
Accession Number : ADA189041
Title : A Probabilistic Approach to LowLevel Vision.
Descriptive Note : Technical rept.,
Corporate Author : ROCHESTER UNIV NY DEPT OF COMPUTER SCIENCE
Personal Author(s) : Sher, David B
PDF Url : ADA189041
Report Date : Oct 1987
Pagination or Media Count : 215
Abstract : A probabilistic approach to lowlevel vision algorithms results in algorithms that are easy to tune for a particular application and modules that can be used for many applications. Several routines that return likelihoods can be combined into a single more robust routine. Thus it is easy to construct specialized yet robust lowlevel vision systems out of algorithms that calculate likelihoods. This dissertation studies algorithms that generate and use likelihoods. Probabilities derive from likelihoods using Bayes' rule. Thus vision algorithms that return likelihoods also generate probabilities. Likelihoods are used by Markov Random Field algorithms. This approach yields facet model boundary pixel detectors that return likelihoods. Experiments show that the detectors designed for the step edge model are on par with the best edge detectors reported in the literature. Algorithms are represented here that use the generalized Hough transform to calculate likelihoods for object recognition. Evidence, represented as likelihoods, from several detectors that view the same data with different models are combined here. The likelihoods that result are used to build robust detectors out of several specialized ones. Results are shown here for combining boundary detectors that assume several levels of noise and combining detectors of several sizes.
Descriptors : *VISION, *ROBOTICS, ALGORITHMS, BOUNDARIES, DETECTORS, EDGES, LOW LEVEL, LOW LIGHT LEVELS, MARKOV PROCESSES, MODELS, NOISE, PROBABILITY, RECOGNITION, YIELD, THESES, ARTIFICIAL INTELLIGENCE, BAYES THEOREM
Subject Categories : Bionics
Distribution Statement : APPROVED FOR PUBLIC RELEASE