Recognition of box-like objects by fusing cues of shape and edges

Boxes are the universal choice for packing, storage, and transportation. In this paper we propose a template-based algorithm for recognition of box-like objects, which is invariant to scale, rotation and translation as well as robust to patterned surfaces and moderate occlusions. The algorithm first oversegments the input image to partition objects into pieces. Based on the smoothness property of surface texture, candidates for component segments of boxes are selected. Guided by a template trained Linear Discriminant Analysis (LDA) classifier, box-like segments are reassembled from these segments of interests. For each box-like segment, we estimate its probability of being a 2D projection of a 3D box model upon the extracted contour and inner edges. Experimental results demonstrate high detection accuracy of boxes and reliable recovery of their 2D models.

• Type: Conference
• Year: 2008
• Where: ICPR
• Authors: Chia-Chih Chen, Jake K. Aggarwal
• Keywords: Box-like Segment, Boxes, Computer Vision, ICPR 2008, Template Trained Linear