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

Abstract: Accurate local region description is a keypoint in many applications and has been the topic of lots of recent papers. Starting from the very accurate SIFT, most of the approaches exploit the local gradient information that suffers from several drawbacks. First it is unstable in case of severe geometry distortions, second it cannot be easily summarized in a compact way and third it is not designed to account vectorial color information. In this paper, we propose an alternative by designing compact descriptors that account both the colors present in the region and their spatial distribution. Each pixel being characterized by five coordinates, two in the image space and three in the color space, we try to evaluate affine transforms that allow to go from the spatial coordinates to the color coordinates and inversely. Obviously such kind of transform does not exist but we show that after applying it to the original coordinates, the resulted positions are both discriminative and invariant to many acquisition conditions. Hence, depending on the original space (image or color) and the destination space (color or image), we design different complementary descriptors. Their discriminative power and invariance properties are assessed and compared with the best color descriptors in the context of region matching and object classification. [Copyright &y& Elsevier]

Abstract: The problem of image reconstruction from its statistical moments is particularly interesting to researchers in the domain of image processing and pattern recognition. Compared to geometric moments, the orthogonal moments offer the ability to recover much more easily the image due to their orthogonality, which allows reducing greatly the complexity of computation in the phase of reconstruction. Since the 1980s, various orthogonal moments, such as Legendre moments, Zernike moments and discrete Tchebichef moments have been introduced early or late to image reconstruction. In this paper, another set of orthonormal moments, the Gaussian–Hermite moments, based on Hermite polynomials modulated by a Gaussian envelope, is proposed to be used for image reconstruction. Especially, the paper''s focus is on the determination of the optimal scale parameter and the improvement of the reconstruction result by a post-processing which make Gaussian–Hermite moments be useful and comparable with other moments for image reconstruction. The algorithms for computing the values of the basis functions, moment computation and image reconstruction are also given in the paper, as well as a brief discussion on the computational complexity. The experimental results and error analysis by comparison with other moments show a good performance of this new approach. [Copyright &y& Elsevier]

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

Abstract: Human hand shape features extraction from image frame sequences is one of the key steps in human hand 2D/3D tracking system and human hand shape recognition system. In order to satisfy the need of human hand tracking in real time, a fast and accurate method for acquirement of edge features from human hand images without consideration of hand over face is put forward in this paper. The proposed approach is composed of two steps, the coarse location phase (CLP) and the refined location phase (RLP) from coarseness to refinement. In the phase of CLP, the hand contour is approximately described by a polygon with concave and convex, an approach to obtaining hand shape polygon using locating points and locating lines is meticulously discussed. Then, a coarse location (CL) algorithm for extraction of interested hand shape features, such as contour, fingertips, roots of fingers, joints and the intersection of knuckle on different fingers, is proposed. In the phase of RLP, a multi-scale approach is introduced into our study to refine the features obtained by the CL algorithm. By means of defining the response strength of different types of features, a refined location (RL) algorithm is proposed. The major contribution of this paper is that the novel detection operators for features of hand images are presented in the above two steps, which have been successfully applied to our 3D hand shape tracking system and 2D hand shape recognition system. A number of comparative studies with real images and online videos demonstrate that the proposed method can extract the three defined human hand image features with high accuracy and high speed. [ABSTRACT FROM AUTHOR]

Abstract: This paper concerns color image restoration aiming at objective quality improvement of compressed color images in general rather than merely artifact reduction. In compressed color images, colors are usually represented by luminance and chrominance components. Considering characteristics of human vision system, chrominance components are generally represented more coarsely than luminance component. To recover such chrominance components, we previously proposed a model-based chrominance restoration algorithm where color images are modeled by a Markov random field. This paper presents a color image restoration algorithm derived by the MAP estimation, where all components are totally estimated. Experimental results show that the proposed restoration algorithm is more effective than the previous one. [Copyright &y& Elsevier]

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

Abstract: Creating successful machine vision systems often begins a process of developing customised reliable image segmentation algorithms for the detection, and possibly categorisation of regions of interest within images. This can require significant investment of time from both the image processing and the domain experts to set up. Frequently this process is mediated via interviews, or language-based systems which may not fully capture the visual decision-making process of the domain experts. The resulting algorithms can also often be “brittle” in the sense of being highly specialised to the task for which they are tuned, and are consequently sensitive to changes in operating conditions or image specifications. One approach is to use interactive evolution for developing rapidly reconfigurable systems in which the users’ tacit knowledge and requirements can be elicited and used for finding the appropriate parameters to achieve the required segmentation without any need for specialised knowledge of the underlying machine vision systems. This paper presents an interactive tool that can be used to quickly and easily evolve optimal image segmentation parameters from scratch. Building on previous work, the new algorithm reported here incorporates user-guided local search and makes the fitness function more flexible to facilitate the underlying multi-objective decision-making process. One of the key requirements for any interactive system is a high level of usability, both in terms of effectiveness—being able to build accurate models that meet end-user requirements—and efficiency—being able to achieve the required results within a minimal amount of time and undue effort. The system described in this paper has been designed with these considerations in mind to ensure a high level of user-experience of the interaction process. We present results from a series of experiments with a range of users to analyse the effect of the improvements that have been made over the previous system. The efficiency of the tool is also tested with “novice users”, and its usability by “novice users” is analysed. [Copyright &y& Elsevier]

