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Image Processing, IEEE Transactions on

Issue 8 • Date Aug. 2005

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Displaying Results 1 - 23 of 23
  • Table of contents

    Page(s): c1 - c4
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  • IEEE Transactions on Image Processing publication information

    Page(s): c2
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  • Compound image compression for real-time computer screen image transmission

    Page(s): 993 - 1005
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4419 KB) |  | HTML iconHTML  

    We present a compound image compression algorithm for real-time applications of computer screen image transmission. It is called shape primitive extraction and coding (SPEC). Real-time image transmission requires that the compression algorithm should not only achieve high compression ratio, but also have low complexity and provide excellent visual quality. SPEC first segments a compound image into text/graphics pixels and pictorial pixels, and then compresses the text/graphics pixels with a new lossless coding algorithm and the pictorial pixels with the standard lossy JPEG, respectively. The segmentation first classifies image blocks into picture and text/graphics blocks by thresholding the number of colors of each block, then extracts shape primitives of text/graphics from picture blocks. Dynamic color palette that tracks recent text/graphics colors is used to separate small shape primitives of text/graphics from pictorial pixels. Shape primitives are also extracted from text/graphics blocks. All shape primitives from both block types are losslessly compressed by using a combined shape-based and palette-based coding algorithm. Then, the losslessly coded bitstream is fed into a LZW coder. Experimental results show that the SPEC has very low complexity and provides visually lossless quality while keeping competitive compression ratios. View full abstract»

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  • Optimal erasure protection for scalably compressed video streams with limited retransmission

    Page(s): 1006 - 1019
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (521 KB) |  | HTML iconHTML  

    This paper shows how the priority encoding transmission (PET) framework may be leveraged to exploit both unequal error protection and limited retransmission for RD-optimized delivery of streaming media. Previous work on scalable media protection with PET has largely ignored the possibility of retransmission. Conversely, the PET framework has not been harnessed by the substantial body of previous work on RD optimized hybrid forward error correction/automatic repeat request schemes. We limit our attention to sources which can be modeled as independently compressed frames (e.g., video frames), where each element in the scalable representation of each frame can be transmitted in one or both of two transmission slots. An optimization algorithm determines the level of protection which should be assigned to each element in each slot, subject to transmission bandwidth constraints. To balance the protection assigned to elements which are being transmitted for the first time with those which are being retransmitted, the proposed algorithm formulates a collection of hypotheses concerning its own behavior in future transmission slots. We show how the PET framework allows for a decoupled optimization algorithm with only modest complexity. Experimental results obtained with Motion JPEG2000 compressed video demonstrate that substantial performance benefits can be obtained using the proposed framework. View full abstract»

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  • Joint source/channel coding for image transmission with JPEG2000 over memoryless channels

    Page(s): 1020 - 1032
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (656 KB) |  | HTML iconHTML  

    The high compression efficiency and various features provided by JPEG2000 make it attractive for image transmission purposes. A novel joint source/channel coding scheme tailored for JPEG2000 is proposed in this paper to minimize the end-to-end image distortion within a given total transmission rate through memoryless channels. It provides unequal error protection by combining the forward error correction capability from channel codes and the error detection/localization functionality from JPEG2000 in an effective way. The proposed scheme generates quality scalable and error-resilient codestreams. It gives competitive performance with other existing schemes for JPEG2000 in the matched channel condition case and provides more graceful quality degradation for mismatched cases. Furthermore, both fixed-length source packets and fixed-length channel packets can be efficiently formed with the same algorithm. View full abstract»

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  • Tile-boundary artifact reduction using odd tile size and the low-pass first convention

    Page(s): 1033 - 1042
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1600 KB) |  | HTML iconHTML  

    It is well known that tile-boundary artifacts occur in wavelet-based lossy image coding. However, until now, their cause has not been understood well. In this paper, we show that boundary artifacts are an inescapable consequence of the usual methods used to choose tile size and the type of symmetric extension employed in a wavelet-based image decomposition system. This paper presents a novel method for reducing these tile-boundary artifacts. The method employs odd tile sizes (2/sup N/+1 samples) rather than the conventional even tile sizes (2/sup N/ samples). It is shown that, for the same bit rate, an image compressed using an odd tile length low-pass first (OTLPF) convention has significantly less boundary artifacts than an image compressed using even tile sizes. The OTLPF convention can also be incorporated into the JPEG 2000 image compression algorithm using extensions defined in Part 2 of this standard. View full abstract»

