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Image Processing, IEEE Transactions on

Issue 9 • Date Sept. 2012

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Displaying Results 1 - 25 of 48
  • Table of contents

    Page(s): C1 - 3826
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  • IEEE Transactions on Image Processing publication information

    Page(s): C2
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  • Adaptive Membership Functions for Handwritten Character Recognition by Voronoi-Based Image Zoning

    Page(s): 3827 - 3837
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (471 KB) |  | HTML iconHTML  

    In the field of handwritten character recognition, image zoning is a widespread technique for feature extraction since it is rightly considered to be able to cope with handwritten pattern variability. As a matter of fact, the problem of zoning design has attracted many researchers who have proposed several image-zoning topologies, according to static and dynamic strategies. Unfortunately, little attention has been paid so far to the role of feature-zone membership functions that define the way in which a feature influences different zones of the zoning method. The result is that the membership functions defined to date follow nonadaptive, global approaches that are unable to model local information on feature distributions. In this paper, a new class of zone-based membership functions with adaptive capabilities is introduced and its effectiveness is shown. The basic idea is to select, for each zone of the zoning method, the membership function best suited to exploit the characteristics of the feature distribution of that zone. In addition, a genetic algorithm is proposed to determine—in a unique process—the most favorable membership functions along with the optimal zoning topology, described by Voronoi tessellation. The experimental tests show the superiority of the new technique with respect to traditional zoning methods. View full abstract»

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  • Semi-Blind Sparse Image Reconstruction With Application to MRFM

    Page(s): 3838 - 3849
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (947 KB) |  | HTML iconHTML  

    We propose a solution to the image deconvolution problem where the convolution kernel or point spread function (PSF) is assumed to be only partially known. Small perturbations generated from the model are exploited to produce a few principal components explaining the PSF uncertainty in a high-dimensional space. Unlike recent developments on blind deconvolution of natural images, we assume the image is sparse in the pixel basis, a natural sparsity arising in magnetic resonance force microscopy (MRFM). Our approach adopts a Bayesian Metropolis-within-Gibbs sampling framework. The performance of our Bayesian semi-blind algorithm for sparse images is superior to previously proposed semi-blind algorithms such as the alternating minimization algorithm and blind algorithms developed for natural images. We illustrate our myopic algorithm on real MRFM tobacco virus data. View full abstract»

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  • Universal Regularizers for Robust Sparse Coding and Modeling

    Page(s): 3850 - 3864
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4507 KB) |  | HTML iconHTML  

    Sparse data models, where data is assumed to be well represented as a linear combination of a few elements from a dictionary, have gained considerable attention in recent years, and their use has led to state-of-the-art results in many signal and image processing tasks. It is now well understood that the choice of the sparsity regularization term is critical in the success of such models. Based on a codelength minimization interpretation of sparse coding, and using tools from universal coding theory, we propose a framework for designing sparsity regularization terms which have theoretical and practical advantages when compared with the more standard ${ell_{0}}$ or ${ell_{1}}$ ones. The presentation of the framework and theoretical foundations is complemented with examples that show its practical advantages in image denoising, zooming and classification. View full abstract»

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  • A Novel Recursive Bayesian Learning-Based Method for the Efficient and Accurate Segmentation of Video With Dynamic Background

    Page(s): 3865 - 3876
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1366 KB) |  | HTML iconHTML  

    Segmentation of video with dynamic background is an important research topic in image analysis and computer vision domains. In this paper, we present a novel recursive Bayesian learning-based method for the efficient and accurate segmentation of video with dynamic background. In the algorithm, each frame pixel is represented as the layered normal distributions which correspond to different background contents in the scene. The layers are associated with a confident term and only the layers satisfy the given confidence which will be updated via the recursive Bayesian estimation. This makes learning of background motion trajectories more accurate and efficient. To improve the segmentation quality, the coarse foreground is obtained via simple background subtraction first. Then, a local texture correlation operator is introduced to fill the vacancies and remove the fractional false foreground regions. Extensive experiments on a variety of public video datasets and comparisons with some classical and recent algorithms are used to demonstrate its improvements in both segmentation accuracy and efficiency. View full abstract»

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  • Piecewise Linear Curve Approximation Using Graph Theory and Geometrical Concepts

