R
Robert M. Haralick
Researcher at City University of New York
Publications - 454
Citations - 49091
Robert M. Haralick is an academic researcher from City University of New York. The author has contributed to research in topics: Image processing & Edge detection. The author has an hindex of 65, co-authored 453 publications receiving 46164 citations. Previous affiliations of Robert M. Haralick include University of Maryland, College Park & University of Washington.
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Textural Features for Image Classification
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.
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Statistical and structural approaches to texture
TL;DR: This survey reviews the image processing literature on the various approaches and models investigators have used for texture, including statistical approaches of autocorrelation function, optical transforms, digital transforms, textural edgeness, structural element, gray tone cooccurrence, run lengths, and autoregressive models.
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Image Analysis Using Mathematical Morphology
TL;DR: The tutorial provided in this paper reviews both binary morphology and gray scale morphology, covering the operations of dilation, erosion, opening, and closing and their relations.
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Image Segmentation Techniques
TL;DR: There are several image segmentation techniques, some considered general purpose and some designed for specific classes of images as discussed by the authors, some of which can be classified as: measurement space guided spatial clustering, single linkage region growing schemes, hybrid link growing scheme, centroid region growing scheme and split-and-merge scheme.
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Increasing tree search efficiency for constraint satisfaction problems
TL;DR: In this article, the authors explore the number of tree search operations required to solve binary constraint satisfaction problems and show that the two principles of first trying the places most likely to fail and remembering what has been done to avoid repeating the same mistake twice improve the standard backtracking search.