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Author

Azriel Rosenfeld

Other affiliations: Meiji University
Bio: Azriel Rosenfeld is an academic researcher from University of Maryland, College Park. The author has contributed to research in topics: Image processing & Feature detection (computer vision). The author has an hindex of 94, co-authored 595 publications receiving 49426 citations. Previous affiliations of Azriel Rosenfeld include Meiji University.


Papers
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Journal ArticleDOI
TL;DR: It is shown that if P satisfies a property called strong normality (SN), and deletion of P preserves the topology of N P (P) , then P is simple.

7 citations

Proceedings ArticleDOI
25 Aug 1996
TL;DR: This paper first uses a local nonlinear operator to detect pixels whose neighborhoods are line-like, and then applies (robust) estimation techniques to find sets of such pixels that lie on, or near straight or circular loci.
Abstract: This paper treats the problem of detecting straight or circular pieces of road in noisy aerial images. It first uses a local nonlinear operator to detect pixels whose neighborhoods are line-like, and then applies (robust) estimation techniques to find sets of such pixels that lie on, or near straight or circular loci. An (unbiased) ordinary least squares estimator cannot handle outlying data; on the other hand, conventional robust techniques for fitting circular arcs are severely affected by digitization effects and the fact that road circular segments are typically short and shallow. We therefore introduce an estimator that is both robust and statistically efficient.

7 citations

01 Jan 2003
TL;DR: In this article, an accurate optical flow estimation algorithm is proposed by combining the 3D structure tensor with a parametric flow model, which is converted to a generalized eigenvalue problem.
Abstract: An accurate optical flow estimation algorithm is proposed in this paper. By combining the three-dimensional (3-D) structure tensor with a parametric flow model, the optical flow estimation problem is converted to a generalized eigenvalue problem. The optical flow can be accurately estimated from the generalized eigenvectors. The confidence measure derived from the generalized eigenvalues is used to adaptively adjust the coherent motion region to further improve the accuracy. Experi- ments using both synthetic sequences with ground truth and real sequences illustrate our method. Comparisons with classical and recently published methods are also given to demonstrate the accuracy of our algorithm. technique by using the affine motion model. He defined the tensor by projecting the image onto a second-degree polyno- mial and integrating the affine model into the tensor. The affine parameters were solved as a linear system. Based on an idea used in (4), we derive the 3-D structure tensor technique in a different way. We show that when we use a parametric motion model to construct the 3-D structure tensor, the motion parameters and subsequently the optical flow can be found using generalized eigenvalue analysiswithout actually solving a linear system. This is demonstrated by using an affine motion model as an example. Since a reliable confidence mea- sure can be derived from the generalized eigenvalues, it can be used to dynamically adjust the neighborhood to include a wider area of coherent motion, so that the estimated flow is more ac- curate and robust to the aperture problem. Here the coherence is in terms of the parametric motion model instead of the optical flow. The rest of this paper is organized as follows. Section II for- mulates the optical flow estimation problem as a generalized eigenvalue problem. Section III analyzes the relationship be- tween generalized eigenvalues/eigenvectors and optical/normal flow. A confidence measure, which is then used to guide the neighborhood adjustment, is also defined in terms of the gen- eralized eigenvalues. Experimental results using synthetic and real image sequences are provided in Section IV. The results are compared to a few classical methods and some recently pub- lished methods. Section V gives conclusions.

7 citations

ReportDOI
01 Dec 1980
TL;DR: In this paper, a tutorial paper on the subjects of digital straightness and convexity is presented, where the central questions treated are: When can a digital arc be the digitization of a real straight line segment? When can an object be the digital object in a real convex set?
Abstract: : This tutorial paper reviews the subjects of digital straightness and convexity. The central questions treated are: When can a digital arc be the digitization of a real straight line segment? When can a digital object be the digitization of a real convex set?

7 citations

Proceedings ArticleDOI
05 Jun 1988
TL;DR: A benchmark is presented that was designed to evaluate the merits of various parallel architectures as applied to image understanding (IU) to gain a better understanding of vision architecture requirements, which can be used to guide the development of the next generation of vision architectures.
Abstract: A benchmark is presented that was designed to evaluate the merits of various parallel architectures as applied to image understanding (IU). This benchmark exercise addresses the issue of system performance on an integrated set of tasks, where the task interactions that are typical of complex vision application are present. The goal of this exercise is to gain a better understanding of vision architecture requirements, which can be used to guide the development of the next generation of vision architectures. >

