.R. measurement. • The suggested method reduces the required data amount used in future prediction. • To obtain a quicker response, the time series analysis is combined with the ICA and MVE algorithms. • The maximum error of suggested method is equal or less than 8% for Ra ≥ 0.4 μm. The momentary control of manufacturing processes is one of the ways to increase the productivity of production lines. The online measurement of the surface roughness by non-contact methods can be utilized in order to predict the future of surface texture and modify the machining parameters. In the technique proposed at this paper, the surface texture is extracted by combining the 2D surface photography and wavelet approach. Then, by extracting the time delay parameters, the embedding dimension and the false nearest neighbor of the produced surface texture, the future surface roughness is predicted. The results show that this technique can be used in lapping, grinding, turning, and milling processes. Although the maximum roughness error occurred in the surface roughness prediction is 24%, the prediction error is almost constant after Ra = 0.4 μm in different machining processes (about 7%). This study is in line with the development of the proposed method by Pour (2018). [ABSTRACT FROM AUTHOR]

This paper implements a real-time and directional counting algorithm using the Graphic Processing Unit (GPU) Programming for the purpose of detecting and counting people. We use the Compute Unified Device Architecture (CUDA) as the environment of the GPU programming. The proposed algorithm is implemented for detecting and counting people employing the single virtual line and two virtual lines, respectively, using three video streams and two GPU graphic cards GeForce GT 630 and GeForce GTX 550Ti. We first test the video streams on the algorithm by using GeForce GT 630 together with applying the single virtual line and two virtual lines, respectively. Then, we repeat the same procedures for the GPU graphic card GeForce GTX 550Ti. The obtained experimental results show that our proposed algorithm running on GPU can be successfully programmed and implemented for people detecting and counting problems. [ABSTRACT FROM AUTHOR]

Enhancement of hazy images or video is challenging task because of low contrast exhibited in them. Global contrast stretching methods have been successful in restoring contrasts but problems like overcompensation, truncation of pixel values amounting to loss of information tends to creep in. Artifacts may be introduced and images may loose its colorfulness. This paper presents an evolutionary enhancement method for restoring contrast in images or videos while preserving its colorfulness and brightness. The study proposes a novel histogram equalization method inspired by principles of water cycle algorithm. The proposed method first smoothes Y channel of YCbCr color space and divides input frame into two components using Otsu's 2D thresholding. A set of weighing constraints have been formulated and applied to both components individually in a controlled manner. Water cycle algorithm has been employed to exploit an optimal value of weighing factors for enforcement of constraints on individual components. A three dimension objective function has been designed to suitably perform equalization and control enhancement process. Experimental results show that proposed method is effective in removing haze like patterns in images and videos. [ABSTRACT FROM AUTHOR]

Robust detection of infrared dim and small target contributes significantly to the infrared systems in many applications. Due to the diversity of background scene and unique characteristic of target, the detection of infrared targets remains a challenging problem. In this paper, a novel approach based on total variation regularization and principal component pursuit (TV-PCP) is presented to deal with this problem. The principal component pursuit model only considers the low-rank feature of background images, which will result in poor detection ability in non-uniform and non-smooth scenes. We take into account the total variation regularization term to thoroughly describe background feature, which can achieve good detection result as well as good background estimation result. Firstly, the input infrared image is transformed to a patch image model. Secondly, the TV-PCP model is presented on the patch image. An effective optimization algorithm is proposed to solve this model. Experiments on six real datasets show that the proposed method has superior detection ability under various backgrounds, especially with good background suppression performance and low false alarm rate. [ABSTRACT FROM AUTHOR]

This paper presents a new set of three-dimensional rotation invariant texture descriptors based on the well-known local binary patterns (LBPs). In the approach proposed here, we extend an existing three-dimensional LBP based on the region growing algorithm using existing features developed exquisitely for two-dimensional LBPs (pixel intensities and differences). We have conducted experiments on a synthetic dataset of three-dimensional randomly rotated texture images in order to evaluate the discriminatory power and the rotation invariant properties of our descriptors as well as those of other two-dimensional and three-dimensional texture descriptors. Our results demonstrate the effectiveness of the extended LBPs and improvements against other state-of-the-art hand-crafted three-dimensional texture descriptors on this dataset. Furthermore, we prove that the extended LBPs can be used in medical datasets to discriminate between MR images of oxygenated and non-oxygenated brain tissues of newborn babies. [ABSTRACT FROM AUTHOR]