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  • Representing images using points on image surfaces

    Page(s): 1043 - 1056
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4582 KB) |  | HTML iconHTML  

    This paper presents a new approach to represent an image by "verge points," which are defined as high-curvature points on the image surface. This representation offers a compact and reversible way to preserve the essence of the original image. Various applications, such as compression, edge detection, image enhancement, and image editing, can be achieved based on this representation. In this paper, the whole procedure for verge point representation is presented. Based on these verge points, image reconstruction can be easily achieved via iterative linear interpolation. These extracted verge points with compatible properties are further linked into verge curves to offer more compact representation. Progressive representation is also developed based on a multiscale extraction scheme. Some potential applications are then presented to demonstrate the versatility of this representation. View full abstract»

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  • Statistical bias in 3-D reconstruction from a monocular video

    Page(s): 1057 - 1062
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (270 KB) |  | HTML iconHTML  

    The present state-of-the-art in computing the error statistics in three-dimensional (3-D) reconstruction from video concentrates on estimating the error covariance. A different source of error which has not received much attention is the fact that the reconstruction estimates are often significantly statistically biased. In this paper, we derive a precise expression for the bias in the depth estimate, based on the continuous (differentiable) version of structure from motion (SfM). Many SfM algorithms, or certain portions of them, can be posed in a linear least-squares (LS) framework Ax=b. Examples include initialization procedures for bundle adjustment or algorithms that alternately estimate depth and camera motion. It is a well-known fact that the LS estimate is biased if the system matrix A is noisy. In SfM, the matrix A contains point correspondences, which are always difficult to obtain precisely; thus, it is expected that the structure and motion estimates in such a formulation of the problem would be biased. Existing results on the minimum achievable variance of the SfM estimator are extended by deriving a generalized Cramer-Rao lower bound. A detailed analysis of the effect of various camera motion parameters on the bias is presented. We conclude by presenting the effect of bias compensation on reconstructing 3-D face models from rendered images. View full abstract»

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  • Fast robust correlation

    Page(s): 1063 - 1073
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2011 KB) |  | HTML iconHTML  

    A new, fast, statistically robust, exhaustive, translational image-matching technique is presented: fast robust correlation. Existing methods are either slow or non-robust, or rely on optimization. Fast robust correlation works by expressing a robust matching surface as a series of correlations. Speed is obtained by computing correlations in the frequency domain. Computational cost is analyzed and the method is shown to be fast. Speed is comparable to conventional correlation and, for large images, thousands of times faster than direct robust matching. Three experiments demonstrate the advantage of the technique over standard correlation. View full abstract»

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  • Optical flow estimation using temporally oversampled video

    Page(s): 1074 - 1087
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1877 KB) |  | HTML iconHTML  

    Recent advances in imaging sensor technology make high frame-rate video capture practical. As demonstrated in previous work, this capability can be used to enhance the performance of many image and video processing applications. The idea is to use the high frame-rate capability to temporally oversample the scene and, thus, to obtain more accurate information about scene motion and illumination. This information is then used to improve the performance of image and standard frame-rate video applications. This paper investigates the use of temporal oversampling to improve the accuracy of optical flow estimation (OFE). A method for obtaining high accuracy optical flow estimates at a conventional standard frame rate, e.g., 30 frames/s, by first capturing and processing a high frame-rate version of the video is presented. The method uses the Lucas-Kanade algorithm to obtain optical flow estimates at a high frame rate, which are then accumulated and refined to estimate the optical flow at the desired standard frame rate. The method demonstrates significant improvements in OFE accuracy both on synthetically generated video sequences and on a real video sequence captured using an experimental high-speed imaging system. It is then shown that a key benefit of using temporal oversampling to estimate optical flow is the reduction in motion aliasing. Using sinusoidal input sequences, the reduction in motion aliasing is identified and the desired minimum sampling rate as a function of the velocity and spatial bandwidth of the scene is determined. Using both synthetic and real video sequences, it is shown that temporal oversampling improves OFE accuracy by reducing motion aliasing not only for areas with large displacements but also for areas with small displacements and high spatial frequencies. The use of other OFE algorithms with temporally oversampled video is then discussed. In particular, the Haussecker algorithm is extended to work with high frame-rate sequences. This extens- - ion demonstrates yet another important benefit of temporal oversampling, which is improving OFE accuracy when brightness varies with time. View full abstract»

