A neural fuzzy system for image motion estimation

Many methods for computing optical flow (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 defined, in block-matching methods, by the best fit 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 different circumstances. In the neural fuzzy motion estimation system, each subblock in the search region is assigned a similarity membership contributing different 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 flow, 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 three-step fast search method is employed to find 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 effect 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 floating point is proposed. Since our system can give the accurate motion vector, we may use the motion information in many different applications such as motion compensation, CCD camera auto-focusing or zooming, moving object extraction, etc. Two application examples will be illustrated in this paper.

[1]  A. Murat Tekalp,et al.  Digital Video Processing , 1995 .

[2]  Liang-Gee Chen,et al.  Parallel architectures for 3-step hierarchical search block-matching algorithm , 1994, IEEE Trans. Circuits Syst. Video Technol..

[3]  Hans-Hellmut Nagel,et al.  Displacement vectors derived from second-order intensity variations in image sequences , 1983, Comput. Vis. Graph. Image Process..

[4]  C. D. Kuglin,et al.  The phase correlation image alignment method , 1975 .

[5]  James H. Duncan,et al.  Temporal Edges: The Detection Of Motion And The Computation Of Optical Flow , 1988, [1988 Proceedings] Second International Conference on Computer Vision.

[6]  Peter K. Allen,et al.  Automated tracking and grasping of a moving object with a robotic hand-eye system , 1993, IEEE Trans. Robotics Autom..

[7]  Hans-Hellmut Nagel,et al.  On the Estimation of Optical Flow: Relations between Different Approaches and Some New Results , 1987, Artif. Intell..

[8]  R.H.J.M. Plompen,et al.  Motion video coding for visual telephony , 1990 .

[9]  Chin-Teng Lin,et al.  Neural-Network-Based Fuzzy Logic Control and Decision System , 1991, IEEE Trans. Computers.

[10]  F. May,et al.  Model based movement compensation and interpolation for ISDN videotelephony , 1988, 1988., IEEE International Symposium on Circuits and Systems.

[11]  Bernd Girod,et al.  Motion-compensating prediction with fractional-pel accuracy , 1993, IEEE Trans. Commun..

[12]  B. Allotta,et al.  Mousebuster: a robot for real-time catching , 1994, IEEE Control Systems.

[13]  J. D. Robbins,et al.  Motion-compensated television coding: Part I , 1979, The Bell System Technical Journal.

[14]  Michael Gilge.  A High Quality Videophone Coder Using Hierarchical Motion Estimation And Structure Coding Of The Prediction Error , 1988, Other Conferences.

[15]  Shinji Ozawa,et al.  An Adaptive Sensing System with Tracking and Zooming a Moving Object , 1993 .

[16]  Berthold K. P. Horn,et al.  Determining Optical Flow , 1981, Other Conferences.

[17]  David J. Fleet.  Measurement of image velocity , 1992 .

[18]  Berthold K. P. Horn,et al.  Determining Optical Flow , 1981, Other Conferences.

[19]  Staffan Efucsson.  Fixed and Adaptive Predictors for Hybrid Predictive/Transform Coding , 1985 .

[20]  J. B. Ra,et al.  An improved block matching algorithm based on successive refinement of motion vector candidates , 1994, Signal Process. Image Commun..

[21]  J. I. Lipp.  Frame-to-frame image motion estimation with a fuzzy logic system , 1992, [1992] Proceedings of the 35th Midwest Symposium on Circuits and Systems.

[22]  Michael Mills,et al.  Blockmatching motion estimation algorithms-new results , 1990 .

[23]  Bernd Girod,et al.  The Efficiency of Motion-Compensating Prediction for Hybrid Coding of Video Sequences , 1987, IEEE J. Sel. Areas Commun..

[24]  Haibo Li,et al.  Image sequence coding at very low bit rates: a review , 1994, IEEE Trans. Image Process..

[25]  Claude L. Fennema,et al.  Velocity determination in scenes containing several moving objects , 1979 .

[26]  Anil K. Jain,et al.  Displacement Measurement and Its Application in Interframe Image Coding , 1981, IEEE Trans. Commun..