A neural fuzzy system for image motion estimation

Many methods for computing optical ow (image motion vector) have been proposed while others continue to appear. Block-matching methods are widely used because of their simplicity and easy implementation. The motion vector is uniquely de ned, in block-matching methods, by the best t of a small reference subblock from a previous image frame in a larger, search region from the present image frame. Hence, this method is very sensitive to the real environments (involving occlusion, specularity, shadowing, transparency, etc.). In this paper, a neural fuzzy system with robust characteristics and learning ability is incorporated with the block-matching method to make a system adaptive for di erent circumstances. In the neural fuzzy motion estimation system, each subblock in the search region is assigned a similarity membership contributing di erent degrees to the motion vector. This system is more reliable, robust, and accurate in motion estimation than many other methods including Horn and Schunck’s optical ow, fuzzy logic motion estimator (FME), best block matching, NR, and fast block matching. Since fast block-matching algorithms can be used to reduce search time, a threestep fast search method is employed to nd the motion vector in our system. However, the candidate motion vector is often trapped by the local minimum, which makes the motion vector undesirable. An improved three-step fast search method is tested to reduce the e ect from local minimum and some comparisons about fast search algorithms are made. In addition, a Quarter Compensation Algorithm for compensating the interframe image to tackle the problem that the motion vector is not an integer but rather a oating point is proposed. Since our system can give the accurate motion vector, we may use the motion information in many di erent applications such as motion compensation, CCD camera auto-focusing or zooming, moving object extraction, etc. Two application examples will be illustrated in this paper. c © 2000 Elsevier Science B.V. All rights reserved.