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Azriel Rosenfeld
Researcher at University of Maryland, College Park
Publications - 613
Citations - 50771
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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Proceedings ArticleDOI
Terrain Classification Using Texture Analysis
TL;DR: A general review of texture analysis can be found in this paper, which summarizes some past work on texture analysis, including comparisons between different classes of features based on spatial statistics and Fourier analysis; recently developed refinements of the spatial statistics approach; methods based on the ex-traction and description of texture primitives; texture models based on random geometric processes; methods of improving texture classification by feature value smoothing; and methods of segmenting images into textured regions.
Journal ArticleDOI
Models for neighbor dependency in planar point patterns
TL;DR: In this article, random processes are defined that generate planar point patterns in which the points have a tendency to cluster, or in which clustering is inhibited, and processes are also defined for labeling a given point pattern in such a way that neighboring points tend to have, or not to have the same labels.
Journal ArticleDOI
Machine vision for industry: Tasks, tools and techniques
TL;DR: The ‘why’, ‘how’ and ‘what’ of industrial machine vision systems are surveyed-why vision is important, how it is accomplished and what sorts of tasks it is being applied to.
Journal ArticleDOI
Problems for Solution: E1456-E1460
Journal ArticleDOI
Some properties of Hueckel-type edge operators
Ravi B Boppana,Azriel Rosenfeld +1 more
TL;DR: Hueckel-type edge operators find the best-fitting step function to a given image neighborhood to derive some basic properties of such operators for various classes of neighborhoods.