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Image Processing, IEEE Transactions on

Issue 5 • Date May 2013

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Displaying Results 1 - 25 of 46
  • Front Cover

    Page(s): C1
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  • IEEE Transactions on Image Processing publication information

    Page(s): C2
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  • Table of contents

    Page(s): 1681 - 1683
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  • Table of contents

    Page(s): 1685 - 1687
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    Page(s): 1688
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  • Bayesian Saliency via Low and Mid Level Cues

    Page(s): 1689 - 1698
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3071 KB) |  | HTML iconHTML  

    Visual saliency detection is a challenging problem in computer vision, but one of great importance and numerous applications. In this paper, we propose a novel model for bottom-up saliency within the Bayesian framework by exploiting low and mid level cues. In contrast to most existing methods that operate directly on low level cues, we propose an algorithm in which a coarse saliency region is first obtained via a convex hull of interest points. We also analyze the saliency information with mid level visual cues via superpixels. We present a Laplacian sparse subspace clustering method to group superpixels with local features, and analyze the results with respect to the coarse saliency region to compute the prior saliency map. We use the low level visual cues based on the convex hull to compute the observation likelihood, thereby facilitating inference of Bayesian saliency at each pixel. Extensive experiments on a large data set show that our Bayesian saliency model performs favorably against the state-of-the-art algorithms. View full abstract»

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  • Exemplar-Based Image Inpainting Using Multiscale Graph Cuts

    Page(s): 1699 - 1711
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1394 KB) |  | HTML iconHTML  

    We present a novel formulation of exemplar-based inpainting as a global energy optimization problem, written in terms of the offset map. The proposed energy function combines a data attachment term that ensures the continuity of reconstruction at the boundary of the inpainting domain with a smoothness term that ensures a visually coherent reconstruction inside the hole. This formulation is adapted to obtain a global minimum using the graph cuts algorithm. To reduce the computational complexity, we propose an efficient multiscale graph cuts algorithm. To compensate the loss of information at low resolution levels, we use a feature representation computed at the original image resolution. This permits alleviation of the ambiguity induced by comparing only color information when the image is represented at low resolution levels. Our experiments show how well the proposed algorithm performs compared with other recent algorithms. View full abstract»

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  • Activity Recognition Using a Mixture of Vector Fields

    Page(s): 1712 - 1725
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (9053 KB) |  | HTML iconHTML  

    The analysis of moving objects in image sequences (video) has been one of the major themes in computer vision. In this paper, we focus on video-surveillance tasks; more specifically, we consider pedestrian trajectories and propose modeling them through a small set of motion/vector fields together with a space-varying switching mechanism. Despite the diversity of motion patterns that can occur in a given scene, we show that it is often possible to find a relatively small number of typical behaviors, and model each of these behaviors by a “simple” motion field. We increase the expressiveness of the formulation by allowing the trajectories to switch from one motion field to another, in a space-dependent manner. We present an expectation-maximization algorithm to learn all the parameters of the model, and apply it to trajectory classification tasks. Experiments with both synthetic and real data support the claims about the performance of the proposed approach. View full abstract»

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  • Low-Resolution Face Tracker Robust to Illumination Variations

    Page(s): 1726 - 1739
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3473 KB) |  | HTML iconHTML  

    In many practical video surveillance applications, the faces acquired by outdoor cameras are of low resolution and are affected by uncontrolled illumination. Although significant efforts have been made to facilitate face tracking or illumination normalization in unconstrained videos, the approaches developed may not be effective in video surveillance applications. This is because: 1) a low-resolution face contains limited information, and 2) major changes in illumination on a small region of the face make the tracking ineffective. To overcome this problem, this paper proposes to perform tracking in an illumination-insensitive feature space, called the gradient logarithm field (GLF) feature space. The GLF feature mainly depends on the intrinsic characteristics of a face and is only marginally affected by the lighting source. In addition, the GLF feature is a global feature and does not depend on a specific face model, and thus is effective in tracking low-resolution faces. Experimental results show that the proposed GLF-based tracker works well under significant illumination changes and outperforms many state-of-the-art tracking algorithms. View full abstract»

