2D partially occluded object recognition using curve moment invariants

Abstract

This project presents a novel approach for the recognition of 2D partially occluded objects using the curve moment invariants as the features. Curve moment can uniquely characterize the geometric features of object boundary. It not only inherits the similarity transform invariance properties from conventional region-based moment, but also has many advantages which are especially promising for our research project. We have adopted successfully the curve moment invariants as our features for recognition of partially occluded object.In the recognition approach, the boundary of object of interest is first extracted after image pre-processing. Then corner points were used to partition the boundary into curve segments consisted of 3 consecutive corners. Subsequently, seven different order moment descriptors are computed as feature vectors for each segment. Finally, feature matching between the object of interest in the scene and the model is performed hierarchically. From the experimental results, the proposed recognition algorithm was found to be robust to similarity transform, noise and partial occlusion, and computational efficient.