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  • Minimal-memory bit-vector architecture for computational mathematical morphology using subspace projections

    Page(s): 1088 - 1095
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1248 KB) |  | HTML iconHTML  

    Computational mathematical morphology (CMM) is a nonlinear filter representation particularly amenable to real-time image processing. A windowed, translation-invariant filter is represented by a set of less-than-or-equal decisions that are executed by a parallel arrangement of comparators. In the state-of-the-art implementation, each pixel value of a windowed observation is indexed into separate lookup tables to retrieve a set of bit vectors which are "anded" together to produce a bit vector with a unique nonzero bit. The position of that bit is used to look up a filter value in a table. The number of stored bit vectors is proportional to the number of image gray levels. An architecture for CMM is presented that uses a minimal number of bit vectors so that required memory is less sensitive to the number of gray levels. The number of pixels in the observation window is the dimension of the image space. In the proposed architecture, basis elements are projected to subspaces of the image space and only bit vectors unique to each subspace are stored. Each projection corresponds to a subspace partition. Filter memory is greatly reduced by using intermediate lookup tables to map observations to unique bit vectors. We investigate two possible projection strategies: A fixed, singleton architecture, in which each subspace is one dimension, and a minimal architecture, in which a large number of subspace projections are searched for, one with minimal memory. Insensitivity to the number of gray levels is demonstrated through simulated, random-image space tessellations. We also present memory savings in a digital photocopier application. View full abstract»

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  • A stochastic method for Bayesian estimation of hidden Markov random field models with application to a color model

    Page(s): 1096 - 1108
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (950 KB) |  | HTML iconHTML  

    We propose a new stochastic algorithm for computing useful Bayesian estimators of hidden Markov random field (HMRF) models that we call exploration/selection/estimation (ESE) procedure. The algorithm is based on an optimization algorithm of O. Francois, called the exploration/selection (E/S) algorithm. The novelty consists of using the a posteriori distribution of the HMRF, as exploration distribution in the E/S algorithm. The ESE procedure computes the estimation of the likelihood parameters and the optimal number of region classes, according to global constraints, as well as the segmentation of the image. In our formulation, the total number of region classes is fixed, but classes are allowed or disallowed dynamically. This framework replaces the mechanism of the split-and-merge of regions that can be used in the context of image segmentation. The procedure is applied to the estimation of a HMRF color model for images, whose likelihood is based on multivariate distributions, with each component following a Beta distribution. Meanwhile, a method for computing the maximum likelihood estimators of Beta distributions is presented. Experimental results performed on 100 natural images are reported. We also include a proof of convergence of the E/S algorithm in the case of nonsymmetric exploration graphs. View full abstract»

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  • Figure-ground segmentation from occlusion

    Page(s): 1109 - 1124
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2421 KB) |  | HTML iconHTML  

    Layered video representations are increasingly popular; see for a recent review. Segmentation of moving objects is a key step for automating such representations. Current motion segmentation methods either fail to segment moving objects in low-textured regions or are computationally very expensive. This paper presents a computationally simple algorithm that segments moving objects, even in low-texture/low-contrast scenes. Our method infers the moving object templates directly from the image intensity values, rather than computing the motion field as an intermediate step. Our model takes into account the rigidity of the moving object and the occlusion of the background by the moving object. We formulate the segmentation problem as the minimization of a penalized likelihood cost function and present an algorithm to estimate all the unknown parameters: the motions, the template of the moving object, and the intensity levels of the object and of the background pixels. The cost function combines a maximum likelihood estimation term with a term that penalizes large templates. The minimization algorithm performs two alternate steps for which we derive closed-form solutions. Relaxation improves the convergence even when low texture makes it very challenging to segment the moving object from the background. Experiments demonstrate the good performance of our method. View full abstract»

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  • Domain decomposition for variational optical-flow computation

