Partially occluded object recognition

Partially occluded object recognition is considered as one of the most difficult problems in machine vision; it has significant importance in industrial environment. In this paper, a 2-D object recognition algorithm applicable for both stand-alone and partially occluded objects is presented. The main contributions are the development of a scale and partial occlusion invariant boundary partition algorithm and a multi-resolution feature extraction algorithm using wavelet. We also implemented a hierarchical matching strategy for feature matching to reduce computational load, but with higher matching accuracy. Experiment results show that the proposed recognition algorithm is robust to similarity transformation and partial occlusion.