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Image Processing, IEEE Transactions on

Issue 1 • Date Jan. 2011

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Displaying Results 1 - 25 of 35
  • Table of contents

    Page(s): C1 - C4
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    Freely Available from IEEE
  • IEEE Transactions on Image Processing publication information

    Page(s): C2
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    Freely Available from IEEE
  • The iDUDE Framework for Grayscale Image Denoising

    Page(s): 1 - 21
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2927 KB) |  | HTML iconHTML  

    We present an extension of the discrete universal denoiser DUDE, specialized for the denoising of grayscale images. The original DUDE is a low-complexity algorithm aimed at recovering discrete sequences corrupted by discrete memoryless noise of known statistical characteristics. It is universal, in the sense of asymptotically achieving, without access to any information on the statistics of the clean sequence, the same performance as the best denoiser that does have access to such information. The DUDE, however, is not effective on grayscale images of practical size. The difficulty lies in the fact that one of the DUDE's key components is the determination of conditional empirical probability distributions of image samples, given the sample values in their neighborhood. When the alphabet is relatively large (as is the case with grayscale images), even for a small-sized neighborhood, the required distributions would be estimated from a large collection of sparse statistics, resulting in poor estimates that would not enable effective denoising. The present work enhances the basic DUDE scheme by incorporating statistical modeling tools that have proven successful in addressing similar issues in lossless image compression. Instantiations of the enhanced framework, which is referred to as iDUDE, are described for examples of additive and nonadditive noise. The resulting denoisers significantly surpass the state of the art in the case of salt and pepper (S&P) and -ary symmetric noise, and perform well for Gaussian noise. View full abstract»

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  • Efficiently Learning a Detection Cascade With Sparse Eigenvectors

    Page(s): 22 - 35
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2503 KB) |  | HTML iconHTML  

    Real-time object detection has many computer vision applications. Since Viola and Jones proposed the first real-time AdaBoost based face detection system, much effort has been spent on improving the boosting method. In this work, we first show that feature selection methods other than boosting can also be used for training an efficient object detector. In particular, we introduce greedy sparse linear discriminant analysis (GSLDA) for its conceptual simplicity and computational efficiency; and slightly better detection performance is achieved compared with . Moreover, we propose a new technique, termed boosted greedy sparse linear discriminant analysis (BGSLDA), to efficiently train a detection cascade. BGSLDA exploits the sample reweighting property of boosting and the class-separability criterion of GSLDA. Experiments in the domain of highly skewed data distributions (e.g., face detection) demonstrate that classifiers trained with the proposed BGSLDA outperforms AdaBoost and its variants. This finding provides a significant opportunity to argue that AdaBoost and similar approaches are not the only methods that can achieve high detection results for real-time object detection. View full abstract»

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  • Adaptive Sequential Prediction of Multidimensional Signals With Applications to Lossless Image Coding

    Page(s): 36 - 42
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2350 KB) |  | HTML iconHTML  

    We investigate the problem of designing adaptive sequential linear predictors for the class of piecewise autoregressive multidimensional signals, and adopt an approach of minimum description length (MDL) to determine the order of the predictor and the support on which the predictor operates. The design objective is to strike a balance between the bias and variance of the prediction errors in the MDL criterion. The predictor design problem is particularly interesting and challenging for multidimensional signals (e.g., images and videos) because of the increased degree of freedom in choosing the predictor support. Our main result is a new technique of sequentializing a multidimensional signal into a sequence of nested contexts of increasing order to facilitate the MDL search for the order and the support shape of the predictor, and the sequentialization is made adaptive on a sample by sample basis. The proposed MDL-based adaptive predictor is applied to lossless image coding, and its performance is empirically established to be the best among all the results that have been published till present. View full abstract»

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  • Large Disparity Motion Layer Extraction via Topological Clustering

    Page(s): 43 - 52
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (6337 KB) |  | HTML iconHTML  