    Page(s): 1125 - 1137
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (918 KB) |  | HTML iconHTML  

    We present an approach to parallel variational optical-flow computation by using an arbitrary partition of the image plane and iteratively solving related local variational problems associated with each subdomain. The approach is particularly suited for implementations on PC clusters because interprocess communication is minimized by restricting the exchange of data to a lower dimensional interface. Our mathematical formulation supports various generalizations to linear/nonlinear convex variational approaches, three-dimensional image sequences, spatiotemporal regularization, and unstructured geometries and triangulations. Results concerning the effects of interface preconditioning, as well as runtime and communication volume measurements on a PC cluster, are presented. Our approach provides a major step toward real-time two-dimensional image processing using off-the-shelf PC hardware and facilitates the efficient application of variational approaches to large-scale image processing problems. View full abstract»

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  • A semiparametric model for accurate camera response function modeling and exposure estimation from comparametric data

    Page(s): 1138 - 1150
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    A fundamentally new approach that accurately estimates the camera response function from comparametric data, i.e., pixel data from two differently exposed images over a common field of view, is presented. It does so by solving for the camera response function from its associated comparametric relation. The approach offers several advantageous features, including having a complexity that is independent of the number of pixel data considered, allowing for the modeling of saturated pixels, enabling an inherently constrained optimization problem to be solved in an unconstrained manner, and the easy incorporation into an existing framework for joint image registration. This is accomplished by approximating the camera response function with a constrained piecewise linear model so that its solution, within the comparametric camera relation, can be obtained. This results in a semiparametric comparametric model, optimally determined from pixel data, which is directly parameterized in terms of the exposure parameter. Subsequently, it is shown how this semiparametric model is used for exposure estimation from captured images. Finally, we incorporate the semiparametric model within an existing and previously published framework for simultaneous and joint spatial and tonal image registration in order to illustrate the developed model's performance. View full abstract»

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  • Quantitative analysis of the factors that affect the determination of colocalization coefficients in dual-color confocal images

    Page(s): 1151 - 1158
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (461 KB) |  | HTML iconHTML  

    Colocalization analysis is a subject of great interest that allows us to determine those locations at which two or more types of molecules or structures are found simultaneously. In confocal dual-color images, in order to consider a given position as colocalized, the following two conditions must be satisfied: 1) the colors emitted by the molecules labeled with different fluorophores must occupy the same pixel in the image and 2) the intensities of each component of the image must be within a certain range. Since it is not straightforward to assess these two conditions without ambiguity, this can lead to either considering false colocalizations as positive, or, conversely, leaving out positive locations. In fact, at present, there is not a general procedure to determine the area of colocalization that contains those pixels that can be considered as really colocalized (i.e., the pixels for which the above conditions are fulfilled). As a result, it is the user who must decide on the appropriate selection which introduces personal bias. These issues and the guides for a computational procedure independent of the user that allows us to quantify the degree of colocalization in a dual-color system are discussed. View full abstract»

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  • Three-dimensional surface reconstruction from multistatic SAR images

    Page(s): 1159 - 1171
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2555 KB) |  | HTML iconHTML  

    This paper discusses reconstruction of three-dimensional surfaces from multiple bistatic synthetic aperture radar (SAR) images. Techniques for surface reconstruction from multiple monostatic SAR images already exist, including interferometric processing and stereo SAR. We generalize these methods to obtain algorithms for bistatic interferometric SAR and bistatic stereo SAR. We also propose a framework for predicting the performance of our multistatic stereo SAR algorithm, and, from this framework, we suggest a metric for use in planning strategic deployment of multistatic assets. View full abstract»

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  • Two-dimensional transforms for device color correction and calibration

    Page(s): 1172 - 1186
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1310 KB) |  | HTML iconHTML  

    Color device calibration is traditionally performed using one-dimensional (1-D) per-channel tone-response corrections (TRCs). While 1-D TRCs are attractive in view of their low implementation complexity and efficient real-time processing of color images, their use severely restricts the degree of control that can be exercised along various device axes. A typical example is that per separation (or per-channel), TRCs in a printer can be used to either ensure gray balance along the C=M=Y axis or to provide a linear response in delta-E units along each of the individual (C, M, and Y) axis, but not both. This paper proposes a novel two-dimensional color correction architecture that enables much greater control over the device color gamut with a modest increase in implementation cost. Results show significant improvement in calibration accuracy and stability when compared to traditional 1-D calibration. Superior cost quality tradeoffs (over 1-D methods) are also achieved for emulation of one color device on another. View full abstract»

