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Image Processing, IEEE Transactions on

Issue 11 • Date Nov. 2000

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Displaying Results 1 - 18 of 18
  • Comments on "modified K-means algorithm for vector quantizer design"

    Page(s): 1964 - 1967
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    Previously a modified K-means algorithm for vector quantization design has been proposed where the codevector updating step is as follows: new codevector=current codevector+scale factor (new centroid-current codevector). This algorithm uses a fixed value for the scale factor. In this paper, we propose the use of a variable scale factor which is a function of the iteration number. For the vector quantization of image data, we show that it offers faster convergence than the modified K-means algorithm with a fixed scale factor, without affecting the optimality of the codebook. View full abstract»

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  • A wavelet-frame based image force model for active contouring algorithms

    Page(s): 1983 - 1988
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    This paper proposes a directional image force (DIF) for active contouring. DIF is the inner product of the zero crossing strength (ZCS) of wavelet frame coefficients, and the normal of a snake, by representing strength and orientation of edges at multiple resolution levels. DIF markedly improves the immunity of snakes to noise and convexity View full abstract»

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  • Fast eigenspace decomposition of correlated images

    Page(s): 1937 - 1949
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    We present a computationally efficient algorithm for the eigenspace decomposition of correlated images. Our approach is motivated by the fact that for a planar rotation of a two-dimensional (2-D) image, analytical expressions can be given for the eigendecomposition, based on the theory of circulant matrices. These analytical expressions turn out to be good first approximations of the eigendecomposition, even for three-dimensional (3-D) objects rotated about a single axis. In addition, the theory of circulant matrices yields good approximations to the eigendecomposition for images that result when objects are translated and scaled. We use these observations to automatically determine the dimension of the subspace required to represent an image with a guaranteed user-specified accuracy, as well as to quickly compute a basis for the subspace. Examples show that the algorithm performs very well on a number of test cases ranging from images of 3-D objects rotated about a single axis to arbitrary video sequences View full abstract»

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  • Nonlinear multiresolution signal decomposition schemes. I. Morphological pyramids

    Page(s): 1862 - 1876
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    Interest in multiresolution techniques for signal processing and analysis is increasing steadily. An important instance of such a technique is the so-called pyramid decomposition scheme. This paper presents a general theory for constructing linear as well as nonlinear pyramid decomposition schemes for signal analysis and synthesis. The proposed theory is based on the following ingredients: 1) the pyramid consists of a (finite or infinite) number of levels such that the information content decreases toward higher levels and 2) each step toward a higher level is implemented by an (information-reducing) analysis operator, whereas each step toward a lower level is implemented by an (information-preserving) synthesis operator. One basic assumption is necessary: synthesis followed by analysis yields the identity operator, meaning that no information is lost by these two consecutive steps. Several examples of pyramid decomposition schemes are shown to be instances of the proposed theory: a particular class of linear pyramids, morphological skeleton decompositions, the morphological Haar pyramid, median pyramids, etc. Furthermore, the paper makes a distinction between single-scale and multiscale decomposition schemes, i.e., schemes without or with sample reduction. Finally, the proposed theory provides the foundation of a general approach to constructing nonlinear wavelet decomposition schemes and filter banks View full abstract»

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  • Spatially variant apodization for image reconstruction from partial Fourier data

    Page(s): 1914 - 1925
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    Sidelobe artifacts are a common problem in image reconstruction from finite-extent Fourier data. Conventional shift-invariant windows reduce sidelobe artifacts only at the expense of worsened mainlobe resolution. Spatially variant apodization (SVA) was previously introduced as a means of reducing sidelobe artifacts, while preserving mainlobe resolution. Although the algorithm has been shown to be effective in synthetic aperture radar (SAR), it is heuristically motivated and it has received somewhat limited analysis. In this paper, we show that SVA is a version of minimum-variance spectral estimation (MVSE). We then present a complete development of the four types of two-dimensional SVA for image reconstruction from partial Fourier data. We provide simulation results for various real-valued and complex-valued targets and point out some of the limitations of SVA. Performance measures are presented to help further evaluate the effectiveness of SVA View full abstract»

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  • Blind identification of multichannel FIR blurs and perfect image restoration