Enhancement of ancient manuscripts is a predominant field in the world of image processing. Binarization is the essential tool to recognize the text through OCR technologies for the preservation of the ancient manuscripts. This paper addresses the problem of enhancing and binarizing the deteriorated old manuscripts by a simple novel method called Log based binarization. The proposed method modifies the pixel intensity so as to better differentiate the black pixels and thus produce better binarization results, has been explored. Detailed comparison of existing thresholding algorithms with the new algorithm has been given. The inspiration for the new algorithm was drawn from the standard Niblack algorithm and Nick Algorithm. [ABSTRACT FROM AUTHOR]

Automatic detection of defective oranges by computer vision system is not easy because of the uneven lightness distribution on the surface of oranges. It means that the methods only directly using global segmentation provide unsatisfactory results when orange images present faint defect characters or inhomogeneous surface. The contrast between sound and defective regions can be used to produce more accurate segmentation results, which is more capable of detecting pixels lying around the defect boundary on orange surface based on the local segmentation method. In this paper, we study and propose a sliding comparison window local segmentation algorithm and also presents the detailed image processing procedure including removal of background pixels, image binarization using local segmentation, image subtraction, image morphological modification, removal of stem end pixels for detecting surface defect in an orange gray-level image. This method is an original contribution that allows successful segmentation of various types of surface defects (e.g., insect injury, wind scarring, thrips scarring, scale infestation, canker spot, dehiscent fruit, copper burn, phytotoxicity).The image segmentation algorithm was tested with 1191 samples of oranges. The proposed algorithm was able to correctly detect 97% of the defective orange. Future work will be focused on whole surface and fast on-line inspection. [ABSTRACT FROM AUTHOR]

Object proposals have recently emerged as an essential cornerstone for object detection. The current state-of-the-art object detectors employ object proposals to detect objects within a modest set of candidate bounding box proposals instead of exhaustively searching across an image using the sliding window approach. However, achieving high recall and good localization with few proposals is still a challenging problem. The challenge becomes even more difficult in the context of autonomous driving, in which small objects, occlusion, shadows, and reflections usually occur. In this paper, we present a robust object proposals re-ranking algorithm that effectivity re-ranks candidates generated from a customized class-independent 3DOP (3D Object Proposals) method using a two-stream convolutional neural network (CNN). The goal is to ensure that those proposals that accurately cover the desired objects are amongst the few top-ranked candidates. The proposed algorithm, which we call DeepStereoOP, exploits not only RGB images as in the conventional CNN architecture, but also depth features including disparity map and distance to the ground. Experiments show that the proposed algorithm outperforms all existing object proposal algorithms on the challenging KITTI benchmark in terms of both recall and localization. Furthermore, the combination of DeepStereoOP and Fast R-CNN achieves one of the best detection results of all three KITTI object classes. [ABSTRACT FROM AUTHOR]

In this paper, a multilayer graph-based saliency detection algorithm by merging fixation is proposed to effectively detect salient objects in complex scenes. First, the fixation location of an image is acquired by using fixation prediction models. This is based on a motivation that human visual attention system would quickly focus on salient regions before further processing. Then, by merging the fixation saliency map into an over-segmented region map, we can obtain a coarse detection result which most likely contains salient objects. To further improve the performance of our saliency detection, the next key idea is to leverage color contrast between superpixels as features in CIE-Lab space and resolve saliency estimation of coarse regions via a multilayer graph-based framework. The final saliency detection is achieved by combining the coarse detection result with multilayer saliency maps. Extensive experiments are conducted on five benchmark datasets. Experimental results show that the proposed method yields comparable or better results in terms of PR curve, ROC curve, and F-measure, and is robust to deal with both cluttered and clean scenes. [ABSTRACT FROM AUTHOR]

