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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]

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]

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]

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]

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]

Abstract: For a continuous elliptical Gaussian flux image over the infinite X–Y plane, the parameter relationships between the elliptical Gaussian flux image and the equivalent circular Gaussian flux images are clearly discussed in both mathematical and graphical ways in this paper with respect to the radial power distribution around the image centre (peak flux location in image plane). This paper presents six typical methods (SIGMA-2Mean, SIGMA-1Mean, SIGMA-0Mean in Class One, SIGMA-RPeak, SIGMA-RMean, SIGMA-SqrRMean in Class Two) to give the equivalent circular Gaussian flux images to the elliptical Gaussian flux image, tries to link these circular Gaussian fitting methods to the relevant solar engineering applications, and makes some assessment comments on the elliptical/circular Gaussian modelling in solar mirror optics. By comparing the approximation performance among these six typical fitting methods, it reveals the reason for the 90% intercept over-estimation phenomenon of Francisco J. Collado’s one-point circular Gaussian fitting practice relative to the experimental flux image. The detailed algorithm for automatically finding out the major/minor axes and the image centre of the digital elliptical flux image is also provided in this paper. SIGMA-2Mean and SIGMA-SqrRMean are equivalent for an elliptical flux image, but they are applied in different ways to figure out either the reasonable intercept factor of the experimental flux image on the physical target plane respecting the aperture region of interest, or the power spillage over the limited experimental target plane. At last, this paper introduces the interpolation reconstruction of an elliptical Gaussian flux image over a rectangular domain just based on the boundary pixel values, so it is quite useful for solar engineering, such as fast simulation of a flux image concentrated by a mirror, and also instant approximation of the flux density over the receiver aperture by the linear array of radiometers around the receiver aperture, when the central receiver system is in the normal working state. [Copyright &y& Elsevier]

This paper proposed a new method to improve the visibility and contrast of color images. The proposed method is a joint technique scheme of Color Adaptive Histogram Equalization (CAHE) and interpolation method. CAHE is an effective algorithm for image contrast enhancement, and interpolation method enhances the image by increasing the number of pixels without missing any intermediate component of image. Hence, the combination of both may give the good agreement of image visuality and contrast. [ABSTRACT FROM AUTHOR]

This paper presents a novel single image super-resolution (SR) reconstruction method using shifted kernel regression. Assuming that a low-resolution (LR) image is made by the shifted regression-based image degradation model, the proposed SR process shifts pixels in a regularly sampled LR image with a subpixel precision according to local gradient. The optimum displacement of each pixel is estimated using the Gaussian filtered second derivatives. The shifted low-resolution image is finally up-scaled by estimating the regression coefficients of the shifted kernels in the spatially adaptive manner. Experimental results show that the proposed method overcomes the limitation of existing intensity-based SR algorithms. The proposed SR algorithm can successfully restore sharp, clear edges without undesired artifacts such as ringing, inverted gradient, halo effects, etc. Without using an iterative process, the proposed single image SR algorithm can easily be implemented in the form of either pre- or post-processing filters for further enhancing the SR result of existing methods. [ABSTRACT FROM AUTHOR]

Multi-frame image super-resolution (SR) aims to utilize information from a set of low resolution (LR) images to compose a high-resolution (HR) one. As it is desirable or essential in many real applications, recent years have witnessed the growing interest in the problem of multi-frame SR reconstruction. In this paper, we propose a novel multi-frame image super-resolution algorithm built on the regularization framework. The objective functional to be minimized for the regularization framework consists of a fidelity term and a regularization term. A new fidelity term is formed by combining L 1 norm and L 2 norm according to defined Residual Weight Parameters (RWP) and Channel Weight Parameters (CWP). A new regularization term which adopts Regional Adaptive Weight Coefficients (RAWC) is proposed to keep edge and flat regions, which are implicitly described in LR images, sharp and smooth, respectively. Thorough experimental results show the new algorithm using both fidelity and regularization terms for SR reconstruction is effective than other methods. [ABSTRACT FROM AUTHOR]