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  • Local Directional Number Pattern for Face Analysis: Face and Expression Recognition

    Page(s): 1740 - 1752
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2541 KB) |  | HTML iconHTML  

    This paper proposes a novel local feature descriptor, local directional number pattern (LDN), for face analysis, i.e., face and expression recognition. LDN encodes the directional information of the face's textures (i.e., the texture's structure) in a compact way, producing a more discriminative code than current methods. We compute the structure of each micro-pattern with the aid of a compass mask that extracts directional information, and we encode such information using the prominent direction indices (directional numbers) and sign-which allows us to distinguish among similar structural patterns that have different intensity transitions. We divide the face into several regions, and extract the distribution of the LDN features from them. Then, we concatenate these features into a feature vector, and we use it as a face descriptor. We perform several experiments in which our descriptor performs consistently under illumination, noise, expression, and time lapse variations. Moreover, we test our descriptor with different masks to analyze its performance in different face analysis tasks. View full abstract»

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  • Regularized Robust Coding for Face Recognition

    Page(s): 1753 - 1766
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (879 KB) |  | HTML iconHTML  

    Recently the sparse representation based classification (SRC) has been proposed for robust face recognition (FR). In SRC, the testing image is coded as a sparse linear combination of the training samples, and the representation fidelity is measured by the l2-norm or l1 -norm of the coding residual. Such a sparse coding model assumes that the coding residual follows Gaussian or Laplacian distribution, which may not be effective enough to describe the coding residual in practical FR systems. Meanwhile, the sparsity constraint on the coding coefficients makes the computational cost of SRC very high. In this paper, we propose a new face coding model, namely regularized robust coding (RRC), which could robustly regress a given signal with regularized regression coefficients. By assuming that the coding residual and the coding coefficient are respectively independent and identically distributed, the RRC seeks for a maximum a posterior solution of the coding problem. An iteratively reweighted regularized robust coding (IR3C) algorithm is proposed to solve the RRC model efficiently. Extensive experiments on representative face databases demonstrate that the RRC is much more effective and efficient than state-of-the-art sparse representation based methods in dealing with face occlusion, corruption, lighting, and expression changes, etc. View full abstract»

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  • Exploration of Optimal Many-Core Models for Efficient Image Segmentation

    Page(s): 1767 - 1777
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1254 KB) |  | HTML iconHTML  

    Image segmentation plays a crucial role in numerous biomedical imaging applications, assisting clinicians or health care professionals with diagnosis of various diseases using scientific data. However, its high computational complexities require substantial amount of time and have limited their applicability. Research has thus focused on parallel processing models that support biomedical image segmentation. In this paper, we present analytical results of the design space exploration of many-core processors for efficient fuzzy c-means (FCM) clustering, which is widely used in many medical image segmentations. We quantitatively evaluate the impact of varying a number of processing elements (PEs) and an amount of local memory for a fixed image size on system performance and efficiency using architectural and workload simulations. Experimental results indicate that PEs=4,096 provides the most efficient operation for the FCM algorithm with four clusters, while PEs=1,024 and PEs=4,096 yield the highest area efficiency and energy efficiency, respectively, for three clusters. View full abstract»

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  • Active Contour-Based Visual Tracking by Integrating Colors, Shapes, and Motions

    Page(s): 1778 - 1792
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1112 KB) |  | HTML iconHTML  

