Accession Number : ADA264335


Title :   Qualitative Methods in Computer Vision


Descriptive Note : Final rept. 1 Jun 1991-30 Sep 1992


Corporate Author : MARYLAND UNIV COLLEGE PARK CENTER FOR AUTOMATION RESEARCH


Personal Author(s) : Rosenfeld, Azriel


Full Text : https://apps.dtic.mil/dtic/tr/fulltext/u2/a264335.pdf


Report Date : Jan 1993


Pagination or Media Count : 10


Abstract : Current object recognition systems can only recognize a limited class of objects. Objects having variable numbers of parts and only loosely constrained shapes cannot be modeled and recognized by these systems. The PI proposed the use of a data structure called the VAPOR (Variable Appearance Object Representation) model to represent objects with these kinds of variable appearances and develop a search procedure called MOSS (Model Space Search) to find instances of these models in two-dimensional image data. The VAPOR model is an idealization of the object; all instances of the model in the image are variations from ideal appearance. The variations are evaluated by the description length of the model, measured in information-theoretic bits. MOSS selects the best model for the given image data by choosing the minimal length description. It was demonstrated how the system performs in a simple domain of circles and polygons and in the complex domain of finding cloverleaf intersections in aerial images of roads.


Descriptors :   *IMAGE PROCESSING , *COMPUTER VISION , TWO DIMENSIONAL , VAPORS , SHAPE , REPORTS , ABSTRACTS , POLYGONS , CIRCLES , INFORMATION THEORY , VARIABLES , VARIATIONS , LENGTH , IMAGES , ROADS


Subject Categories : Cybernetics


Distribution Statement : APPROVED FOR PUBLIC RELEASE