Accession Number : ADA292606

Title :   Learning Object Models From Visual Observation and Background Knowledge.

Descriptive Note : Interim rept. 1 Aug-1 Nov 94,

Corporate Author : INSTITUTE FOR THE STUDY OF LEARNING AND EXPERTISE PALO ALTO CA

Personal Author(s) : Langley, Pat ; Binford, Thomas O. ; Levitt, Tod S.

PDF Url : ADA292606

Report Date : 15 NOV 1994

Pagination or Media Count : 11

Abstract : This research project aims to use machine learning techniques to improve the performance of three dimensional vision systems. Building on our earlier work, our approach represents and organizes models of object classes in a hierarchy of probabilistic concepts, and it uses Bayesian inference methods to focus attention, recognize objects in images, and make predictions about occluded parts. The learning process involves not only updating of the probabilistic descriptions in the concept hierarchy but also involves changes in the structure of memory, including the creation of novel categories, the merging of similar classes, and the elimination of unnecessary ones. An evaluation metric based on probability theory guides decisions about such structural changes, and background knowledge about function and generic object classes further constrains the learning process. We plan to carry out systematic experiments to determine the ability of this approach to improve both classification accuracy and predictive ability on novel images.

Descriptors :   *STATISTICAL INFERENCE, *COMPUTER VISION, *LEARNING, METHODOLOGY, SIZES(DIMENSIONS), MODELS, STRUCTURAL PROPERTIES, PROBABILITY, ACCURACY, LEARNING MACHINES, MEMORY DEVICES, THREE DIMENSIONAL, IMAGES, CLASSIFICATION, VISUAL PERCEPTION, BAYES THEOREM, HIERARCHIES.

Subject Categories : Cybernetics
      Statistics and Probability

Distribution Statement : APPROVED FOR PUBLIC RELEASE