    Page(s): 3877 - 3887
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1154 KB) |  | HTML iconHTML  

    In this paper, a new methodology for curve approximation is presented. The method is suitable for both self-intersected and non self-intersected curves, it combines elements from graph theory and from elementary geometry and it is fully automated. More specifically, graph theory tools are utilized in order: 1) to remove the details that are irrelevant to the general shape of the curve under study and 2) to decompose the curve into non self-intersecting smaller curves. Then, each such smaller curve is processed via geometrical tools in order to approximate it efficiently with linear segments. Experimental results show that the proposed method compares well with several other methods of the same purpose. View full abstract»

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  • Saliency Detection in the Compressed Domain for Adaptive Image Retargeting

    Page(s): 3888 - 3901
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3068 KB) |  | HTML iconHTML  

    Saliency detection plays important roles in many image processing applications, such as regions of interest extraction and image resizing. Existing saliency detection models are built in the uncompressed domain. Since most images over Internet are typically stored in the compressed domain such as joint photographic experts group (JPEG), we propose a novel saliency detection model in the compressed domain in this paper. The intensity, color, and texture features of the image are extracted from discrete cosine transform (DCT) coefficients in the JPEG bit-stream. Saliency value of each DCT block is obtained based on the Hausdorff distance calculation and feature map fusion. Based on the proposed saliency detection model, we further design an adaptive image retargeting algorithm in the compressed domain. The proposed image retargeting algorithm utilizes multioperator operation comprised of the block-based seam carving and the image scaling to resize images. A new definition of texture homogeneity is given to determine the amount of removal block-based seams. Thanks to the directly derived accurate saliency information from the compressed domain, the proposed image retargeting algorithm effectively preserves the visually important regions for images, efficiently removes the less crucial regions, and therefore significantly outperforms the relevant state-of-the-art algorithms, as demonstrated with the in-depth analysis in the extensive experiments. View full abstract»

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  • MIQM: A Multicamera Image Quality Measure

    Page(s): 3902 - 3914
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1519 KB) |  | HTML iconHTML  

    Although several subjective and objective quality assessment methods have been proposed in the literature for images and videos from single cameras, no comparable effort has been devoted to the quality assessment of multicamera images. With the increasing popularity of multiview applications, quality assessment of multicamera images and videos is becoming fundamental to the development of these applications. Image quality is affected by several factors, such as camera configuration, number of cameras, and the calibration process. In order to develop an objective metric specifically designed for multicamera systems, we identified and quantified two types of visual distortions in multicamera images: photometric distortions and geometric distortions. The relative distortion between individual camera scenes is a major factor in determining the overall perceived quality. In this paper, we show that such distortions can be translated into luminance, contrast, spatial motion, and edge-based structure components. We propose three different indices that can quantify these components. We provide examples to demonstrate the correlation among these components and the corresponding indices. Then, we combine these indices into one multicamera image quality measure (MIQM). Results and comparisons with other measures, such as peak signal-to noise ratio, mean structural similarity, and visual information fidelity show that MIQM outperforms other measures in capturing the perceptual fidelity of multicamera images. Finally, we verify the results against subjective evaluation. View full abstract»

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  • Improvements on “Fast Space-Variant Elliptical Filtering Using Box Splines”

    Page(s): 3915 - 3923
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (458 KB) |  | HTML iconHTML  

    It is well-known that box filters can be efficiently computed using pre-integration and local finite-differences. By generalizing this idea and by combining it with a nonstandard variant of the central limit theorem, we had earlier proposed a constant-time or $O(1)$ algorithm that allowed one to perform space-variant filtering using Gaussian-like kernels. The algorithm was based on the observation that both isotropic and anisotropic Gaussians could be approximated using certain bivariate splines called box splines. The attractive feature of the algorithm was that it allowed one to continuously control the shape and size (covariance) of the filter, and that it had a fixed computational cost per pixel, irrespective of the size of the filter. The algorithm, however, offered a limited control on the covariance and accuracy of the Gaussian approximation. In this paper, we propose some improvements of our previous algorithm. View full abstract»

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  • Bi-Exponential Edge-Preserving Smoother

    Page(s): 3924 - 3936
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2156 KB) |  | HTML iconHTML  

