A topic of computer vision that has been recently studied by a substantial number of scientists is the recognition of objects in digitized gray scale images. The primary goal of model-based object recognition research is the efficient and precise matching of features extracted from sensory data with the corresponding features in an object model database. A source of difficulty during the feature extraction is the determination and representation of pertinent attributes from the sensory data of the objects in the image. In addition, features which are visible from a single vantage point are not usually adequate for the unique identification of an object and its orientation. This paper will describe a regimen that can be used to address these problems. Image preprocessing such as edge detection, image thinning, thresholding, etc., will first be addressed. This will be followed by an in depth discussion that will center upon the extraction of local geometric feature vectors and the hypothesis-verification model used for two dimensional object recognition. / Department of Computer Science
| Identifer | oai:union.ndltd.org:BSU/oai:cardinalscholar.bsu.edu:handle/184380 |
| Date | January 1992 |
| Creators | Ditzenberger, David A. |
| Contributors | Ball State University. Dept. of Computer Science., Place, Ralph L. |
| Source Sets | Ball State University |
| Detected Language | English |
| Format | iii, 55 leaves : ill. ; 28 cm. |
| Source | Virtual Press |
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