A region matching motion estimation algorithm

Abstract The problem of motion estimation is reviewed and a region matching 2-D motion estimation algorithm is presented. The central projection of a 3-D object moving in 3-D space is a nonrigid 2-D object which moves on the image plane. The motion of the central projection is called 2-D motion and is modeled by an affine linear transformation. If the 2-D motion is not linear or the segmentation is imperfect, this model will give a good approximation. An objective function is defined in terms of the motion parameters. The algorithm searches for that set of motion parameters which describe best the 2-D motion of the central projection. The algorithm is robust in the presence of noise and, it does not require small motion or smooth intensity profiles. It assumes that the images have been segmented. Its computation time can be significantly reduced by an appropriate choice of the initial values of the motion parameters. The performance of the algorithm is examined for different kinds of motion and various SNRs using computer generated and real images.

[1]  A. A. Sawchuk,et al.  Motion compensated enhancement of noisy image sequences , 1990, International Conference on Acoustics, Speech, and Signal Processing.

[2]  Ishwar K. Sethi,et al.  Finding Trajectories of Feature Points in a Monocular Image Sequence , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Chia-Hoang Lee,et al.  Interpreting Image Curve from Multiframes , 1988, Artif. Intell..

[4]  Ciro Cafforio,et al.  Methods for measuring small displacements of television images , 1976, IEEE Trans. Inf. Theory.

[5]  Ishwar K. Sethi,et al.  Feature Point Correspondence in the Presence of Occlusion , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Narendra Ahuja,et al.  3-D Motion Estimation, Understanding, and Prediction from Noisy Image Sequences , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Thomas S. Huang,et al.  Estimating three-dimensional motion parameters of a rigid planar patch , 1981 .

[8]  Rama Chellappa,et al.  Estimation of Object Motion Parameters from Noisy Images , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Amar Mitiche,et al.  Interpretation of straight line correspondences using angular relations , 1989, Pattern Recognit..

[10]  Gilad Adiv,et al.  Determining Three-Dimensional Motion and Structure from Optical Flow Generated by Several Moving Objects , 1985, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Jake K. Aggarwal,et al.  Computer Analysis of Moving Polygonal Images , 1975, IEEE Transactions on Computers.

[12]  Muralidhara Subbarao.  Interpretation of Image Flow: A Spatio-Temporal Approach , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  J. Potter.  Scene segmentation using motion information , 1977 .

[14]  Allen M. Waxman,et al.  Closed from solutions to image flow equations for planar surfaces in motion , 1986, Comput. Vis. Graph. Image Process..

[15]  Thomas S. Huang,et al.  Estimation of rigid body motion using straight line correspondences , 1986, Comput. Vis. Graph. Image Process..

[16]  N. Nahi,et al.  Estimation-detection of object boundaries in noisy images , 1978 .

[17]  Ellen C. Hildreth,et al.  Computations Underlying the Measurement of Visual Motion , 1984, Artif. Intell..

[18]  Thomas S. Huang,et al.  Estimating three-dimensional motion parameters of a rigid planar patch, II: Singular value decomposition , 1982 .

[19]  J.K. Aggarwal,et al.  Correspondence processes in dynamic scene analysis , 1981, Proceedings of the IEEE.

[20]  Robert J. Schalkoff,et al.  A Model and Tracking Algorithm for a Class of Video Targets , 1982, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  Jake K. Aggarwal,et al.  Computer Analysis of Planar Curvilinear Moving Images , 1977, IEEE Transactions on Computers.

[22]  Thomas S. Huang,et al.  Uniqueness and Estimation of Three-Dimensional Motion Parameters of Rigid Objects with Curved Surfaces , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[23]  Jake K. Aggarwal,et al.  On the computation of motion from sequences of images-A review , 1988, Proc. IEEE.

[24]  J. Limb,et al.  Estimating the Velocity of Moving Images in Television Signals , 1975 .

[25]  Larry S. Davis,et al.  Contour-based motion estimation , 1982, Comput. Vis. Graph. Image Process..

[26]  Jake K. Aggarwal,et al.  Computer analysis of dynamic scenes containing curvilinear figures , 1979, Pattern Recognit..

[27]  Thomas S. Huang,et al.  Some Experiments on Estimating the 3-D Motion Parameters of a Rigid Body from Two Consecutive Image Frames , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[28]  M. Bertero,et al.  Ill-posed problems in early vision , 1988, Proc. IEEE.

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

[30]  Rama Chellappa,et al.  Segmentation and 2-D motion estimation of noisy image sequences , 1988, ICASSP-88., International Conference on Acoustics, Speech, and Signal Processing.

[31]  Olivier D. Faugeras,et al.  Counting straight lines , 1989, Comput. Vis. Graph. Image Process..