    Edge-preserving smoothers need not be taxed by a severe computational cost. We present, in this paper, a lean algorithm that is inspired by the bi-exponential filter and preserves its structure—a pair of one-tap recursions. By a careful but simple local adaptation of the filter weights to the data, we are able to design an edge-preserving smoother that has a very low memory and computational footprint while requiring a trivial coding effort. We demonstrate that our filter (a bi-exponential edge-preserving smoother, or BEEPS) has formal links with the traditional bilateral filter. On a practical side, we observe that the BEEPS also produces images that are similar to those that would result from the bilateral filter, but at a much-reduced computational cost. The cost per pixel is constant and depends neither on the data nor on the filter parameters, not even on the degree of smoothing. View full abstract»

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  • Robust Scale-Space Filter Using Second-Order Partial Differential Equations

    Page(s): 3937 - 3951
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (959 KB) |  | HTML iconHTML  

    This paper describes a robust scale-space filter that adaptively changes the amount of flux according to the local topology of the neighborhood. In a manner similar to modeling heat or temperature flow in physics, the robust scale-space filter is derived by coupling Fick's law with a generalized continuity equation in which the source or sink is modeled via a specific heat capacity. The filter plays an essential part in two aspects. First, an evolution step size is adaptively scaled according to the local structure, enabling the proposed filter to be numerically stable. Second, the influence of outliers is reduced by adaptively compensating for the incoming flux. We show that classical diffusion methods represent special cases of the proposed filter. By analyzing the stability condition of the proposed filter, we also verify that its evolution step size in an explicit scheme is larger than that of the diffusion methods. The proposed filter also satisfies the maximum principle in the same manner as the diffusion. Our experimental results show that the proposed filter is less sensitive to the evolution step size, as well as more robust to various outliers, such as Gaussian noise, impulsive noise, or a combination of the two. View full abstract»

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  • Video Denoising, Deblocking, and Enhancement Through Separable 4-D Nonlocal Spatiotemporal Transforms

    Page(s): 3952 - 3966
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2453 KB) |  | HTML iconHTML  

    We propose a powerful video filtering algorithm that exploits temporal and spatial redundancy characterizing natural video sequences. The algorithm implements the paradigm of nonlocal grouping and collaborative filtering, where a higher dimensional transform-domain representation of the observations is leveraged to enforce sparsity, and thus regularize the data: 3-D spatiotemporal volumes are constructed by tracking blocks along trajectories defined by the motion vectors. Mutually similar volumes are then grouped together by stacking them along an additional fourth dimension, thus producing a 4-D structure, termed group, where different types of data correlation exist along the different dimensions: local correlation along the two dimensions of the blocks, temporal correlation along the motion trajectories, and nonlocal spatial correlation (i.e., self-similarity) along the fourth dimension of the group. Collaborative filtering is then realized by transforming each group through a decorrelating 4-D separable transform and then by shrinkage and inverse transformation. In this way, the collaborative filtering provides estimates for each volume stacked in the group, which are then returned and adaptively aggregated to their original positions in the video. The proposed filtering procedure addresses several video processing applications, such as denoising, deblocking, and enhancement of both grayscale and color data. Experimental results prove the effectiveness of our method in terms of both subjective and objective visual quality, and show that it outperforms the state of the art in video denoising. View full abstract»

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  • Content-Aware Dark Image Enhancement Through Channel Division

    Page(s): 3967 - 3980
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3061 KB) |  | HTML iconHTML  

    The current contrast enhancement algorithms occasionally result in artifacts, overenhancement, and unnatural effects in the processed images. These drawbacks increase for images taken under poor illumination conditions. In this paper, we propose a content-aware algorithm that enhances dark images, sharpens edges, reveals details in textured regions, and preserves the smoothness of flat regions. The algorithm produces an ad hoc transformation for each image, adapting the mapping functions to each image's characteristics to produce the maximum enhancement. We analyze the contrast of the image in the boundary and textured regions, and group the information with common characteristics. These groups model the relations within the image, from which we extract the transformation functions. The results are then adaptively mixed, by considering the human vision system characteristics, to boost the details in the image. Results show that the algorithm can automatically process a wide range of images—e.g., mixed shadow and bright areas, outdoor and indoor lighting, and face images—without introducing artifacts, which is an improvement over many existing methods. View full abstract»

