3D INFORMATION EXTRACTION USING REGION-BASED DEFORMABLE NET FOR MONOCULAR ROBOT NAVIGATION | ||||
| JES. Journal of Engineering Sciences | ||||
| Article 8, Volume 35, No 4, July and August 2007, Page 975-994 PDF (1 MB) | ||||
| Document Type: Research Paper | ||||
| DOI: 10.21608/jesaun.2007.114347 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
| Khaled M. Shaaban* ; Nagwa M. Omar | ||||
| Electrical Engineering Department, Assiut University, Assiut, Egypt | ||||
| Abstract | ||||
| This paper proposes a new method to extract the objects' 3D information for monocular robot navigation. The proposed method is based upon the Region-Based Deformable Net (RbDN) technique that we developed in [1]. This technique is modified to segment any real time video sequence captured from a single moving camera. Instead of deforming a single contour, typically used with other deformable contour methods, RbDN technique deforms a planner net. The net consists of elastic polygons that represent the segmented regions' boundaries. The deformation process tracks the location change of the polygons and their vertices across the frames. The 3D information of each object's corner is extracted based on the location change of the corresponding vertex. Furthermore, the change in the area of each region across the frames is used to accurately extract the average depth of the surface corresponding to that region. The algorithm is completely autonomous and does not require user interference, training or pre-knowledge. The experimental results demonstrate the capability of the algorithm to extract the objects' 3D information with high accuracy within a reasonable time. | ||||
| Keywords | ||||
| Machine Vision; Robot Navigation; Landmarks; Objects 3D Information Extraction; Monocular Vision; Stereo Vision; Correspondence Problem; Deformable Contours | ||||
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