Pictorial Data Analysis (eBook, PDF)
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This volume is the collection of lectures and presentations of the NATO AS! On Pictorial Data Analysis, held August 1-12, 1982 in the beautiful chateau de Bonas, Bonas France. The director of the AS! was Robert M. Haralick and the Co-director was Stefano Levialdi. The papers in the book are arranged in two sections first theory and general prinicples and then applications. Local computations play a central role in image processing both when a traditional computer is used and when parallel machines are used for improving image throughput. Levialdi reviews such neighborhood operators. Hung and K...
This volume is the collection of lectures and presentations of the NATO AS! On Pictorial Data Analysis, held August 1-12, 1982 in the beautiful chateau de Bonas, Bonas France. The director of the AS! was Robert M. Haralick and the Co-director was Stefano Levialdi. The papers in the book are arranged in two sections first theory and general prinicples and then applications. Local computations play a central role in image processing both when a traditional computer is used and when parallel machines are used for improving image throughput. Levialdi reviews such neighborhood operators. Hung and Kasvand discuss a line thinning application which involves detection of critical points on chain encoded data. Most low level image processing has been done using the digital raster as the basic data structure. Within the last few years many of these basic algorithms have been developed for the quadtree data structure. The quadtree permits easier access to certain kinds of spatial adjacency relationships in a variable resolution context. Rosenfeld reviews the properties of these representations and their uses in image segmentation and property measurement. Besslich discusses an expanded form of an invertible quadtree representation which permits a multiprocessor execution. Gisolfi and Vitulano discuss the C-matrix and C-filtering technique for image and texture feature extraction. O'mara et.al. discuss the application of Codel numbers to image feature extraction. Kropatsch discusses an image segmentation technique which permits the effective use of a variety of different kinds of segmentation techniques.
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