Abstract: The hit-or-miss transform (HMT) is a fundamental operation on binary images, widely used since 40 years. As it is not increasing, its extension to grey-level images is not straightforward, and very few authors have considered it. Moreover, despite its potential usefulness, very few applications of the grey-level HMT have been proposed until now. Part I of this paper [B. Naegel, N. Passat, C. Ronse, Grey-level hit-or-miss transforms—part I: unified theory. Pattern Recogn., in press doi:10.1016/j.patcog.2006.06.004] was devoted to the description of a theory enabling to unify the main definitions of the grey-level HMT, mainly proposed by Ronse and Soille, respectively. Part II of this paper, developed hereafter, deals with the applicative potential of the grey-level HMT, illustrated by its use for vessel segmentation from 3D angiographic data. Different HMT-based segmentation methods are then described and analysed, leading to concrete analysis techniques for brain and liver vessels, but also providing algorithmic strategies which could further be used for many other kinds of image processing applications. [Copyright &y& Elsevier]

Abstract: Zernike moments which are superior to geometric moments because of their special properties of image reconstruction and immunity to noise, suffer from several discretization errors. These errors lead to poor quality of reconstructed image and wide variations in the numerical values of the moments. The predominant factor, as observed in this paper, is due to the discrete integer implementation of the steps involved in moment calculation. It is shown in this paper that by modifying the algorithms to include discrete float implementation, the quality of the reconstructed image improves significantly and the first-order moment becomes zero. Low-order Zernike moments have been found to be stable under linear transformations while the high-order moments have large variations. The large variations in high-order moments, however, do not greatly affect the quality of the reconstructed image, implying that they should be ignored when numerical values of moments are used as features. The 11 functions based on geometric moments have also been found to be stable under linear transformations and thus these can be used as features. Pixel level analysis of the images has been carried out to strengthen the results. [Copyright &y& Elsevier]

Abstract: A new method for detecting and tracking multiple moving objects based on discrete wavelet transform and identifying the moving objects by their color and spatial information is proposed in this paper. Many tracking algorithms have better performance under static background but get worse results under background with fake motions. Therefore, most of the tracking algorithms are used indoors instead of outdoor environment. Since discrete wavelet transform has a nice property that it can divide a frame into four different frequency bands without loss of the spatial information, it is adopted to solve this problem due to the fact that most of the fake motions in the background can be decomposed into the high frequency wavelet sub-band. In tracking multiple moving objects, many applications have problems when objects pass across each other. Color and spatial information are used in this paper to solve this problem. The experimental results prove the feasibility and usefulness of the proposed method. [Copyright &y& Elsevier]

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]

Though most of the faces are axis-symmetrical objects, few real-world face images are axis-symmetrical images. In the past years, there are many studies on face recognition, but only little attention is paid to this issue and few studies to explore and exploit the axis-symmetrical property of faces for face recognition are conducted. In this paper, we take the axis-symmetrical nature of faces into consideration and design a framework to produce approximately axis-symmetrical virtual dictionary for enhancing the accuracy of face recognition. It is noteworthy that the novel algorithm to produce axis-symmetrically virtual face images is mathematically very tractable and quite easy to implement. Extensive experimental results demonstrate the superiority in face recognition of the virtual face images obtained using our method to the original face images. Moreover, experimental results on different databases also show that the proposed method can achieve satisfactory classification accuracy in comparison with state-of-the-art image preprocessing algorithms. The MATLAB code of the proposed method can be available at http://www.yongxu.org/lunwen.html . [ABSTRACT FROM AUTHOR]

