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Image Processing, IEEE Transactions on

Issue 5 • Date May 1996

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Displaying Results 1 - 14 of 14
  • Further results on MAP optimality and strong consistency of certain classes of morphological filters

    Publication Year: 1996 , Page(s): 762 - 764
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (340 KB)  

    Morphological openings and closings can be viewed as consistent MAP estimators of smooth random binary image signals immersed in i.i.d. clutter, or suffering from i.i.d. random dropouts. We revisit this viewpoint under much more general assumptions and show that, quite surprisingly, the above interpretation is still valid View full abstract»

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  • On the metric properties of discrete space-filling curves

    Publication Year: 1996 , Page(s): 794 - 797
    Cited by:  Papers (36)  |  Patents (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (388 KB)  

    A space-filling curve is a linear traversal of a discrete finite multidimensional space. In order for this traversal to be useful in many applications, the curve should preserve “locality”. We quantify “locality” and bound the locality of multidimensional space-filling curves. Classic Hilbert space-filling curves come close to achieving optimal locality View full abstract»

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  • One-pixel-wide closed boundary identification

    Publication Year: 1996 , Page(s): 780 - 783
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (504 KB)  

    An appropriate space of one-pixel-wide closed (OPWC) boundary configurations is explicitly defined and an automatic algorithm to obtain OPWC contour estimates from a segmented image is presented. The motivation is to obtain a reasonable starting estimate for a Markov chain Monte Carlo-based (McMC-based) boundary optimization algorithm View full abstract»

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  • An improved method for 2-D self-similar image synthesis

    Publication Year: 1996 , Page(s): 754 - 761
    Cited by:  Papers (26)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1156 KB)  

    We propose a new method called incremental Fourier synthesis to generate 2-D self-similar images based on a 2D fractional Brownian motion (fBm) model. With this method, the stationary increments of fBm are created by a Fourier synthesis method and the increments are added up to generate the nonstationary 2D fBm process. Since the new method takes advantage of the FFT, its computational complexity is only O(N2log2(N)), and its memory requirement is only O(N2) for a self-similar image of size N×N View full abstract»

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  • A fast and accurate Fourier algorithm for iterative parallel-beam tomography

    Publication Year: 1996 , Page(s): 740 - 753
    Cited by:  Papers (23)  |  Patents (9)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2728 KB)  

    We use a series-expansion approach and an operator framework to derive a new, fast, and accurate Fourier algorithm for iterative tomographic reconstruction. This algorithm is applicable for parallel-ray projections collected at a finite number of arbitrary view angles and radially sampled at a rate high enough that aliasing errors are small. The conjugate gradient (CG) algorithm is used to minimize a regularized, spectrally weighted least-squares criterion, and we prove that the main step in each iteration is equivalent to a 2-D discrete convolution, which can be cheaply and exactly implemented via the fast Fourier transform (FFT). The proposed algorithm requires O(N2logN) floating-point operations per iteration to reconstruct an N×N image from P view angles, as compared to O(N 2P) floating-point operations per iteration for iterative convolution-backprojection algorithms or general algebraic algorithms that are based on a matrix formulation of the tomography problem. Numerical examples using simulated data demonstrate the effectiveness of the algorithm for sparse- and limited-angle tomography under realistic sampling scenarios. Although the proposed algorithm cannot explicitly account for noise with nonstationary statistics, additional simulations demonstrate that for low to moderate levels of nonstationary noise, the quality of reconstruction is almost unaffected by assuming that the noise is stationary View full abstract»

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  • Automatic gradient threshold determination for edge detection

    Publication Year: 1996 , Page(s): 784 - 787
    Cited by:  Papers (20)  |  Patents (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (480 KB)  

    We describe a method to automatically find gradient thresholds to separate edge from nonedge pixels. A statistical model that is the weighted sum of two gamma densities corresponding to edge and nonedge pixels is used to identify a threshold. Results closely match human perceptual thresholds even under low signal-to-noise ratio (SNR) levels View full abstract»

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  • Speeding up the generalized adaptive neural filters

    Publication Year: 1996 , Page(s): 705 - 712
    Cited by:  Papers (7)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1176 KB)  

    A new class of adaptive filters called generalized adaptive neural filters (GANFs) has emerged. They share many things in common with stack filters and include all stack filters as a subset. The GANFs allow a very efficient hardware implementation once they are trained. However, the training process can be slow. This paper discusses structural modifications to allow for faster training. In addition, these modifications can lead to an increase in the filter's robustness, given a limited amount of training data. This paper does not attempt to justify use of a GANF; it only presents an alternative implementation of the filter. To verify the results, several simulations were performed by corrupting two images with varying amounts of mixture noise and Gaussian noise View full abstract»

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  • Frame representations for texture segmentation

    Publication Year: 1996 , Page(s): 771 - 780
    Cited by:  Papers (37)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1464 KB)  

    We introduce a novel method of feature extraction for texture segmentation that relies on multichannel wavelet frames and 2-D envelope detection. We describe and compare two algorithms for envelope detection based on (1) the Hilbert transform and (2) zero crossings. We present criteria for filter selection and discuss quantitatively their effect on feature extraction. The performance of our method is demonstrated experimentally on samples of both natural and synthetic textures View full abstract»

