Multi-objective retrieval of object pose from video

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 “similar”. 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