Showing papers on "Segmentation-based object categorization published in 1981"

Segmentation and Estimation of Image Region Properties through Cooperative Hierarchial Computation

Abstract: The task of segmenting an image and that of estimating properties of image regions may be highly interdependent. The goal of segmentation is to partition the image into regions with more or less homogeneous properties; but the processes which estimate these properties should be confined within individual regions. A cooperative, iterative approach to segmentation and property estimation is defined; the results of each process at a given iteration are used to adjust the other process at the next iteration. A linked pyramid structure provides a framework for this process iteration. This hierarchical structure ensures rapid convergence even with strictly local communication between pyramid nodes.

Research Progress on Image Segmentation

Abstract: : A survey of recent progress on image segmentation is presented according to statistically based approaches such as the structural methods. Extensive list of literatures on image segmentation is also given.

Segmentation by Shape Discrimination Using Spatial Filtering Techniques

Abstract: Zadeh (4) introduced the notion of fuzzy set theory and Goetcherian (6) showed how fuzzy logic could extend a variety of binary image processes into a multi-valued (grey image) domain. This paper introduces the basics of fuzzy set theory and discusses the suitability of the physically parallel CLIP machine for implementing the multi-valued operators. A form of spatial filtering is explained and its application to shape discrimination and image segmentation is shown. Finally, an application to a real image processing problem is illustrated.

Some Experimental Results on Linear Estimation for Image Analysis.

Abstract: : Based on a constructed textured subimage, an experimental comparison is made on the three linear estimation methods for image segmentation, namely the maximum likelihood estimation, the maximum a posteriori estimation and the Fisher's linear discriminant. The last two methods are shown to be feasible for effective image segmentation. (Author)