Recent advances in image classification mostly rely on the use of powerful local features combined with an adapted image representation. Although Convolutional Neural Network (CNN) features learned from ImageNet were shown to be generic and very efficient, they still lack of flexibility to take into account variations in the spatial layout of visual elements. In this paper, we investigate the use of structural representations on top of pretrained CNN features to improve image classification. Images are represented as strings of CNN features. Similarities between such representations are computed using two new edit distance variants adapted to the image classification domain. Our algorithms have been implemented and tested on several challenging datasets, 15Scenes, Caltech101, Pascal VOC 2007 and MIT indoor. The results show that our idea of using structural string representations and distances clearly improves the classification performance over standard approaches based on CNN and SVM with linear kernel, as well as other recognized methods of the literature. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new algorithm for power line identification and extraction from high resolution remote sensing images. Theoretically, it is difficult to detect power lines in satellite images due to some characteristics, such as sub-pixel, weak target, discrete and the complicated background. To our knowledge, the problem of extraction of the power lines from satellite images is faced for the first time. An improved Radon transform, Cluster Radon Transform (CRT), was developed to extract linear feature from satellite image. Compared with conventional Radon transform, CRT can efficiently avoid false alarm. After that, a set of rules of power lines was abstracted to distinguish power lines from other linear feature, such as roads. The experimental results show that CRT not only has strong anti-noise capability to random noise, but also has strong anti-noise capability to system noise caused by non- linear feature. Furthermore, CRT also has the strong capability to detect short segment in an image. Finally, synthetic images and true images were used to verify the new approach. The achievement has potential to be applicable not only to the power line extraction, but also to other weak linear target detection. [ABSTRACT FROM AUTHOR]

Establishing correct correspondences between two faces with different viewpoints has played an important role in 3D face reconstruction and other computer-vision applications. Usually, face images are considered to lack sufficient distinctive features to establish a large number of correspondences on uncalibrated images. In this paper, we investigate pore-scale facial features, which are formed from pores, fine wrinkles, and hair. These features have many characteristics that make them suitable for matching facial images under different variations. Using both biological observation and computer-vision consideration, a new framework is devised for pore-scale facial-feature extraction and matching. The matching difficulty under various skin appearances of different subjects and imaging distortion is also analyzed. For further improving the matching performance and tackling distortions such as varying illuminations and unfocused blurring, a pore-to-pore correspondences dataset is established for training a more distinctive and compact descriptor. Experiments are conducted on a face database containing 105 subjects, and the results prove that the pore-scale features are highly distinctive; face images with a minimum resolution of 600×700 (0.4 mega) pixels contain sufficient details to perform a reliable matching in different poses. Generally, our algorithm can establish between 500 and 2000 correct correspondences on a pair of uncalibrated face images of the same person. Furthermore, the proposed methods can be applied to face recognition, 3D reconstruction, etc. [ABSTRACT FROM AUTHOR]

Facial wrinkles are important features of aging human skin which can be incorporated in several image-based applications related to aging. Facial wrinkles are 3D features of skin and appear as subtle discontinuities or cracks in surrounding skin texture. However, facial wrinkles can easily be masked by illumination/acquisition conditions in 2D images due to the specific nature of skin surface texture and its reflective properties. Existing approaches to image-based analysis of aging skin are based on the analysis of wrinkles as texture and not as curvilinear discontinuity/crack features. Previously, we proposed a stochastic approach based on Marked Point Processes (MPP) to localize facial wrinkles as curves. In this paper, we present a fast deterministic algorithm based on Gabor filters and image morphology to improve localization results. We propose image features based on Gabor filter bank to highlight the subtle curvilinear discontinuities in skin texture caused by wrinkles. Then, image morphology is used to incorporate geometric constraints to localize curvilinear shapes of wrinkles at image sites of large Gabor filter responses. Experiments are conducted on two sets of low and high resolution images and results are compared with those of MPP modeling. Experiments show that the proposed algorithm not only is significantly faster than MPP-based approach but also provides visually better results. [ABSTRACT FROM AUTHOR]

Abstract: Deformable shape detection is an important problem in computer vision and pattern recognition. However, standard detectors are typically limited to locating only a few salient landmarks such as landmarks near edges or areas of high contrast, often conveying insufficient shape information. This paper presents a novel statistical pattern recognition approach to locate a dense set of salient and non-salient landmarks in images of a deformable object. We explore the fact that several object classes exhibit a homogeneous structure such that each landmark position provides some information about the position of the other landmarks. In our model, the relationship between all pairs of landmarks is naturally encoded as a probabilistic graph. Dense landmark detections are then obtained with a new sampling algorithm that, given a set of candidate detections, selects the most likely positions as to maximize the probability of the graph. Our experimental results demonstrate accurate, dense landmark detections within and across different databases. [Copyright &y& Elsevier]

Abstract: At present, dictionary based models have been widely used in image classification. The image features are approximated as a linear combination of bases selected from the dictionary in a sparse space, resulting in compact patterns. The features applied to image classification usually reside on low dimensional manifolds embedded in a high dimensional ambient space; traditional sparse coding algorithm, however, does not consider this topological structure. It can be characterized naturally by linear coefficients that reconstruct each data point from its neighbors. One of the central issues here is how to determine the neighbors and learn the coefficients. In this paper, the geometrical structures are encoded in two situations. In simple cases when data points distribute on a single manifold, it is explicitly modeled by locally linear embedding algorithm combined with k-nearest neighbors. Nevertheless, in real-world scenarios, complex data points often lie on multiple manifolds. Sparse representation algorithm combined with k-nearest neighbors is instead utilized to construct the topological structures, because it is capable of approximating the data point by selecting its homogenous neighbors adaptively to guarantee the smoothness of each manifold. After obtaining the local fitting relationship, these two topological structures are then embedded into sparse coding algorithm as regularization terms to formulate the corresponding objective functions of dictionary learning on single manifold (DLSM) and dictionary learning on multiple manifolds (DLMM), respectively. Upon this, a coordinate descent scheme is proposed to solve the unified optimization problems. Experimental results on several benchmark data sets, such as Caltech-256, Caltech-101, Scene 15, and UIUC-Sports, show that our proposed algorithms equal or outperform other state-of-the-art image classification algorithms. [Copyright &y& Elsevier]