    In this paper, we present a robust and efficient approach to extract motion layers from a pair of images with large disparity motion. First, motion models are established as: 1) initial SIFT matches are obtained and grouped into a set of clusters using our developed topological clustering algorithm; 2) for each cluster with no less than three matches, an affine transformation is estimated with least-square solution as tentative motion model; and 3) the tentative motion models are refined and the invalid models are pruned. Then, with the obtained motion models, a graph cuts based layer assignment algorithm is employed to segment the scene into several motion layers. Experimental results demonstrate that our method can successfully segment scenes containing objects with large interframe motion or even with significant interframe scale and pose changes. Furthermore, compared with the previous method invented by Wills and its modified version, our method is much faster and more robust. View full abstract»

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  • A Uniform Framework for Estimating Illumination Chromaticity, Correspondence, and Specular Reflection

    Page(s): 53 - 63
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2029 KB) |  | HTML iconHTML  

    Based upon a new correspondence matching invariant called illumination chromaticity constancy, we present a new solution for illumination chromaticity estimation, correspondence searching, and specularity removal. Using as few as two images, the core of our method is the computation of a vote distribution for a number of illumination chromaticity hypotheses via correspondence matching. The hypothesis with the highest vote is accepted as correct. The estimated illumination chromaticity is then used together with the new matching invariant to match highlights, which inherently provides solutions for correspondence searching and specularity removal. Our method differs from the previous approaches: those treat these vision problems separately and generally require that specular highlights be detected in a preprocessing step. Also, our method uses more images than previous illumination chromaticity estimation methods, which increases its robustness because more inputs/constraints are used. Experimental results on both synthetic and real images demonstrate the effectiveness of the proposed method. View full abstract»

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  • No-Reference Blur Assessment of Digital Pictures Based on Multifeature Classifiers

    Page(s): 64 - 75
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1797 KB) |  | HTML iconHTML  

    In this paper, we address the problem of no-reference quality assessment for digital pictures corrupted with blur. We start with the generation of a large real image database containing pictures taken by human users in a variety of situations, and the conduction of subjective tests to generate the ground truth associated to those images. Based upon this ground truth, we select a number of high quality pictures and artificially degrade them with different intensities of simulated blur (gaussian and linear motion), totalling 6000 simulated blur images. We extensively evaluate the performance of state-of-the-art strategies for no-reference blur quantification in different blurring scenarios, and propose a paradigm for blur evaluation in which an effective method is pursued by combining several metrics and low-level image features. We test this paradigm by designing a no-reference quality assessment algorithm for blurred images which combines different metrics in a classifier based upon a neural network structure. Experimental results show that this leads to an improved performance that better reflects the images' ground truth. Finally, based upon the real image database, we show that the proposed method also outperforms other algorithms and metrics in realistic blur scenarios. View full abstract»

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  • Measuring the Quality of Quality Measures

    Page(s): 76 - 87
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (517 KB) |  | HTML iconHTML  

    Print quality (PQ) is a composite attribute defined by human perception. As such, the ultimate way to determine and quantify PQ is by human survey. However, repeated surveys are time consuming and often represent a burden on processes that involve repeated evaluations. A desired alternative would be an automatic quality rating tool. Once such quality evaluation measure is proposed, it should be qualified. That is, it should be shown to reflect human assessment. If two of the human opinions conflict, the tool cannot possibly agree with both. Conflicts between human opinions are common, which complicates the evaluation of tool's success in reflecting human judgment. There are many optional ways for measuring the agreement between human assessment and tool evaluation, but different methods may have conflicting results. It is, therefore, important to pre-establish the appropriate method for the evaluation of quality-evaluation-tools, a method that takes the disagreement among the survey participants into account. In this paper, we model human quality preference and derive the most appropriate method to qualify quality evaluation tools. We demonstrate the resulting qualification method in a real life scenario-the qualification of the mechanical band meter. View full abstract»

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  • Quality Assessment of Deblocked Images

    Page(s): 88 - 98
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1875 KB) |  | HTML iconHTML  

