Accession Number : ADA295642

Title :   Uncertainty Propagation in Model-Based Recognition.

Descriptive Note : Memorandum rept.,

Corporate Author : MASSACHUSETTS INST OF TECH CAMBRIDGE ARTIFICIAL INTELLIGENCE LAB

Personal Author(s) : Jacobs, D. W. ; Alter, T. D.

PDF Url : ADA295642

Report Date : DEC 1994

Pagination or Media Count : 24

Abstract : Building robust recognition systems requires a careful understanding of the effects of error in sensed features. Error in these image features results in a region of uncertainty in the possible image location of each additional model feature. We present an accurate, analytic approximation for this uncertainty region when model poses are based on matching three image and model points, for both Gaussian and bounded error in the detection of image points, and for both scaled-orthographic and perspective projection models. This result applies to objects that are fully three-dimensional, where past results considered only two-dimensional objects. Further, we introduce a linear programming algorithm to compute the uncertainty region when poses are based on any number of initial matches. Finally, we use these results to extend, from two-dimensional to three-dimensional objects, robust implementations of alignment interpretation-tree search, and transformation clustering. (AN)

Descriptors :   *IMAGE PROCESSING, *PATTERN RECOGNITION, MATHEMATICAL MODELS, ALGORITHMS, UNCERTAINTY, TWO DIMENSIONAL, LINEAR PROGRAMMING, REGIONS, THREE DIMENSIONAL, APPROXIMATION(MATHEMATICS), PIXELS, MATCHING, ERROR CORRECTION CODES, IMAGE REGISTRATION, PROJECTIVE TECHNIQUES.

Subject Categories : Cybernetics

Distribution Statement : APPROVED FOR PUBLIC RELEASE