Non-local means (NLM) filtering is an efficacious algorithm in image denoising which searches the similar neighborhoods and estimates the pixel by averaging these neighborhoods. Some internal parameters such as patch size, search window size and smoothing strength have serious effects on filtering performance. This paper proposes an improved version of NLM by using weak textured patches based single image noise estimation and two-stage NLM with adaptive smoothing parameter. Our proposed method firstly applies weak textured patches based noise estimation to achieve the noise level of input noisy image. Then relying on the estimated noise level, we apply the first stage NLM with adaptive smoothing parameter to attain a basic denoised image. After that, the basic denoised image is refined by the second stage of NLM with smaller smoothing strength. Our experimental results show that the proposed algorithm outperforms the NLM and some NLM recent variants both in visual quality and numerical measures. Additionally, the potential halo effect is almost eliminated in the result images produced by our proposed method. [ABSTRACT FROM AUTHOR]

This paper proposes an image segmentation method for blurred trace infrared images, exploring statistical properties of blurred infrared data to segment target regions. I consider the function of immune factors for blurred infrared image segmentation, combined to the template algorithm framework. First, all of the pixel antigens are divided into three classes by innate recognition factors: target antigens, background antigens and blurred antigens. Next, the innate presentation factors present template characteristics as new features for each antigen. Finally, target and background antigens are used for generating mature adaptive immune factors, and these mature factors will recognise each blurred antigen into two classes: a target pixel or a background pixel. Experimental results indicate that the superstring galaxy template algorithm can improve the target segmentation rate and reduce the segmentation error rate. [ABSTRACT FROM AUTHOR]

Abstract: Image fusion is an alternative for enriching visual experience by combining images with complementary information. Assessment of image fusion performance would prove invaluable for parameter optimizing, fusion scheme selection and testing. This paper presents a new image fusion performance measure, which consists of two parts: the first one is a similarity measure for predicting the amount of information transferred form original images to fused image in Riesz domains; the other is a measure for characterizing the contrast of the fused image. Considering that different morphological components share different importance in Human Visual System, a gradient based image content partition algorithm is adopted to segment original images and fused images into three parts, and according the partition results, different weights are given to different pixels in the process of similarity measure calculating. Experimental results demonstrate the superiority of our measure compared with conventional measures in terms of computation complexity and accuracy. [Copyright &y& Elsevier]

Abstract: The representation of physical characteristics is the most essential feature of mathematical models used for the detection of defects in automatic inspection systems. However, the feature of defects and formation of the defect image are not considered enough in traditional algorithms. This paper presents a mathematical model for defect inspection, denoted as the localized defects image model (LDIM), is different because it modeling the features of manual inspection, using a local defect merit function to quantify the cost that a pixel is defective. This function comprises two components: color deviation and color fluctuation. Parameters related to statistical data of the background region of images are also taken into consideration. Test results demonstrate that the model matches the definition of defects, as defined by international industrial standards IPC-A-610D and IPC-A-600G. Furthermore, the proposed approach enhances small defects to improve detection rates. Evaluation using a defects images database returned a 100% defect inspection rate with 0% false detection. Proving that this method could be practically applied in manufacture to quantify inspection standards and minimize false alarms resulting from human error. [Copyright &y& Elsevier]

Superpixel is one of the most popular image over-segmentations with broad applications in the computer vision field to reduce their computations by replacing pixels as primitives. The main concerns of one superpixel generation algorithm are its accuracy and efficiency. One of the most important things in superpixel accuracy is to fit the image boundaries tightly with a few pixels as possible (namely minimal contour density, which is measured by the percent of superpixel contour pixels in the whole image). In this paper, we propose a new fast algorithm based on the clustering method to produce superpixels accurately with low contour density. First, we adopt the linear path from a pixel to one superpixel seed to define a regular term and propose a new distance measurement between them. In addition, we introduce the gradient and Local Binary Pattern (LBP) features and propose formulas of parameters in the proposed method adaptively. In this way, we can use the new distance measurement to group pixels as initial regions adaptively and produce the final superpixels by merging those small ones. Finally, we test the new algorithm on two public datasets and compare it with the state-of-the-art. Our method can generate superpixels with lower contour density while being competitive in accuracy and computational time. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we address the problem of fusing multi-focus images in dynamic scenes. The proposed approach consists of three main steps: first, the focus information of each source image obtained by morphological filtering is used to get the rough segmentation result which is one of the inputs of image matting. Then, image matting technique is applied to obtain the accurate focused region of each source image. Finally, the focused regions are combined together to construct the fused image. Through image matting, the proposed fusion algorithm combines the focus information and the correlations between nearby pixels together, and therefore tends to obtain more accurate fusion result. Experimental results demonstrate the superiority of the proposed method over traditional multi-focus image fusion methods, especially for those images in dynamic scenes. [Copyright &y& Elsevier]