Abstract: The problem of segmenting a foreground object out from its complex background is of great interest in image processing and computer vision. Many interactive segmentation algorithms such as graph cut have been successfully developed. In this paper, we present four technical components to improve graph cut based algorithms, which are combining both color and texture information for graph cut, including structure tensors in the graph cut model, incorporating active contours into the segmentation process, and using a “softbrush” tool to impose soft constraints to refine problematic boundaries. The integration of these components provides an interactive segmentation method that overcomes the difficulties of previous segmentation algorithms in handling images containing textures or low contrast boundaries and producing a smooth and accurate segmentation boundary. Experiments on various images from the Brodatz, Berkeley and MSRC data sets are conducted and the experimental results demonstrate the high effectiveness of the proposed method to a wide range of images. [Copyright &y& Elsevier]

Abstract: In this paper, we present a framework for object categorization via sketch graphs that incorporate shape and structure information. In this framework, we integrate the learnable And–Or graph model, a hierarchical structure that combines the reconfigurability of a stochastic context free grammar (SCFG) with the constraints of a Markov random field (MRF). Considering the computation efficiency, we generalize instances from the And–Or graph models and perform a set of sequential tests for cascaded object categorization, rather than directly inferring with the And–Or graph models. We study 33 categories, each consisting of a small data set of 30 instances, and 30 additional templates with varied appearance are generalized from the learned And–Or graph model. These samples better span the appearance space and form an augmented training set of 1980 (60×33) training templates. To perform recognition on a testing image, we use a set of sequential tests to project into different representation spaces to narrow the number of candidate matches in . We use “graphlets” (structural elements), as our local features and model at each stage using histograms of graphlets over categories, histograms of graphlets over object instances, histograms of pairs of graphlets over objects, and shape context. Each test is increasingly computationally expensive, and by the end of the cascade we have a small candidate set remaining to use with our most powerful test, a top-down graph matching algorithm. We apply the proposed approach on the challenging public dataset including 33 object categories, and achieve state-of-the-art performance. [Copyright &y& Elsevier]

Abstract: In this paper, we propose a new region-based active contour model (ACM) for image segmentation. In particular, this model utilizes an improved region fitting term to partition the regions of interests in images depending on the local statistics regarding the intensity and the magnitude of gradient in the neighborhood of a contour. By this improved region fitting term, images with noise, intensity non-uniformity, and low-contrast boundaries can be well segmented. Integrated with the duality theory and the anisotropic diffusion process based on structure tensor, a new regularization term is defined through the duality formulation to penalize the length of active contour. By this new regularization term, the structural information of images is utilized to improve the ability of capturing the geometric features such as corners and cusps. From a numerical point of view, we minimize the energy function of our model by an efficient dual algorithm, which avoids the instability and the non-differentiability of traditional numerical solutions, e.g. the gradient descent method. Experiments on medical and natural images demonstrate the advantages of the proposed model over other segmentation models in terms of both efficiency and accuracy. [Copyright &y& Elsevier]

Abstract: Spatial pyramids have been successfully applied to incorporating spatial information into bag-of-words based image representation. However, a major drawback is that it leads to high dimensional image representations. In this paper, we present a novel framework for obtaining compact pyramid representation. First, we investigate the usage of the divisive information theoretic feature clustering (DITC) algorithm in creating a compact pyramid representation. In many cases this method allows us to reduce the size of a high dimensional pyramid representation up to an order of magnitude with little or no loss in accuracy. Furthermore, comparison to clustering based on agglomerative information bottleneck (AIB) shows that our method obtains superior results at significantly lower computational costs. Moreover, we investigate the optimal combination of multiple features in the context of our compact pyramid representation. Finally, experiments show that the method can obtain state-of-the-art results on several challenging data sets. [Copyright &y& Elsevier]

Abstract: In this paper, we introduce a new diffusion algorithm that can be used for reducing aliasing on both step edges and lines. It derives from the diffusion model of Perona and Malik, and works as an adaptive level-curve method in which diffusion is carried out in the normal direction of the gradient for step edges, while the eigenvalues of the Hessian matrix are used for lines. To get sharp images, we use high-pass filters to preserve as much as possible the high frequency content while diffusing. Experimental tests using grayscale and colour images show that our algorithm efficiently reduces aliasing. [Copyright &y& Elsevier]