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  • Block-iterative methods for image reconstruction from projections

    Publication Year: 1996 , Page(s): 792 - 794
    Cited by:  Papers (87)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (320 KB)  

    The simultaneous MART algorithm (SMART) and the expectation maximization method for likelihood maximization (EMML) are extended to block-iterative versions, BI-SMART and BI-EMML, that converge to a solution in the feasible case, for any choice of subsets. The BI-EMML reduces to the “ordered subset” EMML of Hudson et al. (1992, 1994) when their “subset balanced” property holds View full abstract»

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  • On the performance of linear phase wavelet transforms in low bit-rate image coding

    Publication Year: 1996 , Page(s): 689 - 704
    Cited by:  Papers (30)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1472 KB)  

    The behavior of linear phase wavelet transforms in low bit-rate image coding is investigated. The influence of certain characteristics of these transforms such as regularity, number of vanishing moments, filter length, coding gain, frequency selectivity, and the shape of the wavelets on the coding performance is analyzed. The wavelet transforms performance is assessed based on a first-order Markov source and on the image quality, using subjective tests. More than 20 wavelet transforms of a test image were coded with a product code lattice quantizer with the image quality rated by different viewers. The results show that, as long as the wavelet transforms perform reasonably well, features like regularity and number of vanishing moments do not have any important impact on final image quality. The influence of the coding gain by itself is also small. On the other hand, the shape of the synthesis wavelet, which determines the visibility of coding errors on reconstructed images, is very important. Analysis of the data obtained strongly suggests that the design of good wavelet transforms for low bit-rate image coding should take into account chiefly the shape of the synthesis wavelet and, to a lesser extent, the coding View full abstract»

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  • Optical flow computation using extended constraints

    Publication Year: 1996 , Page(s): 720 - 739
    Cited by:  Papers (16)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2092 KB)  

    Several approaches for optical flow estimation use partial differential equations to model changes in image brightness throughout time. A commonly used equation is the so-called optical flow constraint (OFC), which assumes that the image brightness is stationary with respect to time. More recently, a different constraint referred to as the extended optical flow constraint (EOFC) has been introduced, which also contains the divergence of the flow field of image brightness. There is no agreement in the literature about which of these constraints provides the best estimation of the velocity field. Two new solutions for optical flow computation are proposed, which are based on an approximation of the constraint equations. The two techniques have been used with both EOFC and OFC constraint equations. Results achieved by using these solutions have been compared with several well-known computational methods for optical flow estimation in different motion conditions. Estimation errors have also been measured and compared for different types of motion View full abstract»

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  • Optimal detection and estimation of straight patterns

    Publication Year: 1996 , Page(s): 787 - 792
    Cited by:  Papers (12)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1476 KB)  

    This correspondence illustrates the optimal detector and slope estimator of straight patterns. In particular, it is recognized that the output of the likelihood processor, constituted by the Radon transform and a whitening filter, provides a sufficient statistic for both problems of signal detection as well as orientation and offset estimation View full abstract»

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  • Image enhancement using the modified ICM method

    Publication Year: 1996 , Page(s): 765 - 771
    Cited by:  Papers (10)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1036 KB)  

    A generalized version of the iterative conditional modes (ICM) method for image enhancement is developed. The proposed algorithm utilizes the characteristic of Markov random fields (MRF) in modeling the contextual information embedded in image formation. To cope with real images, a new local MRF model with a second-order neighborhood is introduced. This model extracts contextual information not only from the intensity levels but also from the relative position of neighboring cliques. Also, an outlier rejection method is presented. In this method, the rejection depends on each candidate's contribution to the local variance. To cope with a mixed noise case, a hypothesis test is implemented as part of the restoration procedure. The proposed algorithm performs signal adaptive, nonlinear, and recursive filtering. In comparing the performance of the new procedure with several well-known order statistic filters, the superiority of the proposed algorithm is demonstrated both in the mean-square-error (MSE) and the mean-absolute-error (MAE) senses. In addition, the new algorithm preserves the details of the images well. It should be noted that the blurring effect is not considered View full abstract»

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  • Lossless compression of AVIRIS images

    Publication Year: 1996 , Page(s): 713 - 719
    Cited by:  Papers (41)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (760 KB)  

    Adaptive DPCM methods using linear prediction are described for the lossless compression of hyperspectral (224-band) images recorded by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). The methods have two stages-predictive decorrelation (which produces residuals) and residual encoding. Good predictors are described, whose performance closely approaches limits imposed by sensor noise. It is imperative that these predictors make use of the high spectral correlations between bands. The residuals are encoded using variable-length coding (VLC) methods, and compression is improved by using eight codebooks whose design depends on the sensor's noise characteristics. Rice (1979) coding has also been evaluated; it loses 0.02-0.05 b/pixel compression compared with better VLC methods but is much simpler and faster. Results for compressing ten AVIRIS images are reported 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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Editor-in-Chief
Scott Acton
University of Virginia
Charlottesville, VA, USA
E-mail: acton@virginia.edu 
Phone: +1 434-982-2003