Scale-invariant polyhedral object recognition using fragmentary edge segments

We propose a scale-invariant polyhedral object recognition algorithm that is based on the pose clustering paradigm using fragmentary edge segments. Two novel feature-focus techniques are introduced to reduce the computational complexity for matching a scene with n edge fragments and a model with m edges from Q(m²n²) to O(mn) without loss of matching quality. In addition, we suggest a mixed data structure that requires only a three-dimensional accumulation array. The proposed recognition method has been successfully tested on real range data