Abstract: Many applications in computer vision, biomedical informatics, and graphics deal with data in the matrix or tensor form. Non-negative matrix and tensor factorization, which extract data-dependent non-negative basis functions, have been commonly applied for the analysis of such data for data compression, visualization, and detection of hidden information (factors). In this paper, we present a fast and flexible algorithm for sparse non-negative tensor factorization (SNTF) based on columnwise coordinate descent (CCD). Different from the traditional coordinate descent which updates one element at a time, CCD updates one column vector simultaneously. Our empirical results on higher-mode images, such as brain MRI images, gene expression images, and hyperspectral images show that the proposed algorithm is 1–2 orders of magnitude faster than several state-of-the-art algorithms. [Copyright &y& Elsevier]

Abstract: Natural image patches are fundamental elements for visual pattern modeling and recognition. By studying the intrinsic manifold structures in the space of image patches, this paper proposes an approach for representing and recognizing objects with a massive number of local image patches (e.g. 17×17 pixels). Given a large collection () of proto image patches extracted from objects, we map them into two types of manifolds with different metrics: explicit manifolds of low dimensions for structural primitives, and implicit manifolds of high dimensions for stochastic textures. We define these manifolds grown from patches as the “”, where corresponds to the perception residual or fluctuation. Using these as features, we present a novel generative learning algorithm by the information projection principle. This algorithm greedily stepwise pursues the object models by selecting sparse and independent (say for each category). During the detection and classification phase, only a small number (say 20) of features are activated by a fast KD-tree indexing technique. The proposed method owns two characters. (1) Automatically generating features () from local image patches rather than designing marginal feature carefully and category-specifically. (2) Unlike the weak classifiers in the boosting models, these selected features are used to explain object in a generative way and are mutually independent. The advantage and performance of our approach is evaluated on several challenging datasets with the task of localizing objects against appearance variance, occlusion and background clutter. [Copyright &y& Elsevier]

Abstract: Circle detection is fundamental in pattern recognition and computer vision. The randomized approach has received much attention for its computational benefit when compared with the Hough transform. In this paper, a multiple-evidence-based sampling strategy is proposed to speed up the randomized approach. Next, an efficient refinement strategy is proposed to improve the accuracy. Based on different kinds of ten test images, experimental results demonstrate the computation-saving and accuracy effects when plugging the proposed strategies into three existing circle detection methods. [Copyright &y& Elsevier]

Abstract: In this paper we present the Rack algorithm for the detection of optimal non-star-shaped contours in images. It is based on the combination of a user-driven image transformation and dynamic programming. The fundamental idea is to interactively specify and edit the general shape of the desired object by using a rack. This rack is used to model the image as a directed acyclic weighted graph that contains a path corresponding to the expected contour. In this graph, the shortest path with respect to an adequate cost function can be calculated efficiently via dynamic programming. The experimental results indicate the algorithm''s ability of combining an acceptable amount of user interaction with generally good segmentation results. [Copyright &y& Elsevier]

Abstract: In this paper, we propose a fast 3-D facial shape recovery algorithm from a single image with general, unknown lighting. In order to derive the algorithm, we formulate a nonlinear least-square problem with two parameter vectors which are related to personal identity and light conditions. We then combine the spherical harmonics for the surface normals of a human face with tensor algebra and show that in a certain condition, the dimensionality of the least-square problem can be further reduced to one-tenth of the regular subspace-based model by using tensor decomposition (N-mode SVD), which greatly speeds up the computations. In order to enhance the shape recovery performance, we have incorporated prior information in updating the parameters. In the experiment, the proposed algorithm takes less than 0.4s to reconstruct a face and shows a significant performance improvement over other reported schemes. [Copyright &y& Elsevier]

Abstract: This paper presents the MOUGH (mixture of uniform and Gaussian Hough) Transform for shape-based object detection and tracking. We show that the edgels of a rigid object at a given orientation are approximately distributed according to a Gaussian mixture model (GMMs). A variant of the generalized Hough transform is proposed, voting using GMMs and optimized via Expectation-Maximization, that is capable of searching images for a mildly-deformable shape, based on a training dataset of (possibly noisy) images with only crude estimates of scale and centroid of the object in each image. Further modifications are proposed to optimize the algorithm for tracking. The method is able to locate and track objects reliably even against complex backgrounds such as dense moving foliage, and with a moving camera. Experimental results indicate that the algorithm is superior to previously published variants of the Hough transform and to active shape models in tracking pedestrians from a side view. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a novel histogram thresholding – fuzzy C-means hybrid (HTFCM) approach that could find different application in pattern recognition as well as in computer vision, particularly in color image segmentation. The proposed approach applies the histogram thresholding technique to obtain all possible uniform regions in the color image. Then, the Fuzzy C-means (FCM) algorithm is utilized to improve the compactness of the clusters forming these uniform regions. Experimental results have demonstrated that the low complexity of the proposed HTFCM approach could obtain better cluster quality and segmentation results than other segmentation approaches that employing ant colony algorithm. [Copyright &y& Elsevier]