    We study the efficiency of deblocking algorithms for improving visual signals degraded by blocking artifacts from compression. Rather than using only the perceptually questionable PSNR, we instead propose a block-sensitive index, named PSNR-B, that produces objective judgments that accord with observations. The PSNR-B modifies PSNR by including a blocking effect factor. We also use the perceptually significant SSIM index, which produces results largely in agreement with PSNR-B. Simulation results show that the PSNR-B results in better performance for quality assessment of deblocked images than PSNR and a well-known blockiness-specific index. View full abstract»

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  • Optimal Inversion of the Anscombe Transformation in Low-Count Poisson Image Denoising

    Page(s): 99 - 109
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3897 KB) |  | HTML iconHTML  

    The removal of Poisson noise is often performed through the following three-step procedure. First, the noise variance is stabilized by applying the Anscombe root transformation to the data, producing a signal in which the noise can be treated as additive Gaussian with unitary variance. Second, the noise is removed using a conventional denoising algorithm for additive white Gaussian noise. Third, an inverse transformation is applied to the denoised signal, obtaining the estimate of the signal of interest. The choice of the proper inverse transformation is crucial in order to minimize the bias error which arises when the nonlinear forward transformation is applied. We introduce optimal inverses for the Anscombe transformation, in particular the exact unbiased inverse, a maximum likelihood (ML) inverse, and a more sophisticated minimum mean square error (MMSE) inverse. We then present an experimental analysis using a few state-of-the-art denoising algorithms and show that the estimation can be consistently improved by applying the exact unbiased inverse, particularly at the low-count regime. This results in a very efficient filtering solution that is competitive with some of the best existing methods for Poisson image denoising. View full abstract»

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  • Sub-Hexagonal Phase Correlation for Motion Estimation

    Page(s): 110 - 120
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (7102 KB) |  | HTML iconHTML  

    We present a novel frequency-domain motion estimation technique, which operates on hexagonal images and employs the hexagonal Fourier transform. Our method involves image sampling on a hexagonal lattice followed by a normalised hexagonal cross-correlation in the frequency domain. The term subpixel (or subcell) is defined on a hexagonal grid in order to achieve floating point registration. Experiments using both artificially induced motion and actual motion demonstrate that the proposed method outperforms the state-of-the-art in frequency-domain motion estimation operating on a square lattice, in the shape of phase correlation, in terms of subpixel accuracy for a range of test material and motion scenarios. View full abstract»

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  • Fast H.264/AVC FRExt Intra Coding Using Belief Propagation

    Page(s): 121 - 131
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1000 KB) |  | HTML iconHTML  

    In the H.264/AVC FRExt coder, the coding performance of Intra coding significantly overcomes the previous still image coding standards, like JPEG2000, thanks to a massive use of spatial prediction. Unfortunately, the adoption of an extensive set of predictors induces a significant increase of the computational complexity required by the rate-distortion optimization routine. The paper presents a complexity reduction strategy that aims at reducing the computational load of the Intra coding with a small loss in the compression performance. The proposed algorithm relies on selecting a reduced set of prediction modes according to their probabilities, which are estimated adopting a belief-propagation procedure. Experimental results show that the proposed method permits saving up to of the coding time required by an exhaustive rate-distortion optimization method with a negligible loss in performance. Moreover, it permits an accurate control of the computational complexity unlike other methods where the computational complexity depends upon the coded sequence. View full abstract»

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  • Color Extended Visual Cryptography Using Error Diffusion

    Page(s): 132 - 145
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (6119 KB) |  | HTML iconHTML  

    Color visual cryptography (VC) encrypts a color secret message into color halftone image shares. Previous methods in the literature show good results for black and white or gray scale VC schemes, however, they are not sufficient to be applied directly to color shares due to different color structures. Some methods for color visual cryptography are not satisfactory in terms of producing either meaningless shares or meaningful shares with low visual quality, leading to suspicion of encryption. This paper introduces the concept of visual information pixel (VIP) synchronization and error diffusion to attain a color visual cryptography encryption method that produces meaningful color shares with high visual quality. VIP synchronization retains the positions of pixels carrying visual information of original images throughout the color channels and error diffusion generates shares pleasant to human eyes. Comparisons with previous approaches show the superior performance of the new method. View full abstract»

