Multi-objective retrieval of object pose from video

TitleMulti-objective retrieval of object pose from video
Publication TypeConference Paper
Year of Publication2000
AuthorsAvanaki, A. N., B. Hamidzadeh, and F. Kossentini
Conference NameTools with Artificial Intelligence, 2000. ICTAI 2000. Proceedings. 12th IEEE International Conference on
Pagination242 -249
Keywordscomputer vision, feedback, genetic algorithms, image texture, mask difference, multi-objective retrieval, object pose retrieval, optimisation, optimization methods, pose comparison, pose similarity measures, reference frame rotation, reference pose, rigid object pose estimation, rotated reference view, rotation, texture difference, unknown-pose view, video, video signal processing
Abstract

Introduces a novel approach for rigid object pose estimation. The system rotates a reference frame of the object of interest until it reaches a view at which the rotated reference view and the unknown-pose view seem to be ldquo;similar rdquo;. A number of pose similarity measures were tested for different types of objects undergoing various amounts of rotation from the reference pose. We demonstrate that the sum of the texture difference and the mask difference can be used as an effective pose similarity measure, which is capable of a unique determination of the correct pose. A number of optimization methods (e.g. genetic algorithms) were used as feedback from pose comparison to reference frame rotation. The results of comparing these methods in a number of experiments is reported in this paper as well

URLhttp://dx.doi.org/10.1109/TAI.2000.889877
DOI10.1109/TAI.2000.889877

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