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  • Efficient Image Denoising Method Based on a New Adaptive Wavelet Packet Thresholding Function

    Page(s): 3981 - 3990
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1522 KB) |  | HTML iconHTML  

    This paper proposes a statistically optimum adaptive wavelet packet (WP) thresholding function for image denoising based on the generalized Gaussian distribution. It applies computationally efficient multilevel WP decomposition to noisy images to obtain the best tree or optimal wavelet basis, utilizing Shannon entropy. It selects an adaptive threshold value which is level and subband dependent based on analyzing the statistical parameters of subband coefficients. In the utilized thresholding function, which is based on a maximum a posteriori estimate, the modified version of dominant coefficients was estimated by optimal linear interpolation between each coefficient and the mean value of the corresponding subband. Experimental results, on several test images under different noise intensity conditions, show that the proposed algorithm, called OLI-Shrink, yields better peak signal noise ratio and superior visual image quality—measured by universal image quality index—compared to standard denoising methods, especially in the presence of high noise intensity. It also outperforms some of the best state-of-the-art wavelet-based denoising techniques. View full abstract»

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  • Partial Differential Equation-Based Approach for Empirical Mode Decomposition: Application on Image Analysis

    Page(s): 3991 - 4001
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2855 KB) |  | HTML iconHTML  

    The major problem with the empirical mode decomposition (EMD) algorithm is its lack of a theoretical framework. So, it is difficult to characterize and evaluate this approach. In this paper, we propose, in the 2-D case, the use of an alternative implementation to the algorithmic definition of the so-called “sifting process” used in the original Huang's EMD method. This approach, especially based on partial differential equations (PDEs), was presented by Niang in previous works, in 2005 and 2007, and relies on a nonlinear diffusion-based filtering process to solve the mean envelope estimation problem. In the 1-D case, the efficiency of the PDE-based method, compared to the original EMD algorithmic version, was also illustrated in a recent paper. Recently, several 2-D extensions of the EMD method have been proposed. Despite some effort, 2-D versions for EMD appear poorly performing and are very time consuming. So in this paper, an extension to the 2-D space of the PDE-based approach is extensively described. This approach has been applied in cases of both signal and image decomposition. The obtained results confirm the usefulness of the new PDE-based sifting process for the decomposition of various kinds of data. Some results have been provided in the case of image decomposition. The effectiveness of the approach encourages its use in a number of signal and image applications such as denoising, detrending, or texture analysis. View full abstract»

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  • Frame Fundamental High-Resolution Image Fusion From Inhomogeneous Measurements

    Page(s): 4002 - 4015
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1134 KB) |  | HTML iconHTML  

    Frame and fusion frame high-resolution image fusion formulations are presented. These techniques use the physical point spread function (PSF) of cameras as the building block of the mathematical frames in the fusion process. Cameras producing the low-resolution images are allowed to be different, and thereby possess different PSFs. Fused image reconstructions are carried out by a dimension invariance principle and by a set of iterative reconstruction algorithms. These frame fundamental approaches are also seen to be robust to realistic fusion problems from inhomogeneous image measurements (taken at different space or time or by different cameras), which is one of the main focuses of this paper. The effectiveness of this approach is demonstrated through both simulated and realistic examples. The results are quite encouraging. View full abstract»

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  • Single-Image Super-Resolution Reconstruction via Learned Geometric Dictionaries and Clustered Sparse Coding

    Page(s): 4016 - 4028
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1708 KB) |  | HTML iconHTML  

    Recently, single image super-resolution reconstruction (SISR) via sparse coding has attracted increasing interest. In this paper, we proposed a multiple-geometric-dictionaries-based clustered sparse coding scheme for SISR. Firstly, a large number of high-resolution (HR) image patches are randomly extracted from a set of example training images and clustered into several groups of “geometric patches,” from which the corresponding “geometric dictionaries” are learned to further sparsely code each local patch in a low-resolution image. A clustering aggregation is performed on the HR patches recovered by different dictionaries, followed by a subsequent patch aggregation to estimate the HR image. Considering that there are often many repetitive image structures in an image, we add a self-similarity constraint on the recovered image in patch aggregation to reveal new features and details. Finally, the HR residual image is estimated by the proposed recovery method and compensated to better preserve the subtle details of the images. Some experiments test the proposed method on natural images, and the results show that the proposed method outperforms its counterparts in both visual fidelity and numerical measures. View full abstract»