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  • Convex Total Variation Denoising of Poisson Fluorescence Confocal Images With Anisotropic Filtering

    Page(s): 146 - 160
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2094 KB) |  | HTML iconHTML  

    Fluorescence confocal microscopy (FCM) is now one of the most important tools in biomedicine research. In fact, it makes it possible to accurately study the dynamic processes occurring inside the cell and its nucleus by following the motion of fluorescent molecules over time. Due to the small amount of acquired radiation and the huge optical and electronics amplification, the FCM images are usually corrupted by a severe type of Poisson noise. This noise may be even more damaging when very low intensity incident radiation is used to avoid phototoxicity. In this paper, a Bayesian algorithm is proposed to remove the Poisson intensity dependent noise corrupting the FCM image sequences. The observations are organized in a 3-D tensor where each plane is one of the images acquired along the time of a cell nucleus using the fluorescence loss in photobleaching (FLIP) technique. The method removes simultaneously the noise by considering different spatial and temporal correlations. This is accomplished by using an anisotropic 3-D filter that may be separately tuned in space and in time dimensions. Tests using synthetic and real data are described and presented to illustrate the application of the algorithm. A comparison with several state-of-the-art algorithms is also presented. View full abstract»

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  • Fast Model-Based X-Ray CT Reconstruction Using Spatially Nonhomogeneous ICD Optimization

    Page(s): 161 - 175
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2674 KB) |  | HTML iconHTML  

    Recent applications of model-based iterative reconstruction (MBIR) algorithms to multislice helical CT reconstructions have shown that MBIR can greatly improve image quality by increasing resolution as well as reducing noise and some artifacts. However, high computational cost and long reconstruction times remain as a barrier to the use of MBIR in practical applications. Among the various iterative methods that have been studied for MBIR, iterative coordinate descent (ICD) has been found to have relatively low overall computational requirements due to its fast convergence. This paper presents a fast model-based iterative reconstruction algorithm using spatially nonhomogeneous ICD (NH-ICD) optimization. The NH-ICD algorithm speeds up convergence by focusing computation where it is most needed. The NH-ICD algorithm has a mechanism that adaptively selects voxels for update. First, a voxel selection criterion VSC determines the voxels in greatest need of update. Then a voxel selection algorithm VSA selects the order of successive voxel updates based upon the need for repeated updates of some locations, while retaining characteristics for global convergence. In order to speed up each voxel update, we also propose a fast 1-D optimization algorithm that uses a quadratic substitute function to upper bound the local 1-D objective function, so that a closed form solution can be obtained rather than using a computationally expensive line search algorithm. We examine the performance of the proposed algorithm using several clinical data sets of various anatomy. The experimental results show that the proposed method accelerates the reconstructions by roughly a factor of three on average for typical 3-D multislice geometries. View full abstract»

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  • Tomographic Reconstruction of Gated Data Acquisition Using DFT Basis Functions

    Page(s): 176 - 185
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1166 KB) |  | HTML iconHTML  

    In image reconstruction gated acquisition is often used in order to deal with blur caused by organ motion in the resulting images. However, this is achieved almost inevitably at the expense of reduced signal-to-noise ratio in the acquired data. In this work, we propose a reconstruction procedure for gated images based upon use of discrete Fourier transform (DFT) basis functions, wherein the temporal activity at each spatial location is regulated by a Fourier representation. The gated images are then reconstructed through determination of the coefficients of the Fourier representation. We demonstrate this approach in the context of single photon emission computed tomography (SPECT) for cardiac imaging, which is often hampered by the increased noise due to gating and other degrading factors. We explore two different reconstruction algorithms, one is a penalized least-square approach and the other is a maximum a posteriori approach. In our experiments, we conducted a quantitative evaluation of the proposed approach using Monte Carlo simulated SPECT imaging. The results demonstrate that use of DFT-basis functions in gated imaging can improve the accuracy of the reconstruction. As a preliminary demonstration, we also tested this approach on a set of clinical acquisition. View full abstract»

