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Using the structure and motion of stereo point clouds for the semantic segmentation of images

University of British Columbia

The segmentation of images into semantically coherent regions has been approached in many different ways in the over 40 years since the problem was first addressed. Recently systems using the motion of point clouds derived from laser depth scanners and structure from motion have been described, but these are monetarily and computationally expensive options. We explore the use of stereo cameras to achieve the same results. This approach is shown to work in an indoor environment, giving results that compare favorably with existing systems. The use of stereo instead of structure from motion is shown to be preferable in this environment, while the choice of stereo algorithm proves highly critical to the quality of the results. The use of aggregated voting regions is explored, which is shown to moderately improve the results while speeding up the process considerably. Experiments are also run biasing the randomized input to the classifier generation process, which show further improvements in both performance and execution time. Overall, the approach is shown to be feasible, but not currently practical for robotic navigation in this environment.

Text

2010-01-04

Attribution-ShareAlike 3.0 Unported

http://hdl.handle.net/2429/17415

Computer Science

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-sa/3.0/