Effect of noise on the performance of the temporally-sequenced intelligent block-matching and motion-segmentation algorithm

Most algorithms for motion-based segmentation depend on the system's ability to estimate optic flow from successive image frames. Block-matching is often used for this, but it faces the problems of noise-sensitivity and texture-insufficiency. Recently, we proposed a two-pathway approach based on locally coupled neural networks to address this issue. The system uses a pixel-level (P) pathway to perform robust block-matching in regions with sufficient texture, and a region-level (R) pathway to estimate motion from feature matching in low-texture regions. The fused optic-flow from the P and R pathways is then segmented by a pulse-coupled neural network (PCNN). The algorithm has produced very good results on synthetic and natural images. We show that its performance shows significant robustness to additive noise in the images.