Abstract: Two novel real-time local visual features, namely FAST+LBP and FAST+CSLBP, are proposed in this paper for omnidirectional vision. They combine the advantages of two computationally simple operators by using FAST as the feature detector, and LBP and CS-LBP operators as feature descriptors. The matching experiments of the panoramic images from the COLD database were performed to determine their optimal parameters, and to evaluate and compare their performance with SIFT. The experimental results show that our algorithms perform better, and features can be extracted in real-time. Therefore, our local visual features can be applied to those computer/robot vision tasks with high real-time requirements. [Copyright &y& Elsevier]

Abstract: Motion analysis of complex signals is a particularly important and difficult topic, as classical Computer Vision and Image Processing methodologies, either based on some extended conservation hypothesis or regularity conditions, may show their inherent limitations. An important example of such signals are those coming from the remote sensing of the oceans. In those signals, the inherent complexities of the acquired phenomenon (a fluid in the regime of fully developed turbulence—FDT) are made even more fraught through the alterations coming from the acquisition process (sun glint, haze, missing data etc.). The importance of understanding and computing vector fields associated to motion in the oceans or in the atmosphere (e.g.: cloud motion) raises some fundamental questions and the need for derivating motion analysis and understanding algorithms that match the physical characteristics of the acquired signals. Among these questions, one of the most fundamental is to understand what classical methodologies (e.g.: such as the various implementations of the optical flow) are missing, and how their drawbacks can be mitigated. In this paper, we show that the fundamental problem of motion evaluation in complex and turbulent acquisitions can be tackled using new multiscale characterizations of transition fronts. The use of appropriate paradigms coming from Statistical Physics can be combined with some specific Signal Processing evaluation of the microcanonical cascade associated to turbulence. This leads to radically new methods for computing motion fields in these signals. These methods are first assessed on the results of a 3D oceanic circulation model, and then applied on real data. [Copyright &y& Elsevier]

Abstract: This paper is concerned with the introduction of a new gradient vector flow (GVF) field formulation that shows increased robustness in the presence of mixed noise and with its evaluation when included in the development of image enhancement algorithms. In this regard, the main contribution associated with this work resides in the development of an adaptive image enhancement framework that couples the anisotropic diffusion models with the adaptive median filtering that is designed for the restoration of digital images corrupted with mixed noise. To further illustrate the advantages associated with the proposed GVF field formulation, additional experiments are conducted when the proposed strategy is applied in the construction of anisotropic models for texture enhancement. [Copyright &y& Elsevier]

Abstract: As the development of the technology for radar target recognition, missile target automatic recognition has received considerable attention in recent years. Missile target, compared with the plane target, is hard to recognize for its smallness, feebleness and maneuver. In this paper, a new recognition method based on radar image time-series, which can significantly reduce the recognition time and classification error, is proposed. The image time-series are produced by range instantaneous Doppler imaging algorithm firstly, and then cross-range scaling of the images is processed. In particular, the inertia ratio, extracted from the obtained image time-series, is introduced to distinguish the missile from decoys. Furthermore, the effectiveness of this method is demonstrated by application to simulated data and it has been shown that this method has the potential to be used in a number of real-time applications. [Copyright &y& Elsevier]

Abstract: This paper proposes a new method for multi-focus image fusion based on dual-channel pulse coupled neural networks (dual-channel PCNN). Compared with previous methods, our method does not decompose the input source images and need not employ more PCNNs or other algorithms such as DWT. This method employs the dual-channel PCNN to implement multi-focus image fusion. Two parallel source images are directly input into PCNN. Meanwhile focus measure is carried out for source images. According to results of focus measure, weighted coefficients are automatically adjusted. The rule of auto-adjusting depends on the specific transformation. Input images are combined in the dual-channel PCNN. Four group experiments are designed to testify the performance of the proposed method. Several existing methods are compared with our method. Experimental results show our presented method outperforms existing methods, in both visual effect and objective evaluation criteria. Finally, some practical applications are given further. [Copyright &y& Elsevier]

Abstract: The relative position between two 2-D spatial regions is often represented quantitatively by a force histogram. In the case of raster data, force histograms are usually computed in time, where N is the number of pixels in the image and K is the number of directions in which forces are considered. When the regions are defined as fuzzy sets instead of crisp sets, the complexity also depends on the number M of possible membership degrees. In this paper, we show that the force histogram can be defined in a completely different but equivalent way, one which leads to an algorithm, with complexity independent of K and M. Moreover, the equivalent definition is better adapted to the solving of theoretical issues. We use it here to determine the behavior of the force histogram towards any invertible affine transformation. [Copyright &y& Elsevier]

