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Flavio R. Dias Velasco

Bio: Flavio R. Dias Velasco is an academic researcher. The author has contributed to research in topics: Thresholding & Texture filtering. The author has an hindex of 4, co-authored 5 publications receiving 180 citations.

Papers
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ReportDOI
TL;DR: It is proved that in one dimension, ISODATA always converges, and this algorithm is applied to requantize images into specified numbers of gray levels.
Abstract: : A recently proposed iterative thresholding scheme turns out to be essentially the well-known ISODATA clustering algorithm, applied to a one- dimensional feature space (the sole feature of a pixel is its gray level). We prove that in one dimension, ISODATA always converges. We also apply it to requantize images into specified numbers of gray levels.

103 citations

ReportDOI
01 Mar 1979
TL;DR: A collection of new techniques for image smoothing involve averaging over half-neighborhoods, weighted averaging, and averaging based on local property probabilities.
Abstract: : This report documents a collection of new techniques for image smoothing, and gives examples of their performance. The techniques involve averaging over half-neighborhoods, weighted averaging, and averaging based on local property probabilities.

59 citations

Journal ArticleDOI
01 May 1981
TL;DR: Three simple methods of extracting texture primitives are compared and it appears that the simplest of these, thresholding at a fixed percentile, yields primitives that are quite effective in texture discrimination, using a set of simple properties of the primitives.
Abstract: Three simple methods of extracting texture primitives are compared. It appears that the simplest of these, thresholding at a fixed percentile, yields primitives that are quite effective in texture discrimination, using a set of simple properties of the primitives. Second-order statistics of these properties were also computed for pairs of neighboring primitives, using several definitions of "neighboring." In some cases, textures not discriminable using first-order statistics can be discriminated using statistics of the second order.

18 citations

Journal ArticleDOI
01 Aug 1979
TL;DR: An extension of the discrete relaxation operator is proposed which can be applied to waveforms with ambiguous segmentations using just one relaxation network.
Abstract: In order to apply relaxation-like methods to waveforms with ambiguous segmentations one has to consider all possible segmentations separately. An extension of the discrete relaxation operator is proposed which can be applied to such waveforms using just one relaxation network. Examples of the application of the method are also given.

5 citations

ReportDOI
01 Apr 1979
TL;DR: Three simple methods of extracting texture primitives are compared and it appears that the simplest, thresholding at a fixed percentile, yields primitives that are quite effective in texture discrimination.
Abstract: : Three simple methods of extracting texture primitives are compared. It appears that the simplest of these, thresholding at a fixed percentile, yields primitives that are quite effective in texture discrimination. (Author)

1 citations


Cited by
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Journal ArticleDOI
TL;DR: 40 selected thresholding methods from various categories are compared in the context of nondestructive testing applications as well as for document images, and the thresholding algorithms that perform uniformly better over nonde- structive testing and document image applications are identified.
Abstract: We conduct an exhaustive survey of image thresholding methods, categorize them, express their formulas under a uniform notation, and finally carry their performance comparison. The thresholding methods are categorized according to the information they are exploiting, such as histogram shape, measurement space clustering, entropy, object attributes, spatial correlation, and local gray-level surface. 40 selected thresholding methods from various categories are compared in the context of nondestructive testing applications as well as for document images. The comparison is based on the combined performance measures. We identify the thresholding algorithms that perform uniformly better over nonde- structive testing and document image applications. © 2004 SPIE and IS&T. (DOI: 10.1117/1.1631316)

4,543 citations

Journal ArticleDOI
TL;DR: The texture analysis methods being used at present are reviewed and statistical as well as structural approaches are included and their performances are compared.
Abstract: In this paper the texture analysis methods being used at present are reviewed. Statistical as well as structural approaches are included and their performances are compared. Concerning the former approach, the gray level difference method, filter mask texture measures, Fourier power spectrum analysis, cooccurrence features, gray level run lengths, autocorrelation features, methods derived from texture models, relative extrema measures, and gray level profiles are discussed. Structural methods which describe texture by its primitives and some placement rules are treated as well. Attention has to be paid to some essential preprocessing steps and to the influence of rotation and scale on the texture analysis methods. Finally the problem of texture segmentation is briefly discussed.

440 citations

Journal ArticleDOI
TL;DR: Based upon estimates of the short length scale spatial covariance of the image, a method utilizing indicator kriging to complete the image segmentation is developed.
Abstract: We consider the problem of segmenting a digitized image consisting of two univariate populations. Assume a priori knowledge allows incomplete assignment of voxels in the image, in the sense that a fraction of the voxels can be identified as belonging to population II/sub 0/, a second fraction to II/sub 1/, and the remaining fraction have no a priori identification. Based upon estimates of the short length scale spatial covariance of the image, we develop a method utilizing indicator kriging to complete the image segmentation.

428 citations

Journal ArticleDOI
TL;DR: It is demonstrated that features extracted from multiresolution representations can provide an adaptive mechanism for accomplishing local contrast enhancement and by improving the visualization of breast pathology, one can improve chances of early detection while requiring less time to evaluate mammograms for most patients.
Abstract: Introduces a novel approach for accomplishing mammographic feature analysis by overcomplete multiresolution representations. The authors show that efficient representations may be identified within a continuum of scale-space and used to enhance features of importance to mammography. Methods of contrast enhancement are described based on three overcomplete multiscale representations: 1) the dyadic wavelet transform (separable), 2) the /spl phi/-transform (nonseparable, nonorthogonal), and 3) the hexagonal wavelet transform (nonseparable). Multiscale edges identified within distinct levels of transform space provide local support for image enhancement. Mammograms are reconstructed from wavelet coefficients modified at one or more levels by local and global nonlinear operators. In each case, edges and gain parameters are identified adaptively by a measure of energy within each level of scale-space. The authors show quantitatively that transform coefficients, modified by adaptive nonlinear operators, can make more obvious unseen or barely seen features of mammography without requiring additional radiation. The authors' results are compared with traditional image enhancement techniques by measuring the local contrast of known mammographic features. They demonstrate that features extracted from multiresolution representations can provide an adaptive mechanism for accomplishing local contrast enhancement. By improving the visualization of breast pathology, one can improve chances of early detection while requiring less time to evaluate mammograms for most patients. >

382 citations

Journal ArticleDOI
TL;DR: A PDE-based level set method that needs to minimize a smooth convex functional under a quadratic constraint, and shows numerical results using the method for segmentation of digital images.
Abstract: In this paper, we propose a PDE-based level set method. Traditionally, interfaces are represented by the zero level set of continuous level set functions. Instead, we let the interfaces be represented by discontinuities of piecewise constant level set functions. Each level set function can at convergence only take two values, i.e., it can only be 1 or -1; thus, our method is related to phase-field methods. Some of the properties of standard level set methods are preserved in the proposed method, while others are not. Using this new method for interface problems, we need to minimize a smooth convex functional under a quadratic constraint. The level set functions are discontinuous at convergence, but the minimization functional is smooth. We show numerical results using the method for segmentation of digital images.

382 citations