Abstract: Finite depth-of-field poses a problem in light optical imaging systems since the objects present outside the range of depth-of-field appear blurry in the recorded image. Effective depth-of-field of a sensor can be enhanced considerably without compromising the quality of the image by combining multi-focus images of a scene. This paper presents a block-based algorithm for multi-focus image fusion. In general, finding a suitable block-size is a problem in block-based methods. A large block is more likely to contain portions from both focused and defocused regions. This may lead to selection of considerable amount of defocused regions. On the other hand, small blocks do not vary much in relative contrast and hence difficult to choose from. Moreover, small blocks are more affected by mis-registration problems. In this work, we present a block-based algorithm which do not use a fixed block-size and rather makes use of a quad-tree structure to obtain an optimal subdivision of blocks. Though the algorithm starts with blocks, it ultimately identifies sharply focused regions in input images. The algorithm is simple, computationally efficient and gives good results. A new focus-measure called energy of morphologic gradients is introduced and is used in the algorithm. It is comparable with other focus measures viz.energy of gradients, variance, Tenengrad, energy of Laplacian and sum modified Laplacian. The algorithm is robust since it works with any of the above focus measures. It is also robust against pixel mis-registration. Performance of the algorithm has been evaluated by using two different quantitative measures. [Copyright &y& Elsevier]

Abstract: Edit propagation on images/videos has become more and more popular in recent years due to simple and intuitive interaction. It propagates sparse user edits to the whole data following the policy that nearby regions with similar appearances receive similar edits. While it gives a friendly editing mode, it often produces aliasing artifacts on edge pixels. In this paper, we present a simple algorithm to resolve this artifact for edit propagation. The key in our method is a new representation called Antialias Map, in which we represent each antialiased edge pixel by a linear interpolation of neighboring pixels around the edge, and instead of considering the original edge pixels in solving edit propagation, we consider those neighboring pixels. We demonstrate that our work is effective in preserving antialiased edges for edit propagation and could be easily integrated with existing edit propagation methods such as [1,2]. [Copyright &y& Elsevier]

Abstract: To construct effective image sharpness measure with good discrimination ability, a multi-scale toggle operator based algorithm is proposed in this paper. Firstly, toggle operator is used to extract image details. And, the multi-scale theory is used in toggle operator to extract multi-scale image details. Then, the final image details are obtained through applying the pixel-wise maximum operation on the extracted multi-scale image details. Finally, the mean value of the obtained final image details is used as the constructed image sharpness measure. Experimental results on different images show that, the proposed image sharpness measure could correctly quantify the clarity of image and is suitable for discriminating the image clarity change. [Copyright &y& Elsevier]

Abstract: This paper puts forward a new scene based nonuniformity correction algorithm for IRFPA. This method adopts phase-correlation method for motion estimation and takes the sum of mean-square errors of the pixel brightness between several adjacent frames as the cost function when the brightness constancy assumption between two adjacent frames is satisfied. Nonuniformity correction parameters could be estimated by minimizing such cost function. In order to reduce calculation quantity, we can divide these images into several subblocks, and solve for the optimum solution of the cost function respectively in each subblock. From the analysis, it is shown that the optimum solution is of global uniqueness when all the elements in subblocks could satisfy the ergodicity condition. Then the estimated value of nonuniformity correction parameters could be deduced by minimizing the cost functions. The nonuniformity correction experiments for both infrared image sequence with simulated nonuniformity and infrared imagery with real nonuniformity show that the proposed algorithm could achieve a great correction effect by only analyzing a small number of frames. [Copyright &y& Elsevier]