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  • Super Resolution Image Reconstruction Through Bregman Iteration Using Morphologic Regularization

    Page(s): 4029 - 4039
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1769 KB) |  | HTML iconHTML  

    Multiscale morphological operators are studied extensively in the literature for image processing and feature extraction purposes. In this paper, we model a nonlinear regularization method based on multiscale morphology for edge-preserving super resolution (SR) image reconstruction. We formulate SR image reconstruction as a deblurring problem and then solve the inverse problem using Bregman iterations. The proposed algorithm can suppress inherent noise generated during low-resolution image formation as well as during SR image estimation efficiently. Experimental results show the effectiveness of the proposed regularization and reconstruction method for SR image. View full abstract»

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  • Magnification of Label Maps With a Topology-Preserving Level-Set Method

    Page(s): 4040 - 4053
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (845 KB) |  | HTML iconHTML  

    Image segmentation aims at partitioning an image into multiple segments. The application of this procedure produces a label map (also referred to as segmentation map) that classifies the pixels of the original image. In contrast to “natural” images, label maps are nominal-scale images, typically represented as integer-valued images. Nominal-scaled label maps can also appear as a representation of the raw data in areas, such as in geostatistics. In some applications, the original resolution of a label map does not suffice and a larger size map has to be generated. In this paper, we present a magnification algorithm for label maps and nominal images. The main property of our method is that it preserves the topology during the magnification process, which means that no isolated pixel vanishes. To the best of our knowledge, apart from nearest-neighbor interpolation, the problem of label map magnification has not previously been addressed in the literature. The main idea of the proposed method is to accomplish a boundary refinement by smoothing the regions' boundaries on a finer grid. The method relies on well known methods, namely, the fundamental operations of morphological image processing–erosion and dilation–and the level-set method. The level-set method is well suited for our purposes since it does not depend on a parametrization and it is numerically stable. The topological flexibility of the level-set method—often found to be an advantage in applications—is a drawback here, since the topology of the original label map should be preserved. However, using the so-called simple point criterion from digital topology, one can adapt the conventional level-set method so that the topology will not be modified throughout the magnification procedure. View full abstract»

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  • Generative Bayesian Image Super Resolution With Natural Image Prior

    Page(s): 4054 - 4067
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1378 KB) |  | HTML iconHTML  

    We propose a new single image super resolution (SR) algorithm via Bayesian modeling with a natural image prior modeled by a high-order Markov random field (MRF). SR is one of the long-standing and active topics in image processing community. It is of great use in many practical applications, such as astronomical observation, medical imaging, and the adaptation of low-resolution contents onto high-resolution displays. One category of the conventional approaches for image SR is formulating the problem with Bayesian modeling techniques and then obtaining its maximum-a-posteriori solution, which actually boils down to a regularized regression task. Although straightforward, this approach cannot exploit the full potential offered by the probabilistic modeling, as only the posterior mode is sought. On the other hand, current Bayesian SR approaches using the posterior mean estimation typically use very simple prior models for natural images to ensure the computational tractability. In this paper, we present a Bayesian image SR approach with a flexible high-order MRF model as the prior for natural images. The minimum mean square error (MMSE) criteria are used for estimating the HR image. A Markov chain Monte Carlo-based sampling algorithm is presented for obtaining the MMSE solution. The proposed method cannot only enjoy the benefits offered by the flexible prior, but also has the advantage of making use of the probabilistic modeling to perform a posterior mean estimation, thus is less sensitive to the local minima problem as the MAP solution. Experimental results indicate that the proposed method can generate competitive or better results than state-of-the-art SR algorithms. View full abstract»

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  • Unified Framework for Automated Iris Segmentation Using Distantly Acquired Face Images

    Page(s): 4068 - 4079
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1736 KB) |  | HTML iconHTML  