    Page(s): 1877 - 1896
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    Despite its practical importance in image processing and computer vision, blind blur identification and blind image restoration have so far been addressed under restrictive assumptions such as all-pole stationary image models blurred by zero or minimum-phase point-spread functions. Relying upon diversity (availability of a sufficient number of multiple blurred images), we develop blind FIR blur identification and order determination schemes. Apart from a minimal persistence of the excitation condition (also present with nonblind setups), the inaccessible input image is allowed to be deterministic or random and of unknown color of distribution. With the blurs satisfying a certain co-primeness condition in addition, we establish existence and uniqueness results which guarantee that single input/multiple-output FIR blurred images can be restored blindly, though perfectly in the absence of noise, using linear FIR filters. Results of simulations employing the blind order determination, blind blur identification, and blind image restoration algorithms are presented. When the SNR is high, direct image restoration is found to yield better results than indirect image restoration which employs the estimated blurs. In low SNR, indirect image restoration performs well while the direct restoration results vary with the delay but improve with larger equalizer orders View full abstract»

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  • An analysis of some common scanning techniques for lossless image coding

    Page(s): 1837 - 1848
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    Though most image coding techniques use a raster scan to order pixels prior to coding, Hilbert and other scans have been proposed as having better performance due to their superior locality preserving properties. However, a general understanding of the merits of various scans has been lacking. This paper develops an approach for quantitatively analyzing the effect of pixel scan order for context-based, predictive lossless image compression and uses it to compare raster, Hilbert, random and hierarchical scans. Specifically, for a quantized-Gaussian image model and a given scan order, it shows how the encoding rate can be estimated from the frequencies with which various pixel configurations are available as previously scanned contexts, and from the corresponding conditional differential entropies. Formulas are derived for such context frequencies and entropies. Assuming an isotropic image model and contexts consisting of previously scanned adjacent pixels, it is found that the raster scan is better than the Hilbert scan which is often used in compression applications due to its locality preserving properties. The hierarchical scan is better still, though it is based on nonadjacent contexts. The random scan is the worst of the four considered. Extensions and implications of the results to lossy coding are also discussed View full abstract»

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  • A dual interpretation for direct binary search and its implications for tone reproduction and texture quality

    Page(s): 1950 - 1963
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    The direct binary search (DBS) algorithm employs a search heuristic to minimize the mean-squared perceptually filtered error between the halftone and continuous-tone original images. Based on an efficient method for evaluating the effect on the mean squared error of trial changes to the halftone image, we show that DBS also minimizes in a pointwise sense the absolute error under the same visual model, but at twice the viewing distance associated with the mean-squared error metric. This dual interpretation sheds light on the convergence properties of the algorithm, and clearly explains the tone bias that has long been observed with halftoning algorithms of this type. It also demonstrates how tone bias and texture quality are linked via the scale parameter, the product of printer resolution and viewing distance. Finally, we show how the tone bias can be eliminated by tone-correcting the continuous-tone image prior to halftoning it View full abstract»

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  • A level-set approach to image blending

    Page(s): 1849 - 1861
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    This paper presents a novel method for blending images. Image blending refers to the process of creating a set of discrete samples of a continuous, one-parameter family of images that connects a pair of input images. Image blending has uses in a variety of computer graphics and image processing applications. In particular, it ran be used for image morphing, which is a method for creating video streams that depict transformations of objects in scenes based solely on pairs of images and sets of user-defined fiducial points. Image blending also has applications for video compression and image-based rendering. The proposed method for image blending relies on the progressive minimization of a difference metric which compares the level sets between two images. This strategy results in an image blend which is the solution of a pair of coupled, nonlinear, first-order partial differential equations that model multidimensional level-set propagations. When compared to interpolation this method produces more natural appearances of motion because it manipulates the shapes of image contours rather than simply interpolating intensity values. This strategy results in a process that has the qualitative property of deforming greyscale objects in images rather than producing a simple fade from one object to another. This paper presents the mathematics that underlie this new method, a numerical implementation, and results on real images that demonstrate its effectiveness View full abstract»

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  • Refined moment calculation using image block representation

    Page(s): 1977 - 1978
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    This paper deals with effective calculation of moments of binary images. Recently, Spiliotis and Mertzios (see ibid., vol.7, p.1609-15, Nov. 1998) published a method based on image block representation (IBR). We propose a refinement of their method which yields exact results and performs even more effectively View full abstract»

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  • Self-affine mapping system and its application to object contour extraction

    Page(s): 1926 - 1936
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    A self-affine mapping system which has conventionally been used to produce fractal images is used to fit rough lines to contours. The self-affine map's parameters are detected by analyzing the blockwise self-similarity of a grayscale image using a simplified algorithm in fractal encoding. The phenomenon that edges attract mapping points in self-affine mapping is utilized in the proposed method. The boundary of the foreground region of an alpha mask is fitted by mapping iterations of the region. It is shown that the proposed method accurately produces not only smooth curves but also sharp corners, and has the ability to extract both distinct edges and blurred edges using the same parameter. It is also shown that even large gaps between the hand-drawn line and the contour can be fitted well by the recursive procedure of the proposed algorithm, in which the block size is progressively decreased. These features reduce the time required for drawing contours by hand View full abstract»