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  • CLUE: cluster-based retrieval of images by unsupervised learning

    Page(s): 1187 - 1201
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    In a typical content-based image retrieval (CBIR) system, target images (images in the database) are sorted by feature similarities with respect to the query. Similarities among target images are usually ignored. This paper introduces a new technique, cluster-based retrieval of images by unsupervised learning (CLUE), for improving user interaction with image retrieval systems by fully exploiting the similarity information. CLUE retrieves image clusters by applying a graph-theoretic clustering algorithm to a collection of images in the vicinity of the query. Clustering in CLUE is dynamic. In particular, clusters formed depend on which images are retrieved in response to the query. CLUE can be combined with any real-valued symmetric similarity measure (metric or nonmetric). Thus, it may be embedded in many current CBIR systems, including relevance feedback systems. The performance of an experimental image retrieval system using CLUE is evaluated on a database of around 60,000 images from COREL. Empirical results demonstrate improved performance compared with a CBIR system using the same image similarity measure. In addition, results on images returned by Google's Image Search reveal the potential of applying CLUE to real-world image data and integrating CLUE as a part of the interface for keyword-based image retrieval systems. View full abstract»

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  • Parametric reconstruction of generalized cylinders from limb edges

    Page(s): 1202 - 1214
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2987 KB) |  | HTML iconHTML  

    The three-dimensional (3-D) reconstruction of generalized cylinders (GCs) is an important research field in computer vision. One of the main difficulties is that some contour features in images cannot be reconstructed by traditional stereovision because they do not correspond to reflectance discontinuities of surface in space. In this paper, we present a novel, parametric approach for the 3-D reconstruction of circular generalized cylinders (CGCs) only from the limb edges of CGCs in two images. Instead of exploiting the invariant and quasiinvariant properties of some specific subclasses of GCs in projections, our reconstruction is achieved by some general assumptions on GCs, and can, therefore, be applied to a broader subclass of GCs. In order to improve robustness, we perform the extraction and labeling of the limb edge interactively, and estimate the epipolar geometry between two images by an optimal algorithm. Then, for different types of GCs, three kinds of symmetries (parallel symmetry, skew symmetry, and local smooth symmetry) are employed to compute the symmetry of limb edges. The surface points corresponding to limb edges in images are reconstructed by integrating the recovered epipolar geometry and the properties induced from the assumptions that we make on the GCs. Finally, a homography-based method is exploited to further refine the 3-D description of the GC with a coplanar curved axis. View full abstract»

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  • Nonmetric calibration of camera lens distortion: differential methods and robust estimation

    Page(s): 1215 - 1230
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3029 KB) |  | HTML iconHTML  

    This paper addresses the problem of calibrating camera lens distortion, which can be significant in medium to wide angle lenses. Our approach is based on the analysis of distorted images of straight lines. We derive new distortion measures that can be optimized using nonlinear search techniques to find the best distortion parameters that straighten these lines. Unlike the other existing approaches, we also provide fast, closed-form solutions to the distortion coefficients. We prove that including both the distortion center and the decentering coefficients in the nonlinear optimization step may lead to instability of the estimation algorithm. Our approach provides a way to get around this, and, at the same time, it reduces the search space of the calibration problem without sacrificing the accuracy and produces more stable and noise-robust results. In addition, while almost all existing nonmetric distortion calibration methods needs user involvement in one form or another, we present a robust approach to distortion calibration based on the least-median-of-squares estimator. Our approach is, thus, able to proceed in a fully automatic manner while being less sensitive to erroneous input data such as image curves that are mistakenly considered projections of three-dimensional linear segments. Experiments to evaluate the performance of this approach on synthetic and real data are reported. View full abstract»

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  • IEEE Transactions on Image Processing Information for authors

    Page(s): 1231 - 1232
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  • IEEE Signal Processing Society Information

    Page(s): c3
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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