Abstract: This paper develops new geometrical filtering and edge detection algorithms for processing non-Euclidean image data. We view image data as residing on a Riemannian manifold, and we work with a representation based on the exponential map for this manifold together with the Riemannian weighted mean of image data. We show how the weighted mean can be efficiently computed using Newton''s method, which converges faster than the gradient descent method described elsewhere in the literature. Based on geodesic distances and the exponential map, we extend the classical median filter and the Perona–Malik anisotropic diffusion technique to smooth non-Euclidean image data. We then propose an anisotropic Gaussian kernel for image filtering, and we also show how both the median filter and the anisotropic Gaussian filter can be combined to develop a new edge preserving filter, which is effective at removing both Gaussian noise and impulse noise. By using the intrinsic metric of the feature manifold, we also generalise Di Zenzo''s structure tensor to non-Euclidean images for edge detection. We demonstrate the applications of our Riemannian filtering and edge detection algorithms both on directional and tensor-valued images. [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: In this paper, a genetic clustering algorithm based on dynamic niching with niche migration (DNNM-clustering) is proposed. It is an effective and robust approach to clustering on the basis of a similarity function relating to the approximate density shape estimation. In the new algorithm, a dynamic identification of the niches with niche migration is performed at each generation to automatically evolve the optimal number of clusters as well as the cluster centers of the data set without invoking cluster validity functions. The niches can move slowly under the migration operator which makes the dynamic niching method independent of the radius of the niches. Compared to other existing methods, the proposed clustering method exhibits the following robust characteristics: (1) robust to the initialization, (2) robust to clusters volumes (ability to detect different volumes of clusters), and (3) robust to noise. Moreover, it is free of the radius of the niches and does not need to pre-specify the number of clusters. Several data sets with widely varying characteristics are used to demonstrate its superiority. An application of the DNNM-clustering algorithm in unsupervised classification of the multispectral remote sensing image is also provided. [Copyright &y& Elsevier]

Abstract: In this paper we address the problem of traffic sign recognition. Novel image representation and discriminative feature selection algorithms are utilised in a traditional three-stage framework involving detection, tracking and recognition. The detector captures instances of equiangular polygons in the scene which is first appropriately filtered to extract the relevant colour information and establish the regions of interest. The tracker predicts the position and the scale of the detected sign candidate over time to reduce computation. The classifier compares a discrete-colour image of the observed sign with the model images with respect to the class-specific sets of discriminative local regions. They are learned off-line from the idealised template sign images, in accordance with the principle of one-vs-all dissimilarity maximisation. This dissimilarity is defined based on the so-called Colour Distance Transform which enables robust discrete-colour image comparisons. It is shown that compared to the well-established feature selection techniques, such as Principal Component Analysis or AdaBoost, our approach offers a more adequate description of signs and involves effortless training. Upon this description we have managed to build an efficient road sign recognition system which, based on a conventional nearest neighbour classifier and a simple temporal integration scheme, demonstrates a competitive performance in the experiments involving real traffic video. [Copyright &y& Elsevier]

Abstract: Based on a classical convex hull algorithm called gift-wrapping, the purpose of the paper is to provide a new algorithm for computing the vertices of a polytope called preimage—roughly the set of naive digital planes containing a finite subset of . The vertices of the upper hemisphere, the ones of the lower hemisphere and at last the equatorial vertices are computed independently. The principle of the algorithm is based on duality and especially on the fact that the vertices of the preimage correspond to faces of the input set or of its chords set . It allows to go from one vertex to another by gift-wrapping until the whole region of interest has been explored. [Copyright &y& Elsevier]

Abstract: This paper presents a new image denoising algorithm based on the modeling of coefficients in each subband of steerable pyramid employing a Laplacian probability density function (pdf) with local variance. This pdf is able to model the heavy-tailed nature of steerable pyramid coefficients and the empirically observed correlation between the coefficient amplitudes. Within this framework, we describe a novel method for image denoising based on designing both maximum a posteriori (MAP) and minimum mean squared error (MMSE) estimators, which relies on the zero-mean Laplacian random variables with high local correlation. Despite the simplicity of our spatially adaptive denoising method, both in its concern and implementation, our denoising results achieves better performance than several published methods such as Bayes least squared Gaussian scale mixture (BLS-GSM) technique that is a state-of-the-art denoising technique. [Copyright &y& Elsevier]

Abstract: In line generalization, results depend very much on the characteristics of the line in question. For this reason it would be useful to obtain an automatic segmentation and enrichment of lines in order to apply to each section the best algorithm and the most appropriate parameter. In this paper, we present a line segmentation methodology based on a sinuosity pattern recognition measured by means of the effective-area as derived from the Visvalingam–Whyatt algorithm. Sections are determined by applying the Douglas–Peucker algorithm to a shape signature of the line: an effective-area/length space representation. An experiment is carried out with a set of 24 road features from a 1:25000 scale map with a recommendation of the value of some parameters and a procedure for the automated search of that defined as natural number of sections. This procedure is based in the search of zones of stability in a graph of the number of sections when applying Douglas–Peucker to the shape signature. The results are positively assessed by an independent group of experts. [Copyright &y& Elsevier]

