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26 September 1997 Navigation using self-initializing active contours
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This paper examines a novel approach for extracting motion information to allow the autonomous navigation of an intelligent mobile robot using computer vision in a moving camera, moving object environment. The approach begins by extracting low-level scene feature information using algorithms such as the SUSAN corner and edge detector. A routine is described for converting the information obtained from these stable features to initialization information for creating active contour models or 'snakes.' Multiple open and closed active contours are identified in an initialization frame from this primary feature extraction. These contours are allowed to converge more closely to the features to which they are attached. These contours are then allowed to converge to the features within each frame through image sequences, with criteria for the re-initialization of new contours when motion information in the sequence or a region becomes sparse. The information received from these contour models is then used to determine the motion information in the scene. Reasons for this approach are outlined and justified. This theoretical approach is then applied to the practical cases of a mobile robot navigating indoor scenes. Large sections of this approach have been implemented in the Khoros environment, with new routines written for this approach. Promising results are already available and this approach is being examined to allow the extraction of depth information in the scene for assisting navigation using a form of '3-D snakes.'
© (1997) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Derek Molloy and Paul F. Whelan "Navigation using self-initializing active contours", Proc. SPIE 3208, Intelligent Robots and Computer Vision XVI: Algorithms, Techniques, Active Vision, and Materials Handling, (26 September 1997);

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