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 over-segments 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.