    In this paper, we present a framework for active contour-based visual tracking using level sets. The main components of our framework include contour-based tracking initialization, color-based contour evolution, adaptive shape-based contour evolution for non-periodic motions, dynamic shape-based contour evolution for periodic motions, and the handling of abrupt motions. For the initialization of contour-based tracking, we develop an optical flow-based algorithm for automatically initializing contours at the first frame. For the color-based contour evolution, Markov random field theory is used to measure correlations between values of neighboring pixels for posterior probability estimation. For adaptive shape-based contour evolution, the global shape information and the local color information are combined to hierarchically evolve the contour, and a flexible shape updating model is constructed. For the dynamic shape-based contour evolution, a shape mode transition matrix is learnt to characterize the temporal correlations of object shapes. For the handling of abrupt motions, particle swarm optimization is adopted to capture the global motion which is applied to the contour in the current frame to produce an initial contour in the next frame. View full abstract»

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  • Image Quality Assessment Using Multi-Method Fusion

    Page(s): 1793 - 1807
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (742 KB) |  | HTML iconHTML  

    A new methodology for objective image quality assessment (IQA) with multi-method fusion (MMF) is presented in this paper. The research is motivated by the observation that there is no single method that can give the best performance in all situations. To achieve MMF, we adopt a regression approach. The new MMF score is set to be the nonlinear combination of scores from multiple methods with suitable weights obtained by a training process. In order to improve the regression results further, we divide distorted images into three to five groups based on the distortion types and perform regression within each group, which is called “context-dependent MMF” (CD-MMF). One task in CD-MMF is to determine the context automatically, which is achieved by a machine learning approach. To further reduce the complexity of MMF, we perform algorithms to select a small subset from the candidate method set. The result is very good even if only three quality assessment methods are included in the fusion process. The proposed MMF method using support vector regression is shown to outperform a large number of existing IQA methods by a significant margin when being tested in six representative databases. View full abstract»

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  • Robust Radial Face Detection for Omnidirectional Vision

    Page(s): 1808 - 1821
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1617 KB) |  | HTML iconHTML  

    Bio-inspired and non-conventional vision systems are highly researched topics. Among them, omnidirectional vision systems have demonstrated their ability to significantly improve the geometrical interpretation of scenes. However, few researchers have investigated how to perform object detection with such systems. The existing approaches require a geometrical transformation prior to the interpretation of the picture. In this paper, we investigate what must be taken into account and how to process omnidirectional images provided by the sensor. We focus our research on face detection and highlight the fact that particular attention should be paid to the descriptors in order to successfully perform face detection on omnidirectional images. We demonstrate that this choice is critical to obtaining high detection rates. Our results imply that the adaptation of existing object-detection frameworks, designed for perspective images, should be focused on the choice of appropriate image descriptors in the design of the object-detection pipeline. View full abstract»

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  • Optimized 3D Watermarking for Minimal Surface Distortion

    Page(s): 1822 - 1835
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1585 KB) |  | HTML iconHTML  

    This paper proposes a new approach to 3D watermarking by ensuring the optimal preservation of mesh surfaces. A new 3D surface preservation function metric is defined consisting of the distance of a vertex displaced by watermarking to the original surface, to the watermarked object surface as well as the actual vertex displacement. The proposed method is statistical, blind, and robust. Minimal surface distortion according to the proposed function metric is enforced during the statistical watermark embedding stage using Levenberg-Marquardt optimization method. A study of the watermark code crypto-security is provided for the proposed methodology. According to the experimental results, the proposed methodology has high robustness against the common mesh attacks while preserving the original object surface during watermarking. View full abstract»

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  • Approximate Least Trimmed Sum of Squares Fitting and Applications in Image Analysis

    Page(s): 1836 - 1847
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (697 KB) |  | HTML iconHTML  

    The least trimmed sum of squares (LTS) regression estimation criterion is a robust statistical method for model fitting in the presence of outliers. Compared with the classical least squares estimator, which uses the entire data set for regression and is consequently sensitive to outliers, LTS identifies the outliers and fits to the remaining data points for improved accuracy. Exactly solving an LTS problem is NP-hard, but as we show here, LTS can be formulated as a concave minimization problem. Since it is usually tractable to globally solve a convex minimization or concave maximization problem in polynomial time, inspired by , we instead solve LTS' approximate complementary problem, which is convex minimization. We show that this complementary problem can be efficiently solved as a second order cone program. We thus propose an iterative procedure to approximately solve the original LTS problem. Our extensive experiments demonstrate that the proposed method is robust, efficient and scalable in dealing with problems where data are contaminated with outliers. We show several applications of our method in image analysis. View full abstract»