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  • Studentized Dynamical System for Robust Object Tracking

    Page(s): 186 - 199
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2053 KB) |  | HTML iconHTML  

    This paper describes a studentized dynamical system (SDS) for robust target tracking using a subspace representation. Dynamical systems (DS) provide a powerful framework for the probabilistic modeling of temporal sequences. Visual tracking problems are often cast as a sequential inference problem within the DS framework and a compact way to model the observation distributions (i.e., object appearances) is through probabilistic principal component analysis (PPCA). PPCA is a classic Gaussian based subspace representation method and a popular tool for appearance modeling. Although Gaussian density has theoretically nice properties, resulting in models that are always tractable, they are also severely limited in practical settings. One of the central issues in the use of PPCA for target appearance modeling is that it is very sensitive to outliers. The Gaussian density has a very light tail, while real world data with outliers exhibit heavy tails. Recently, more heavy-tailed distributions (e.g., Student's t-distribution) have been introduced to increase the robustness of the original PPCA. We propose to augment the traditional target tracking DS by adding a set of auxiliary latent variables to adjust the shape of the observation distribution. We show that by carefully choosing the probability density of these auxiliary latent variables, a more robust observation distribution can be obtained with tails heavier than Gaussian. Numerical experiments verify that the proposed SDS has a better capability to handle considerable amount of outlier noise and an improved tracking performance over DS with a Gaussian based observation model. View full abstract»

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  • Anisotropic Morphological Filters With Spatially-Variant Structuring Elements Based on Image-Dependent Gradient Fields

    Page(s): 200 - 212
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (7630 KB) |  | HTML iconHTML  

    This paper deals with the theory and applications of spatially-variant discrete mathematical morphology. We review and formalize the definition of spatially variant dilation/erosion and opening/closing for binary and gray-level images using exclusively the structuring function, without resorting to complement. This theoretical framework allows to build morphological operators whose structuring elements can locally adapt their shape and orientation across the dominant direction of the structures in the image. The shape and orientation of the structuring element at each pixel are extracted from the image under study: the orientation is given by means of a diffusion process of the average square gradient field, which regularizes and extends the orientation information from the edges of the objects to the homogeneous areas of the image; and the shape of the orientated structuring elements can be linear or it can be given by the distance to relevant edges of the objects. The proposed filters are used on binary and gray-level images for enhancement of anisotropic features such as coherent, flow-like structures. Results of spatially-variant erosions/dilations and openings/closings-based filters prove the validity of this theoretical sound and novel approach. View full abstract»

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  • Incremental Training of a Detector Using Online Sparse Eigendecomposition

    Page(s): 213 - 226
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3883 KB) |  | HTML iconHTML  

    The ability to efficiently and accurately detect objects plays a very crucial role for many computer vision tasks. Recently, offline object detectors have shown a tremendous success. However, one major drawback of offline techniques is that a complete set of training data has to be collected beforehand. In addition, once learned, an offline detector cannot make use of newly arriving data. To alleviate these drawbacks, online learning has been adopted with the following objectives: 1) the technique should be computationally and storage efficient; 2) the updated classifier must maintain its high classification accuracy. In this paper, we propose an effective and efficient framework for learning an adaptive online greedy sparse linear discriminant analysis model. Unlike many existing online boosting detectors, which usually apply exponential or logistic loss, our online algorithm makes use of linear discriminant analysis' learning criterion that not only aims to maximize the class-separation criterion but also incorporates the asymmetrical property of training data distributions. We provide a better alternative for online boosting algorithms in the context of training a visual object detector. We demonstrate the robustness and efficiency of our methods on handwritten digit and face data sets. Our results confirm that object detection tasks benefit significantly when trained in an online manner. View full abstract»

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  • Fractal Dimension of Color Fractal Images

    Page(s): 227 - 235
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1383 KB) |  | HTML iconHTML  