Abstract: The total focusing method (TFM) is a post-processing imaging algorithm for ultrasonic array data in which the array is synthetically focused at every image pixel. In this paper, the performance of the TFM imaging algorithm is examined in the presence of inter-element variability in the array, coupling and instrumentation. The inter-element variability includes amplitude, phase and delay inconsistencies. First, typical experimentally observed inter-element variations are extracted by back-propagation of the pulse-echo signals reflected from the planar back wall of a defect-free sample. The effect of different degrees of variability in the amplitude, phase and time delay associated with each element is then investigated through simulation. The peak amplitude and a measure of the size of the point spread function are used to assess the quality of TFM images simulated for a 1mm side-drilled hole. It is shown that the performance is most sensitive to variability of time-delay rather than amplitude or phase. [Copyright &y& Elsevier]

Abstract: The visual secret sharing for multiple secrets (VSSM) allows for the encryption of a greater number of secret images into a given image area. Previous researches on VSSM schemes incur a very serious pixel expansion that will damage capable of increasing the capacity of secret image encryption. Moreover, the most of VSSM schemes will decrease the contrast of recover images while the amount of secret image encryption increases. These drawbacks limit applicability of the existing VSSM schemes. In this paper, we propose a highly efficient encryption algorithm to cope with this problem. The proposed algorithm adopts a novel hybrid encryption approach that includes a VC-based encryption and a camouflaging process. The experimental results demonstrate that the proposed approach not only can increase the capacity efficient for VSSM schemes, but also maintains an excellent level of contrast in the recovered secret images. [Copyright &y& Elsevier]

Abstract: Automatic image segmentation is always a fundamental but challenging problem in computer vision. The simplest approach to image segmentation may be clustering feature vectors of pixels at first, then labeling each pixel with its corresponding cluster. This requires that the clustering on feature space must be robust. However, most of popular clustering algorithms could not obtain a robust clustering result yet, if the clusters in feature space have a complex distribution. Generally, for most of clustering-based segmentation methods, it still needs more constraints of positional relations between pixels in image lattice to be utilized during the procedure of clustering. Our works in this paper address the problem of image segmentation under the paradigm of pure clustering-then-labeling. A robust clustering algorithm which could maintain good coherence of data in feature space is proposed and utilized to do clustering on the L∗a∗b∗ color feature space of pixels. Image segmentation is straightforwardly obtained by setting each pixel with its corresponding cluster. Further, based on the theory of Minimum Description Length, an effective approach to automatic parameter selection for our segmentation method is also proposed. We test our segmentation method on Berkeley segmentation database, and the experimental results show that our method compares favorably against some state-of-the-art segmentation methods. [Copyright &y& Elsevier]

Abstract: Generalized fuzzy c-means clustering algorithm with improved fuzzy partitions (GIFP_FCM) is a novel fuzzy clustering algorithm. However when GIFP_FCM is applied to image segmentation, it is sensitive to noise in the image because of ignoring the spatial information contained in the pixels. In order to solve this problem, a novel fuzzy clustering algorithm with non local adaptive spatial constraint (FCA_NLASC) is proposed in this paper. In the proposed method, a novel non local adaptive spatial constraint term is introduced to modify the objective function of GIFP_FCM. The characteristic of this technique is that the adaptive spatial parameter for each pixel is designed to make the non local spatial information of each pixel playing a different role in guiding the noisy image segmentation. Segmentation experiments on synthetic and real images, especially magnetic resonance (MR) images, are performed to assess the performance of an FCA_NLASC in comparison with GIFP_FCM and fuzzy c-means clustering algorithms with local spatial constraint. Experimental results show that the proposed method is robust to noise in the image and more effective than the comparative algorithms. [ABSTRACT FROM AUTHOR]

Abstract: As one of widely used clustering algorithms, spectral clustering clusters data using the eigenvectors of the Laplacian matrix derived from a dataset and has been successfully applied to image segmentation. However, spectral clustering algorithms are sensitive to noise and other imaging artifacts because of not taking into account the spatial information of the pixels in the image. In this paper, a novel non-local spatial spectral clustering algorithm for image segmentation is presented. In the proposed method, the objective function of weighted kernel k-means algorithm is firstly modified by incorporating the non-local spatial constraint term. Then the equivalence between the objective functions of normalized cut and weighted kernel k-means with non-local spatial constraints is given and a novel non-local spatial matrix is constructed to replace the normalized Laplacian matrix. Finally, spectral clustering techniques are applied to this matrix to obtain the final segmentation result. The novel algorithm is performed on synthetic and real images, especially magnetic resonance (MR) images, and compared with the traditional spectral clustering algorithms and segmentation algorithms with spatial information. Experimental results demonstrate that the proposed algorithm is robust to noise in the image and obtains more effective performance than the comparison algorithms. [ABSTRACT FROM AUTHOR]