In this paper, a modified nonlocal means denoising (NL-means) algorithm is proposed for rolling element bearing fault diagnosis. Although, nonlocal means denoising is widely used in image processing, this algorithm is rarely used in 1-D signal processing. The present work deals with application of 1-D nonlocal means denoising method for enhancement of fault signature in rolling element bearings. The parameters for the NL-means method are obtained by maximizing kurtosis value of bearing vibration signal. The proposed method is compared with minimum entropy deconvolution (MED) technique and the results indicate that the proposed method performs better for bearing fault diagnosis. The method is shown to be robust against various noise levels. Further, envelope spectrum of bearing vibration signal is also used to obtain characteristic bearing defect frequencies. [ABSTRACT FROM AUTHOR]

This paper is an in-depth look at the problem of removing the blur from a complex motion-blurred star image. Accordingly, a simple yet effective l p ( 0 < p ≤ 1 ) -regularized deblurring method based on stars image intensity is proposed. The model builds on the principle that the intensity of clear star image is in accordance with Laplacian distribution or generalized p Gaussian distribution. Further, two algorithms are introduced to solve the ensuing non-smooth ( p = 1 ) or non-convex ( p < 1 ) constrained optimization problem. Simulations and actual star image restoration experiment are implemented to demonstrate that the centroids extraction accuracy of the proposed method is higher than 0.1 pixel, the running time is 3 to 5 times better than Richardson–Lucy filter or other methods based on image gradient constraint, and the peak signal to noise ratio (PSNR) of recovered star images excel results of several other image deconvolution methods. [ABSTRACT FROM AUTHOR]

Retargeting algorithms are used to transfer and display images on devices with various sizes and resolutions. All of these algorithms try to preserve the important parts of image against distortions while producing a retargeted image with visual quality comparable with the original one. The main challenge in different algorithms is to find a suitable energy function that properly estimates the importance of each pixel in image. Hence the energy map needs to be improved. In this paper we propose a novel energy function which combines the information from saliency map, depth map and gradient map. We also present an algorithm to adaptively assign proper weights to these three importance maps for each input image. Then we calculate a switching threshold based on energy map that determines when to apply seam carving or scaling. The idea is to use a combination of seam carving and scaling to preserve the structure of important parts of image against distortion when the image size decreases beyond a point. This method reduces shape deformations and visual artifacts in salient regions of images and produces better quality output images. The results of the proposed method show superior visual quality in produced images in comparison to the state-of-the-arts. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel saliency detection algorithm. The saliency of an image element is defined not only as its contrast to the background but as its similarity to the foreground. First, we extract background seeds as well as their spatial layout information from image boundaries to compute the background-based saliency map. Second, we generate a compact foreground region from the first-stage saliency map to describe the appearance and location of the salient object and calculate the foreground-based saliency map accordingly. We integrate these two saliency maps and further refine the unified one to obtain a more smooth and accurate saliency map. Each component of the presented algorithm is evaluated on the public available datasets and the experimental results also show that the presented algorithm achieves favorable performance compared to the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Human action recognition from RGB-D (Red, Green, Blue and Depth) data has attracted increasing attention since the first work reported in 2010. Over this period, many benchmark datasets have been created to facilitate the development and evaluation of new algorithms. This raises the question of which dataset to select and how to use it in providing a fair and objective comparative evaluation against state-of-the-art methods. To address this issue, this paper provides a comprehensive review of the most commonly used action recognition related RGB-D video datasets, including 27 single-view datasets, 10 multi-view datasets, and 7 multi-person datasets. The detailed information and analysis of these datasets is a useful resource in guiding insightful selection of datasets for future research. In addition, the issues with current algorithm evaluation vis-á-vis limitations of the available datasets and evaluation protocols are also highlighted; resulting in a number of recommendations for collection of new datasets and use of evaluation protocols. [ABSTRACT FROM AUTHOR]