    Remote human identification using iris biometrics has high civilian and surveillance applications and its success requires the development of robust segmentation algorithm to automatically extract the iris region. This paper presents a new iris segmentation framework which can robustly segment the iris images acquired using near infrared or visible illumination. The proposed approach exploits multiple higher order local pixel dependencies to robustly classify the eye region pixels into iris or noniris regions. Face and eye detection modules have been incorporated in the unified framework to automatically provide the localized eye region from facial image for iris segmentation. We develop robust postprocessing operations algorithm to effectively mitigate the noisy pixels caused by the misclassification. Experimental results presented in this paper suggest significant improvement in the average segmentation errors over the previously proposed approaches, i.e., 47.5%, 34.1%, and 32.6% on UBIRIS.v2, FRGC, and CASIA.v4 at-a-distance databases, respectively. The usefulness of the proposed approach is also ascertained from recognition experiments on three different publicly available databases. View full abstract»

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  • Bayesian Technique for Image Classifying Registration

    Page(s): 4080 - 4091
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (802 KB) |  | HTML iconHTML  

    In this paper, we address a complex image registration issue arising while the dependencies between intensities of images to be registered are not spatially homogeneous. Such a situation is frequently encountered in medical imaging when a pathology present in one of the images modifies locally intensity dependencies observed on normal tissues. Usual image registration models, which are based on a single global intensity similarity criterion, fail to register such images, as they are blind to local deviations of intensity dependencies. Such a limitation is also encountered in contrast-enhanced images where there exist multiple pixel classes having different properties of contrast agent absorption. In this paper, we propose a new model in which the similarity criterion is adapted locally to images by classification of image intensity dependencies. Defined in a Bayesian framework, the similarity criterion is a mixture of probability distributions describing dependencies on two classes. The model also includes a class map which locates pixels of the two classes and weighs the two mixture components. The registration problem is formulated both as an energy minimization problem and as a maximum a posteriori estimation problem. It is solved using a gradient descent algorithm. In the problem formulation and resolution, the image deformation and the class map are estimated simultaneously, leading to an original combination of registration and classification that we call image classifying registration. Whenever sufficient information about class location is available in applications, the registration can also be performed on its own by fixing a given class map. Finally, we illustrate the interest of our model on two real applications from medical imaging: template-based segmentation of contrast-enhanced images and lesion detection in mammograms. We also conduct an evaluation of our model on simulated medical data and show its ability to take into account spatial variations o- intensity dependencies while keeping a good registration accuracy. View full abstract»

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  • Multiview Image Coding Using Depth Layers and an Optimized Bit Allocation

    Page(s): 4092 - 4105
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1319 KB) |  | HTML iconHTML  

    In this paper, we present a novel wavelet-based compression algorithm for multiview images. This method uses a layer-based representation, where the 3-D scene is approximated by a set of depth planes with their associated constant disparities. The layers are extracted from a collection of images captured at multiple viewpoints and transformed using the 3-D discrete wavelet transform (DWT). The DWT consists of the 1-D disparity compensated DWT across the viewpoints and the 2-D shape-adaptive DWT across the spatial dimensions. Finally, the wavelet coefficients are quantized and entropy coded along with the layer contours. To improve the rate-distortion performance of the entire coding method, we develop a bit allocation strategy for the distribution of the available bit budget between encoding the layer contours and the wavelet coefficients. The achieved performance of our proposed scheme outperforms the state-of-the-art codecs for several data sets of varying complexity. View full abstract»

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  • Mode-Dependent Templates and Scan Order for H.264/AVC-Based Intra Lossless Coding

    Page(s): 4106 - 4116
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (866 KB) |  | HTML iconHTML  

    In H.264/advanced video coding (AVC), lossless coding and lossy coding share the same entropy coding module. However, the entropy coders in the H.264/AVC standard were original designed for lossy video coding and do not yield adequate performance for lossless video coding. In this paper, we analyze the problem with the current lossless coding scheme and propose a mode-dependent template (MD-template) based method for intra lossless coding. By exploring the statistical redundancy of the prediction residual in the H.264/AVC intra prediction modes, more zero coefficients are generated. By designing a new scan order for each MD-template, the scanned coefficients sequence fits the H.264/AVC entropy coders better. A fast implementation algorithm is also designed. With little computation increase, experimental results confirm that the proposed fast algorithm achieves about 7.2% bit saving compared with the current H.264/AVC fidelity range extensions high profile. 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