Abstract: For the uncontrolled micro-scanning where the displacement of image sequence is random, a sub-pixel processing algorithm based on boundary recursive and subsequent error optimization is proposed in this paper. Neighboring pixels are used to approximate the boundary pixels of the original image in the algorithm. Then the approximate error is optimized by the gray statistical principle. At last a sub-pixel image with high resolution is reconstructed. The results show that our algorithm is better than the general over-sample reconstruction algorithms and robust in different displacement situations. [ABSTRACT FROM AUTHOR]

Abstract: This paper adds to the abundant visual tracking literature with two main contributions. First, we illustrate the interest of using Graphic Processing Units (GPU) to support efficient implementations of computer vision algorithms, and secondly, we introduce the use of point-based 3D models as a shape prior for real-time 3D tracking with a monocular camera. The joint use of point-based 3D models together with GPU allows to adapt and simplify an existing tracking algorithm originally designed for triangular meshes. Point-based models are of particular interest in this context, because they are the direct output of most laser scanners. We show that state-of-the-art techniques developed for point-based rendering can be used to compute in real-time intermediate values required for visual tracking. In particular, apparent motion predictors at each pixel are computed in parallel, and novel views of the tracked object are generated online to help wide-baseline matching. Both computations derive from the same general surface splatting technique which we implement, along with other low-level vision tasks, on the GPU, leading to a real-time tracking algorithm. [Copyright &y& Elsevier]

Abstract: High content neuron image processing is considered as an important method for quantitative neurobiological studies. The main goal of analysis in this paper is to provide automatic image processing approaches to process neuron images for studying neuron mechanism in high content screening. In the nuclei channel, all nuclei are segmented and detected by applying the gradient vector field based watershed. Then the neuronal nuclei are selected based on the soma region detected in neurite channel. In neurite images, we propose a novel neurite centerline extraction approach using the improved line-pixel detection technique. The proposed neurite tracing method can detect the curvilinear structure more accurately compared with the current existing methods. An interface called NeuriteIQ based on the proposed algorithms is developed finally for better application in high content screening. [Copyright &y& Elsevier]

Abstract: This paper introduces a novel binarization method based on the concept of transition pixel, a generalization of edge pixels. Such pixels are characterized by extreme transition values computed using pixel-intensity differences in a small neighborhood. We show how to adjust the threshold of several binary threshold methods which compute gray-intensity thresholds, using the gray-intensity mean and variance of the pixels in the transition set. Our experiments show that the new approach yields segmentation performance superior to several with current state-of-the-art binarization algorithms. [Copyright &y& Elsevier]

Abstract: This paper presents an image scrambling encryption algorithm of pixel bit based on chaos map. The algorithm takes advantage of the best features of chaos maps, such as their pseudorandom property, system parameters, sensitive dependence on initial conditions and un-periodicity, combined with the pixel value bits. The new algorithm uses a single chaos map only once to implement the gray scrambling encryption of an image, in which the pixel values ranging from 0 to 255 are distributed evenly, the positions of all pixels are also permutated. In this way, the proposed method transforms drastically the statistical characteristic of original image information, so, it increases the difficulty of an unauthorized individual to break the encryption. Finally, the numerical experimental results show that the image encryption algorithm suggested has perfect hiding ability including large key space, sensitive key to initial conditions, high gray scrambling degree, and is suitable for practical use to protect the security of digital image information over the Internet. [Copyright &y& Elsevier]