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  • Design of Low-Complexity High-Performance Wavelet Filters for Image Analysis

    Page(s): 1848 - 1858
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3998 KB) |  | HTML iconHTML  

    This paper addresses the construction of a family of wavelets based on halfband polynomials. An algorithm is proposed that ensures maximum zeros at for a desired length of analysis and synthesis filters. We start with the coefficients of the polynomial and then use a generalized matrix formulation method to construct the filter halfband polynomial. The designed wavelets are efficient and give acceptable levels of peak signal-to-noise ratio when used for image compression. Furthermore, these wavelets give satisfactory recognition rates when used for feature extraction. Simulation results show that the designed wavelets are effective and more efficient than the existing standard wavelets. View full abstract»

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  • Noise Reduction Based on Partial-Reference, Dual-Tree Complex Wavelet Transform Shrinkage

    Page(s): 1859 - 1872
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    This paper presents a novel way to reduce noise introduced or exacerbated by image enhancement methods, in particular algorithms based on the random spray sampling technique, but not only. According to the nature of sprays, output images of spray-based methods tend to exhibit noise with unknown statistical distribution. To avoid inappropriate assumptions on the statistical characteristics of noise, a different one is made. In fact, the non-enhanced image is considered to be either free of noise or affected by non-perceivable levels of noise. Taking advantage of the higher sensitivity of the human visual system to changes in brightness, the analysis can be limited to the luma channel of both the non-enhanced and enhanced image. Also, given the importance of directional content in human vision, the analysis is performed through the dual-tree complex wavelet transform (DTWCT). Unlike the discrete wavelet transform, the DTWCT allows for distinction of data directionality in the transform space. For each level of the transform, the standard deviation of the non-enhanced image coefficients is computed across the six orientations of the DTWCT, then it is normalized. The result is a map of the directional structures present in the non-enhanced image. Said map is then used to shrink the coefficients of the enhanced image. The shrunk coefficients and the coefficients from the non-enhanced image are then mixed according to data directionality. Finally, a noise-reduced version of the enhanced image is computed via the inverse transforms. A thorough numerical analysis of the results has been performed in order to confirm the validity of the proposed approach. View full abstract»

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  • Hessian Schatten-Norm Regularization for Linear Inverse Problems

    Page(s): 1873 - 1888
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2172 KB) |  | HTML iconHTML  

    We introduce a novel family of invariant, convex, and non-quadratic functionals that we employ to derive regularized solutions of ill-posed linear inverse imaging problems. The proposed regularizers involve the Schatten norms of the Hessian matrix, which are computed at every pixel of the image. They can be viewed as second-order extensions of the popular total-variation (TV) semi-norm since they satisfy the same invariance properties. Meanwhile, by taking advantage of second-order derivatives, they avoid the staircase effect, a common artifact of TV-based reconstructions, and perform well for a wide range of applications. To solve the corresponding optimization problems, we propose an algorithm that is based on a primal-dual formulation. A fundamental ingredient of this algorithm is the projection of matrices onto Schatten norm balls of arbitrary radius. This operation is performed efficiently based on a direct link we provide between vector projections onto norm balls and matrix projections onto Schatten norm balls. Finally, we demonstrate the effectiveness of the proposed methods through experimental results on several inverse imaging problems with real and simulated data. View full abstract»

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  • Structured Sparse Error Coding for Face Recognition With Occlusion

    Page(s): 1889 - 1900
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1110 KB) |  | HTML iconHTML  