    Fractal dimension is a very useful metric for the analysis of the images with self-similar content, such as textures. For its computation there exist several approaches, the probabilistic algorithm being accepted as the most elegant approach. However, all the existing methods are defined for 1-D signals or binary images, with extension to grayscale images. Our purpose is to propose a color version of the probabilistic algorithm for the computation of the fractal dimension. To validate this new approach, we also propose an extension of the existing algorithm for the generation of probabilistic fractals, in order to obtain color fractal images. Then we show the results of our experiments and conclude this paper. View full abstract»

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  • Computational Perceptual Features for Texture Representation and Retrieval

    Page(s): 236 - 246
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1803 KB) |  | HTML iconHTML  

    A perception-based approach to content-based image representation and retrieval is proposed in this paper. We consider textured images and propose to model their textural content by a set of features having a perceptual meaning and their application to content-based image retrieval. We present a new method to estimate a set of perceptual textural features, namely coarseness, directionality, contrast, and busyness. The proposed computational measures can be based upon two representations: the original images representation and the autocorrelation function (associated with original images) representation. The set of computational measures proposed is applied to content-based image retrieval on a large image data set, the well-known Brodatz database. Experimental results and benchmarking show interesting performance of our approach. First, the correspondence of the proposed computational measures to human judgments is shown using a psychometric method based upon the Spearman rank-correlation coefficient. Second, the application of the proposed computational measures in texture retrieval shows interesting results, especially when using results fusion returned by each of the two representations. Comparison is also given with related works and show excellent performance of our approach compared to related approaches on both sides: correspondence of the proposed computational measures with human judgments as well as the retrieval effectiveness. View full abstract»

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  • Face Recognition by Exploring Information Jointly in Space, Scale and Orientation

    Page(s): 247 - 256
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2274 KB) |  | HTML iconHTML  

    Information jointly contained in image space, scale and orientation domains can provide rich important clues not seen in either individual of these domains. The position, spatial frequency and orientation selectivity properties are believed to have an important role in visual perception. This paper proposes a novel face representation and recognition approach by exploring information jointly in image space, scale and orientation domains. Specifically, the face image is first decomposed into different scale and orientation responses by convolving multiscale and multi-orientation Gabor filters. Second, local binary pattern analysis is used to describe the neighboring relationship not only in image space, but also in different scale and orientation responses. This way, information from different domains is explored to give a good face representation for recognition. Discriminant classification is then performed based upon weighted histogram intersection or conditional mutual information with linear discriminant analysis techniques. Extensive experimental results on FERET, AR, and FRGC ver 2.0 databases show the significant advantages of the proposed method over the existing ones. View full abstract»

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  • Random Phase Textures: Theory and Synthesis

    Page(s): 257 - 267
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3306 KB) |  | HTML iconHTML  

    This paper explores the mathematical and algorithmic properties of two sample-based texture models: random phase noise (RPN) and asymptotic discrete spot noise (ADSN). These models permit to synthesize random phase textures. They arguably derive from linearized versions of two early Julesz texture discrimination theories. The ensuing mathematical analysis shows that, contrarily to some statements in the literature, RPN and ADSN are different stochastic processes. Nevertheless, numerous experiments also suggest that the textures obtained by these algorithms from identical samples are perceptually similar. The relevance of this study is enhanced by three technical contributions providing solutions to obstacles that prevented the use of RPN or ADSN to emulate textures. First, RPN and ADSN algorithms are extended to color images. Second, a preprocessing is proposed to avoid artifacts due to the nonperiodicity of real-world texture samples. Finally, the method is extended to synthesize textures with arbitrary size from a given sample. View full abstract»

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  • Comments on "Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering

    Page(s): 268 - 270
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1122 KB) |  | HTML iconHTML  

    In order to resolve the problem that the denoising performance has a sharp drop when noise standard deviation reaches 40, proposed to replace the wavelet transform by the DCT. In this comment, we argue that this replacement is unnecessary, and that the problem can be solved by adjusting some numerical parameters. We also present this parameter modification approach here. Experimental results demonstrate that the proposed modification achieves better results in terms of both peak signal-to-noise ratio and subjective visual quality than the original method for strong noise. 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