Abstract: This paper presents a whole body surface imaging system based on stereo vision technology. We have adopted a compact and economical configuration which involves only four stereo units to image the frontal and rear sides of the body. The success of the system depends on a stereo matching process that can effectively segment the body from the background in addition to recovering sufficient geometric details. For this purpose, we have developed a novel sub-pixel, dense stereo matching algorithm which includes two major phases. In the first phase, the foreground is accurately segmented with the help of a predefined virtual interface in the disparity space image, and a coarse disparity map is generated with block matching. In the second phase, local least squares matching is performed in combination with global optimization within a regularization framework, so as to ensure both accuracy and reliability. Our experimental results show that the system can realistically capture smooth and complete whole body shapes with high accuracy. [Copyright &y& Elsevier]

Abstract: In this paper, we present a new geodesic distance transform that uses a non-Euclidean metric suitable for non-convex discrete 2D domains. The geodesic metric used is defined as the shortest path length through a set of pixels called Locally Nearest Hidden Pixels, and manages visibility zones using bounding angles. The algorithm is designed using ordered propagation, which makes it extremely efficient and linear in the number of pixels in the domain. We have compared our algorithm with the four most similar geodesic distance transform techniques, and we show that our approach has higher accuracy and lower computational complexity. [Copyright &y& Elsevier]

Abstract: An image pixel peer group is defined as the set of its neighbor pixels which are similar to it according to an appropriate distance or similarity measure. This concept has been successfully used to devise algorithms for detection and suppression of impulsive noise in gray-scale and color images. In this paper, we present a novel peer group-based approach intended to improve the trade-off between computational efficiency and filtering quality of previous peer group-based methods. We improve the computational efficiency by using a modification of a recent approach that can only be applied when the distance or similarity measure used fulfills the so-called triangular inequality property. The improvement of the filtering quality is achieved by the inclusion of a refinement stage in the noise detection. The proposed method performs according to the following steps: First, we partition the image into disjoint blocks and we perform a fast classification of the pixels into three types: non-corrupted, non-diagnosed and corrupted; second, we refine the initial findings by analyzing the non-diagnosed pixels and finally every pixel is classified either as corrupted or non-corrupted. Then, only corrupted pixels are replaced so that uncorrupted image data is preserved. Experimental results suggest that the proposed method is able to outperform state-of-the-art methods both in filtering quality and computational efficiency. [Copyright &y& Elsevier]

Abstract: The objective of this paper is to simulate the pixel moirés arising in the liquid crystal display with an image processing technique. The LCD panel and the backlight can be regarded as the micro shutter arrays and the surface illuminator, respectively. The RGB shutters have the spatial periodicity and the illumination lights condensed by a brightness enhancement film (BEF) also have the spatial periodicity depending on the prismatic structure size and orientation of a BEF. Pixel moirés become visible when the spatially aliased periods and amplitudes of illumination lights exceed certain critical values. Avoidance of pixel moirés in the liquid crystal display is a design problem related with choice of prism pitch and orientation of a BEF. In this study an image processing algorithm which can predict the pixel moirés arising in the liquid crystal display was devised, and prediction accuracy was investigated. As a design example, optimal values of prism pitch and orientation of a new BEF having relatively large pitches, 150–200μm were tried to be found with a devised simulator, and simulation results were discussed. [Copyright &y& Elsevier]

Abstract: The Hough transform has been a frequently used method for detecting lines in images. However, when applying Hough transform and derived algorithms using the standard Hough voting scheme on real-world images, the methods often suffer considerable degeneration in performance, especially in detection rate, because of the large amount of edges given by complex background or texture. It is very likely that these edges form false peaks in Hough space and thus produce false positives in the final results, or even suppress true peaks and cause missing lines. To reduce the impact of these texture region edges, a novel method utilizing surround suppression is proposed in this paper. By introducing a measure of isotropic surround suppression, the new algorithm treats edge pixels differently, giving small weights to edges in texture regions and large weights to edges on strong and clear boundaries, and uses these weights to accumulate votes in Hough space. In this way, false peaks formed by texture region edges are suppressed, and the quality of detection results is improved. An efficient computation method for calculating the isotropic surround suppression was also given, accelerating the proposed algorithm. Experimental results on a real-world image base show that the new method improves line detection rate significantly, compared with the standard Hough transform and the Hough transform using gradient direction information to guide the voting process. Though slower than the other two methods, the new algorithm can be preferable in applications where detection rate is of the most concern and where there is no very strict requirement for high speed performance. [Copyright &y& Elsevier]