    Face recognition with occlusion is common in the real world. Inspired by the works of structured sparse representation, we try to explore the structure of the error incurred by occlusion from two aspects: the error morphology and the error distribution. Since human beings recognize the occlusion mainly according to its region shape or profile without knowing accurately what the occlusion is, we argue that the shape of the occlusion is also an important feature. We propose a morphological graph model to describe the morphological structure of the error. Due to the uncertainty of the occlusion, the distribution of the error incurred by occlusion is also uncertain. However, we observe that the unoccluded part and the occluded part of the error measured by the correntropy induced metric follow the exponential distribution, respectively. Incorporating the two aspects of the error structure, we propose the structured sparse error coding for face recognition with occlusion. Our extensive experiments demonstrate that the proposed method is more stable and has higher breakdown point in dealing with the occlusion problems in face recognition as compared to the related state-of-the-art methods, especially for the extreme situation, such as the high level occlusion and the low feature dimension. View full abstract»

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  • Accurate Multiple View 3D Reconstruction Using Patch-Based Stereo for Large-Scale Scenes

    Page(s): 1901 - 1914
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2083 KB) |  | HTML iconHTML  

    In this paper, we propose a depth-map merging based multiple view stereo method for large-scale scenes which takes both accuracy and efficiency into account. In the proposed method, an efficient patch-based stereo matching process is used to generate depth-map at each image with acceptable errors, followed by a depth-map refinement process to enforce consistency over neighboring views. Compared to state-of-the-art methods, the proposed method can reconstruct quite accurate and dense point clouds with high computational efficiency. Besides, the proposed method could be easily parallelized at image level, i.e., each depth-map is computed individually, which makes it suitable for large-scale scene reconstruction with high resolution images. The accuracy and efficiency of the proposed method are evaluated quantitatively on benchmark data and qualitatively on large data sets. View full abstract»

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  • Mixed-Domain Edge-Aware Image Manipulation

    Page(s): 1915 - 1925
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    This paper presents a novel approach to edge-aware image manipulation. Our method processes a Gaussian pyramid from coarse to fine, and at each level, applies a nonlinear filter bank to the neighborhood of each pixel. Outputs of these spatially-varying filters are merged using global optimization. The optimization problem is solved using an explicit mixed-domain (real space and DCT transform space) solution, which is efficient, accurate, and easy-to-implement. We demonstrate applications of our method to a set of problems, including detail and contrast manipulation, HDR compression, nonphotorealistic rendering, and haze removal. View full abstract»

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  • Monocular Depth Ordering Using T-Junctions and Convexity Occlusion Cues

    Page(s): 1926 - 1939
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2706 KB) |  | HTML iconHTML  

    This paper proposes a system that relates objects in an image using occlusion cues and arranges them according to depth. The system does not rely on a priori knowledge of the scene structure and focuses on detecting special points, such as T-junctions and highly convex contours, to infer the depth relationships between objects in the scene. The system makes extensive use of the binary partition tree as hierarchical region-based image representation jointly with a new approach for candidate T-junction estimation. Since some regions may not involve T-junctions, occlusion is also detected by examining convex shapes on region boundaries. Combining T-junctions and convexity leads to a system which only relies on low level depth cues and does not rely on semantic information. However, it shows a similar or better performance with the state-of-the-art while not assuming any type of scene. As an extension of the automatic depth ordering system, a semi-automatic approach is also proposed. If the user provides the depth order for a subset of regions in the image, the system is able to easily integrate this user information to the final depth order for the complete image. For some applications, user interaction can naturally be integrated, improving the quality of the automatically generated depth map. View full abstract»

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Aims & Scope

IEEE Transactions on Image Processing focuses on signal-processing aspects of image processing, imaging systems, and image scanning, display, and printing.

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Meet Our Editors

Editor-in-Chief
Scott Acton
University of Virginia
Charlottesville, VA, USA
E-mail: acton@virginia.edu 
Phone: +1 434-982-2003