Azriel Rosenfeld

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.

Digital Straight Line Segments

Abstract: It is shown that a digital arc S is the digitization of a straight line segment if and only if it has the "chord property:" the line segment joining any two points of S lies everywhere within distance 1 of S. This result is used to derive several regularity properties of digitizations of straight line segments.

Fuzzy digital topology

Abstract: Topological relationships among parts of a digital picture, such as connectedness and surroundedness, play an important role in picture analysis and description. This paper generalizes these concepts to fuzzy subsets, and develops some of their basic properties.

Histogram concavity analysis as an aid in threshold selection

Abstract: A well-known heuristic for segmenting an image into gray level subpopulations is to select thresholds at the bottoms of valleys on the image's histogram. When the subpopulations overlap, valleys may not exist, but it is often still possible to define good thresholds at the `shoulders' of histogram peaks. Both valleys and shoulders correspond to concavities on the histogram, and this suggests that it should be possible to find good candidate thresholds by analyzing the histogram's concavity structure. Histogram concavity analysis as an approach to threshold selection is investigated and its performance on a set of histograms of infrared images of tanks is illustrated.

Computer Vision and Image Processing

Abstract: On the use of morphological operators in a class of edge detectors, L. Hertz and R.W. Schafer a valley-seeking threshold selection technique, S.C. Sahasrabudhe and K.S. Das Gupta local characteristics of binary images and their application to the automatic control of low-level robot vision, P.W. Pachowicz corner detection and localization in a pyramid, S. Baugher and A. Rosenfeld parallel-hierarchical image partitioning and region extraction, G.N. Khan and D.F. Gillies invariant architectures for low-level vision, L. Jacobson and H. Wechsler representation - primitives chain code, L. O'Gorman generalized cones - useful geometric properties, K. Rao and G. Medioni vision-based rendering - image synthesis for vision feature algorithms, J.D. Yates, et al recognition - investigation of a number of character recognition algorithms, A.A. Verikas, et al log-polar mapping applied to pattern representation and recognition, J.C. Wilson and R.M. Hodgson pattern recognition of binary image objects using morphological shape decomposition, I. Pitas and N.D. Sidiropoulos a pattern classification approach to multi-level thresholding for image segmentation, J.G. Postaire and M. Ameziane KOR - a knowledge-based object recognition system, C.M. Lee, et al shape decomposition based on perceptual structure, H.S. Kim and K.H. Park three dimensional - the Frobenius metric in image registration, K. Zikan and T.M. Silberberg binocular fusion revisited utilizing a log-polar tessellation, N.C. Griswold, et al an expert system for recovering 3D shape and orientation from a single view, W.J. Shomar, et al integrating intensity and range sensing to construct 3D polyhedra representation, W.N. Lie, et al notes - texture segmentation using topographic labels, T.C. Pong, et al an improved algorithm for labelling connected components in a binary image, X.D. Yang a note on the paper "The Visual Potential - One Convex Polygon", A. Laurentini a string descriptor for matching partial shapes, H.C. Liu and M.D. Srinath formulation and error analysis for a generalized image point correspondence algorithm, S. Fotedar, et al a new surface tracking system in 3D binary images, L.W. Chang and M.J. Tsai.

A Computational Approach to Edge Detection

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.

Textural Features for Image Classification

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.

A theory for multiresolution signal decomposition: the wavelet representation

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. >

Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images

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.