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Haralick is one of the leading figures in computer vision, pattern recognition, and image analysis. He is a Fellow of the Institute of Electrical and Electronics Engineers (IEEE) and a Fellow and past president of the International Association of Pattern Recognition.
Haralick began his work as one of the principal investigators of the NASA ERTS satellite data doing remote sensing image analysis. Haralick has made a series of contributions in the field of computer vision. In the high-level vision area, he has worked on inferring 3D geometry from one or more perspective projection views. He has also identified a variety of vision problems which are special cases of the consistent labeling problem. His papers on consistent labeling, arrangements, relation homomorphism, matching, and tree search translate some specific computer vision problems to the more general combinatorial consistent labeling problem and then discuss the theory of the look-ahead operators that speed up the tree search. The most basic of these is called Forward Checking. This gives a framework for the control structure required in high-level vision problems. He has also extended the forward-checking tree search technique to propositional logic theorem proving.
In the low-and mid-level areas, Haralick has worked in image texture analysis using spatial gray tone co-occurrence texture features. These features have been used with success on biological cell images, x-ray images, satellite images, aerial images and many other kinds of images taken at small and large scales. In the feature detection area, Haralick has developed the facet model for image processing. The facet model states that many low-level image processing operations can be interpreted relative to what the processing does to the estimated underlying gray tone intensity surface of which the given image is a sampled noisy version. The facet papers develop techniques for edge detection, line detection, noise removal, peak and pit detection, as well as a variety of other topographic gray tone surface features.
Haralick began his work as one of the principal investigators of the NASA ERTS satellite data doing remote sensing image analysis. Haralick has made a series of contributions in the field of computer vision. In the high-level vision area, he has worked on inferring 3D geometry from one or more perspective projection views. He has also identified a variety of vision problems which are special cases of the consistent labeling problem. His papers on consistent labeling, arrangements, relation homomorphism, matching, and tree search translate some specific computer vision problems to the more general combinatorial consistent labeling problem and then discuss the theory of the look-ahead operators that speed up the tree search. The most basic of these is called Forward Checking. This gives a framework for the control structure required in high-level vision problems. He has also extended the forward-checking tree search technique to propositional logic theorem proving.
In the low-and mid-level areas, Haralick has worked in image texture analysis using spatial gray tone co-occurrence texture features. These features have been used with success on biological cell images, x-ray images, satellite images, aerial images and many other kinds of images taken at small and large scales. In the feature detection area, Haralick has developed the facet model for image processing. The facet model states that many low-level image processing operations can be interpreted relative to what the processing does to the estimated underlying gray tone intensity surface of which the given image is a sampled noisy version. The facet papers develop techniques for edge detection, line detection, noise removal, peak and pit detection, as well as a variety of other topographic gray tone surface features.
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2023 IEEE LONG ISLAND SYSTEMS, APPLICATIONS AND TECHNOLOGY CONFERENCE, LISATpp.1-11, (2023)
semanticscholar(2019)
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Artyom Diky,Robert M. Haralick
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