Abstract: Marker pixel detection is a low-level task used by video-based motion capture systems for motion analysis. This is a highly time-consuming task because there are usually a large number of frames to be processed in motion analysis applications. In this paper a novel method for determining marker pixels is proposed. The basic idea behind the proposed method is to reduce the repeated observations of pixels that have already been examined. The method is compared to two classic fill algorithms that are used for this purpose. The results indicate that the proposed method performs better for small circular markers used in this type of applications. [Copyright &y& Elsevier]

Abstract: This paper presents a new, simple and effective low-level processing edge detection algorithm based on the law of universal gravity. The algorithm assumes that each image pixel is a celestial body with a mass represented by its grayscale intensity. Accordingly, each celestial body exerts forces onto its neighboring pixels and in return receives forces from the neighboring pixels. These forces can be calculated by the law of universal gravity. The vector sums of all gravitational forces along, respectively, the horizontal and the vertical directions are used to compute the magnitude and the direction of signal variations. Edges are characterized by high magnitude of gravitational forces along a particular direction and can therefore be detected. The proposed algorithm was tested and compared with conventional methods such as Sobel, LOG, and Canny using several standard images, with and without the contamination of Gaussian white noise and salt & pepper noise. Results show that the proposed edge detector is more robust under noisy conditions. Furthermore, the edge detector can be tuned to work at any desired scale. [Copyright &y& Elsevier]

In this paper we apply restricted equivalence functions to the computation of the threshold of an image. In the first part we present an algorithm for obtaining the best threshold of a grayscale image with a single object. In the second part we study different algorithms for calculating the optimal threshold. Then we analyze two algorithms for obtaining a sequence of optimal thresholds in images with several objects. Lastly, we compare our results with those obtained with other methods and carry out a study of the time efficiency of the methods we propose. [Copyright &y& Elsevier]

Abstract: Fragile watermarking techniques for digital content have been studied in the past few years. Fragile watermarks are used to determine if a piece of watermarked digital content has been tampered, and distinguish tampered areas from non-tampered areas without referring to the original digital content. In this paper, a general framework for fragile watermark is proposed, and then a novel fragile watermarking scheme for image authentication is presented. The embedding process of fragile watermark starts from computing the difference image between the host image and its chaotic pattern, followed by mapping the difference image into a binary image. The binary image is then inserted into the least significant bit (LSB) bitplane of the host image. In addition, chaotic map is used to generate the chaotic pattern image, which can be used as secret key to improve the security of watermark algorithm. Due to employing permutation transform and chaotic image pattern, the corresponding position relation is broken between pixels in the watermarked image and the watermark. Simulation results and performance analysis show that the presented method is fast, secure and capable of detecting and localizing modification. [Copyright &y& Elsevier]

Abstract: In the performance assessment of an image processing algorithm, an image is often compared with an available reference. Measuring image similarity can be achieved in many ways; comparison algorithm varies from pixel-based mean square error method to structure-based image quality index. In this paper, we present a new feature-based approach that utilizes image phase congruency measurement to quantify the assessment of the similarities or differences between two images. Test results with standard images and industrial inspection images are presented. [Copyright &y& Elsevier]

Abstract: This paper presents fast moving window algorithms for calculating local statistics in a diamond, hexagon, and general polygonal shaped windows of an image which is important for real-time applications. The algorithms for a diamond shaped window requires only seven or eight additions and subtractions per pixel. A fast sparse algorithm only needs four additions and subtractions for a sparse diamond shaped window. A number of other shapes of diamond windows such as skewed or parallelogram shaped diamond, long diamond, and lozenged diamond shaped, are also investigated. Similar algorithms are also developed for hexagon shaped windows. The computation for a hexagon window only needs eight additions and subtractions for each pixel. Fast algorithms for general polygonal shaped windows are also developed. The computation cost of all these algorithms is independent of the window size. A variety of synthetic and real images have been tested. [Copyright &y& Elsevier]