7 citations


Cited by
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Journal ArticleDOI
TL;DR: There is a natural uncertainty principle between detection and localization performance, which are the two main goals, and with this principle a single operator shape is derived which is optimal at any scale.
Abstract: This paper describes a computational approach to edge detection. The success of the approach depends on the definition of a comprehensive set of goals for the computation of edge points. These goals must be precise enough to delimit the desired behavior of the detector while making minimal assumptions about the form of the solution. We define detection and localization criteria for a class of edges, and present mathematical forms for these criteria as functionals on the operator impulse response. A third criterion is then added to ensure that the detector has only one response to a single edge. We use the criteria in numerical optimization to derive detectors for several common image features, including step edges. On specializing the analysis to step edges, we find that there is a natural uncertainty principle between detection and localization performance, which are the two main goals. With this principle we derive a single operator shape which is optimal at any scale. The optimal detector has a simple approximate implementation in which edges are marked at maxima in gradient magnitude of a Gaussian-smoothed image. We extend this simple detector using operators of several widths to cope with different signal-to-noise ratios in the image. We present a general method, called feature synthesis, for the fine-to-coarse integration of information from operators at different scales. Finally we show that step edge detector performance improves considerably as the operator point spread function is extended along the edge.

28,073 citations

Journal ArticleDOI
01 Nov 1973
TL;DR: These results indicate that the easily computable textural features based on gray-tone spatial dependancies probably have a general applicability for a wide variety of image-classification applications.
Abstract: Texture is one of the important characteristics used in identifying objects or regions of interest in an image, whether the image be a photomicrograph, an aerial photograph, or a satellite image. This paper describes some easily computable textural features based on gray-tone spatial dependancies, and illustrates their application in category-identification tasks of three different kinds of image data: photomicrographs of five kinds of sandstones, 1:20 000 panchromatic aerial photographs of eight land-use categories, and Earth Resources Technology Satellite (ERTS) multispecial imagery containing seven land-use categories. We use two kinds of decision rules: one for which the decision regions are convex polyhedra (a piecewise linear decision rule), and one for which the decision regions are rectangular parallelpipeds (a min-max decision rule). In each experiment the data set was divided into two parts, a training set and a test set. Test set identification accuracy is 89 percent for the photomicrographs, 82 percent for the aerial photographic imagery, and 83 percent for the satellite imagery. These results indicate that the easily computable textural features probably have a general applicability for a wide variety of image-classification applications.

20,442 citations

Journal ArticleDOI
TL;DR: In this paper, it is shown that the difference of information between the approximation of a signal at the resolutions 2/sup j+1/ and 2 /sup j/ (where j is an integer) can be extracted by decomposing this signal on a wavelet orthonormal basis of L/sup 2/(R/sup n/), the vector space of measurable, square-integrable n-dimensional functions.
Abstract: Multiresolution representations are effective for analyzing the information content of images. The properties of the operator which approximates a signal at a given resolution were studied. It is shown that the difference of information between the approximation of a signal at the resolutions 2/sup j+1/ and 2/sup j/ (where j is an integer) can be extracted by decomposing this signal on a wavelet orthonormal basis of L/sup 2/(R/sup n/), the vector space of measurable, square-integrable n-dimensional functions. In L/sup 2/(R), a wavelet orthonormal basis is a family of functions which is built by dilating and translating a unique function psi (x). This decomposition defines an orthogonal multiresolution representation called a wavelet representation. It is computed with a pyramidal algorithm based on convolutions with quadrature mirror filters. Wavelet representation lies between the spatial and Fourier domains. For images, the wavelet representation differentiates several spatial orientations. The application of this representation to data compression in image coding, texture discrimination and fractal analysis is discussed. >

20,028 citations

Journal ArticleDOI
TL;DR: The analogy between images and statistical mechanics systems is made and the analogous operation under the posterior distribution yields the maximum a posteriori (MAP) estimate of the image given the degraded observations, creating a highly parallel ``relaxation'' algorithm for MAP estimation.
Abstract: We make an analogy between images and statistical mechanics systems. Pixel gray levels and the presence and orientation of edges are viewed as states of atoms or molecules in a lattice-like physical system. The assignment of an energy function in the physical system determines its Gibbs distribution. Because of the Gibbs distribution, Markov random field (MRF) equivalence, this assignment also determines an MRF image model. The energy function is a more convenient and natural mechanism for embodying picture attributes than are the local characteristics of the MRF. For a range of degradation mechanisms, including blurring, nonlinear deformations, and multiplicative or additive noise, the posterior distribution is an MRF with a structure akin to the image model. By the analogy, the posterior distribution defines another (imaginary) physical system. Gradual temperature reduction in the physical system isolates low energy states (``annealing''), or what is the same thing, the most probable states under the Gibbs distribution. The analogous operation under the posterior distribution yields the maximum a posteriori (MAP) estimate of the image given the degraded observations. The result is a highly parallel ``relaxation'' algorithm for MAP estimation. We establish convergence properties of the algorithm and we experiment with some simple pictures, for which good restorations are obtained at low signal-to-noise ratios.

18,761 citations