Abstract: The incorporation of spatial context into clustering algorithms for image segmentation has recently received a significant amount of attention. Many modified clustering algorithms have been proposed and proven to be effective for image segmentation. In this paper, we propose a different framework for incorporating spatial information with the aim of achieving robust and accurate segmentation in case of mixed noise without using experimentally set parameters based on the original robust information clustering (RIC) algorithm, called adaptive spatial information-theoretic clustering (ASIC) algorithm. The proposed objective function has a new dissimilarity measure, and the weighting factor for neighborhood effect is fully adaptive to the image content. It enhances the smoothness towards piecewise-homogeneous segmentation and reduces the edge blurring effect. Furthermore, a unique characteristic of the new information segmentation algorithm is that it has the capabilities to eliminate outliers at different stages of the ASIC algorithm. These result in improved segmentation result by identifying and relabeling the outliers in a relatively stronger noisy environment. Comprehensive experiments and a new information-theoretic proof are carried out to illustrate that our new algorithm can consistently improve the segmentation result while effectively handles the edge blurring effect. The experimental results with both synthetic and real images demonstrate that the proposed method is effective and robust to mixed noise and the algorithm outperforms other popular spatial clustering variants. [Copyright &y& Elsevier]

Abstract: In this paper we introduce an adaptive image thresholding technique via minimax optimization of a novel energy functional that consists of a non-linear convex combination of an edge sensitive data fidelity term and a regularization term. While the proposed data fidelity term requires the threshold surface to intersect the image surface only at places with large image gradient magnitude, the regularization term enforces smoothness in the threshold surface. To the best of our knowledge, all the previously proposed energy functional-based adaptive image thresholding algorithms rely on manually set weighting parameters to achieve a balance between the data fidelity and the regularization terms. In contrast, we use minimax principle to automatically find this weighting parameter value, as well as the threshold surface. Our conscious choice of the energy functional permits a variational formulation within the minimax principle leading to a globally optimum solution. The proposed variational minimax optimization is carried out by an iterative gradient descent with exact line search technique that we experimentally demonstrate to be computationally far more attractive than the Fibonacci search applied to find the minimax solution. Our method shows promising results to preserve edge/texture structures in different benchmark images over other competing methods. We also demonstrate the efficacy of the proposed method for delineating lung boundaries from magnetic resonance imagery (MRI). [Copyright &y& Elsevier]

Abstract: A novel algorithm that permits the fast and accurate computation of geometric moments on gray-scale images is presented in this paper. The proposed algorithm constitutes an extension of the IBR algorithm, introduced in the past, which was applicable only for binary images. A new image representation scheme, the ISR (intensity slice representation), which represents a gray-scale image as an expansion of several two-level images of different intensity values, enables the partially application of the IBR algorithm to each image component. Moreover, using the resulted set of image blocks, the geometric moments’ computation can be accelerated through appropriate computation schemes. [Copyright &y& Elsevier]

Abstract: This paper addresses the problems of depth recovery and affine reconstruction from two perspective images, which are generated by an uncalibrated translating camera. Firstly, we develop a new constraint that the homography for the plane, which is orthogonal to the optical axis, is determined only by the epipole and the plane''s relative distance to the origin under camera pure translation. The algorithm of depth recovery is based on this new constraint, and it can successfully avoid the step of camera calibration. With the recovered depth, we show that affine reconstruction can be obtained readily. The proposed affine reconstruction does not need any control points, which were used to expand the affine coordinate system in existing method. Therefore, it could avoid the step of non-planarity verification as well as the errors from the control points. Error analysis is also presented to evaluate the uncertainty for the recovered depth value. Finally, we have tested the proposed algorithm with both simulated data and real image data. And the results show that the proposed algorithm is accurate and practical. [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]

Abstract: This paper proposes an active contour-based active appearance model (AAM) that is robust to a cluttered background and a large motion. The proposed AAM fitting algorithm consists of two alternating procedures: active contour fitting to find the contour sample that best fits the face image and then the active appearance model fitting over the best selected contour. We also suggest an effective fitness function for fitting the contour samples to the face boundary in the active contour technique; this function defines the quality of fitness in terms of the strength and/or the length of edge features. Experimental results show that the proposed active contour-based AAM provides better accuracy and convergence characteristics in terms of RMS error and convergence rate than the existing robust AAM. The combination of the existing robust AAM and the proposed active contour-based AAM (AC-R-AAM) had the best accuracy and convergence performances. [Copyright &y& Elsevier]