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  • Morphological text extraction from images

    Page(s): 1978 - 1983
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    This paper presents a morphological technique for text extraction from images. The proposed morphological technique is insensitive to noise, skew and text orientation. It is also free from artifacts that are usually introduced by both fixed/optimal global thresholding and fixed-size block-based local thresholding. Examples are presented to illustrate the performance of the proposed method View full abstract»

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  • Image data compression using cubic convolution spline interpolation

    Page(s): 1988 - 1995
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    A new cubic convolution spline interpolation (CCSI )for both one-dimensional (1-D) and two-dimensional (2-D) signals is developed in order to subsample signal and image compression data. The CCSI yields a very accurate algorithm for smoothing. It is also shown that this new and fast smoothing filter for CCSI can be used with the JPEG standard to design an improved JPEG encoder-decoder for a high compression ratio View full abstract»

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  • Scalable image coding using reversible integer wavelet transforms

    Page(s): 1972 - 1977
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    Reversible integer wavelet transforms allow both lossless and lossy decoding using a single bitstream. We present a new fully scalable image coder and investigate the lossless and lossy performance of these transforms in the proposed coder. The lossless compression performance of the presented method is comparable to JPEG-LS. The lossy performance is quite competitive with other efficient lossy compression methods View full abstract»

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  • Nonlinear multiresolution signal decomposition schemes. II. Morphological wavelets

    Page(s): 1897 - 1913
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    For pt.I see ibid., vol.9, no.11, p.1862-76 (2000). In its original form, the wavelet transform is a linear tool. However, it has been increasingly recognized that nonlinear extensions are possible. A major impulse to the development of nonlinear wavelet transforms has been given by the introduction of the lifting scheme by Sweldens (1995, 1996, 1998). The aim of this paper, which is a sequel to a previous paper devoted exclusively to the pyramid transform, is to present an axiomatic framework encompassing most existing linear and nonlinear wavelet decompositions. Furthermore, it introduces some, thus far unknown, wavelets based on mathematical morphology, such as the morphological Haar wavelet, both in one and two dimensions. A general and flexible approach for the construction of nonlinear (morphological) wavelets is provided by the lifting scheme. This paper briefly discusses one example, the max-lifting scheme, which has the intriguing property that preserves local maxima in a signal over a range of scales, depending on how local or global these maxima are View full abstract»

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  • A least-squares-based 2-D filtering scheme for stereo image compression

    Page(s): 1967 - 1972
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    A two-dimensional (2-D) least squares (LS)-based filtering scheme for high fidelity stereo image compression applications is introduced in this correspondence. This method removes the effects of mismatching in a stereo image pair by applying the left image as the reference input to a 2-D transversal filter while the right image is used as the desired output. The weights of the filter are computed using a block-based LS method. A reduced order filtering scheme is also introduced to find the optimum number of filter coefficients. The principal coefficients and the disparity vectors are used together with left image to reconstruct the right image at the receiver, The proposed schemes are examined on a real stereo image pair for 3D-TV applications and the results were benchmarked against those of the block-matching method View full abstract»

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  • Lossless quantization of Hadamard transform coefficients

    Page(s): 1995 - 1999
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    This paper shows that an n×1 integer vector can be exactly recovered from its Hadamard transform coefficients, even when 0.5n log 2(n) of the (less significant) bits of these coefficients are removed. The paper introduces a fast “lossless” dequantization algorithm for this purpose. To investigate the usefulness of the procedure in data compression, the paper investigates an embedded block image coding technique called the “LHAD” based on the algorithm. The results show that lossless compression ratios close to the state of the art can be achieved, but that techniques such as CALIC and S+P still perform better View full abstract»

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  • A new dynamic finite-state vector quantization algorithm for image compression

    Page(s): 1825 - 1836
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    The picture quality of conventional memory vector quantization techniques is limited by their supercodebooks. This paper presents a new dynamic finite-state vector quantization (DFSVQ) algorithm which provides better quality than the best quality that the supercodebook can offer. The new DFSVQ exploits the global interblock correlation of image blocks instead of local correlation in conventional DFSVQs. For an input block, we search the closest block from the previously encoded data using the side-match technique. The closest block is then used as the prediction of the input block, or used to generate a dynamic codebook. The input block is encoded by the closest block, dynamic codebook or supercodebook. Searching for the closest block from the previously encoded data is equivalent to expand the codevector space; thus the picture quality achieved is not limited by the supercodebook. Experimental results reveal that the new DFSVQ reduces bit rate significantly and provides better visual quality, as compared to the basic VQ and other DFSVQs 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