Combined Optic Flow Optimization Method using Pulse-Couple Neural Network

Y. Cao, P. Cook, and A. Renfrew (UK)


Optic flow, Optimization, Block Matching, Pulse Coupled Neural Network, Motion estimation, Color segmentation.


Noise disturbance and “Blank Wall” problem are two main challenges faced in optic flow estimation based on Block Matching. In this paper we propose a novel method to optimize optic flow by combining BMP Re-calculation and color image segmentation. Firstly standard Block Matching technique is applied to two consecutive images to produce optic flow estimation of scene; after introducing novel concept – Block Matching Probability (BMP), 3D Pulse-Coupled Neural Network (PCNN) is applied to re-calculate BMP value of displacements of pixels on boundary; then a novel PCNN is proposed to partition image based on color information; in the end optimized BMP values and image segmentation are combined to produce more accurate optic flow estimations. Experimental results show combined method effectively improves accuracy of optic flow estimation in both synthetic and real images.

Important Links:

Go Back