Abstract: A conventional FCM algorithm does not fully utilize the spatial information in the image. In this paper, we present a fuzzy c-means (FCM) algorithm that incorporates spatial information into the membership function for clustering. The spatial function is the summation of the membership function in the neighborhood of each pixel under consideration. The advantages of the new method are the following: (1) it yields regions more homogeneous than those of other methods, (2) it reduces the spurious blobs, (3) it removes noisy spots, and (4) it is less sensitive to noise than other techniques. This technique is a powerful method for noisy image segmentation and works for both single and multiple-feature data with spatial information. [Copyright &y& Elsevier]

Image segmentation process is one of the most interesting and challenging problems in digital image processing tasks. The segmentation process involves finding similar regions within an image. Many segmentation problems are achieved by the incorporation of clustering techniques. One of the most common technique for clustering process is the Fuzzy C-means (FCM) algorithm. However, even when FCM is one of the most popular techniques applied in image segmentation, it presents some issues such as large computational time complexity, noise sensitivity, and initial cluster centers dependency. In order to solve these problems, this paper presents a Histogram Based Fuzzy Clustering (HBFC) technique using an improved version of Firefly Algorithm (FA). In the proposed approach, the FA involves three search strategies: rough set-based population, random attraction and local search mechanism. Also, the clustering process is conducted based on gray level histograms instead of single pixels of an image. Under such circumstances, the occurrence of misclassification of pixels is reduced. A rigorous comparative study is conducted among the proposed approach and several state-of-art Nature-Inspired Optimization Algorithms (NIOAs) and traditional clustering techniques. The numerical results indicate that the proposed approach outperform the well-known NIOA based clustering methods in terms of precision, robustness and quality of the segmented outputs. • An improved Firefly algorithm has been proposed for Image Clustering. • Random Attraction model and rough set have been utilized. • The proposed approach is a histogram based fast fuzzy clustering model. • The proposed method is compared against state-of-art segmentation methods. • Numerical results indicate the superior performance of the proposed method. [ABSTRACT FROM AUTHOR]

• Appropriate local blur scales are selected when image block is reblurred. • Sub-band fusion of DCT coefficients is proposed in calculating blur amount. • Edge blur maps are computed in multi-scale and multi-orientation image windows. • Blur points are added to initialize input image in image matting stage. In common natural image blur, objects that not lie in the focal length of a digital camera generate defocus areas in the photographed image. In this paper, we propose a novel edge-based method for spatially varying defocus blur detection based on reblurred DCT coefficients ratios of the corresponding local patches. This method selects appropriate local reblur scales while detecting the edge points to deal with the problem of different blur degree and texture richness of the local image blocks. A sub-band fusion method of DCT coefficients is proposed to expand the difference between DCT features of in-focus and out-of-focus regions. Edge points blur maps are computed in multi-scale and multi-orientation image windows and more blur points are added to initialize sparse blur maps, finally Matting Laplacian method is used along with multi-scale fusion algorithm to obtain a more accurate blur segmentation. Experimental results present the proposed method has strong advantages in image detail processing and outperforms state-of-the-art methods for blur detection. [ABSTRACT FROM AUTHOR]

This paper proposes an automatic crack detection, localization, and quantification method using the integration of a faster region proposal convolutional neural network (Faster R-CNN) algorithm to detect crack regions. The regions were located using various bounding boxes and a modified tubularity flow field (TuFF) algorithm to segment the crack pixels from the detected crack regions. A modified distance transform method (DTM) was used to measure crack thickness and length in terms of pixel measurement. To validate the proposed method, 100 images were taken in different places with complex backgrounds containing different angles and distances between the camera and the objects. The results obtained from the Faster-R-CNN-based crack damage detection had a 95% average precision. The pixel-level segmentation performance of the modified TuFF algorithm exhibited an authentic outcome, with 83% intersection over union. Finally, the modified DTM algorithm provided 93% accuracy with respect to crack length and thickness with a 2.6 pixel root mean square error. • The first proposal of an automatic hybrid crack segmentation and quantification under complex backgrounds and different lightening conditions. • The hybrid method successfully removes the limitation of independent application of deep learning and image processing method. • The proposed method achieved the AP of the Faster R-CNN is 95%, and the IoU of the modified TuFF with CLAHE is 83%. • The original DTM method is improved to measure the thickness and length of the segmented cracks with an RMS error of 2.6 pixels, providing crack length accuracy of 93%. [ABSTRACT FROM AUTHOR]