Facial model coding is an integral part in MPEG-4 related applications. The generation of the facial model usually requires stereoscopic view of the face in the pre-processing stage. Although facial model can be successfully estimated from two stereo facial images, the occlusion effect and imprecise location of the feature point prohibit obtaining an accurate facial model. In this paper, several facial model estimation (FME) algorithms are proposed in order to find the precise facial model from a stereo or mono image sequence. Since a sequence of images is used to find the facial model, the problem of occlusion effects is less serious. An accurate facial model (within 7.21% error) can still be obtained by our schemes, even without the prior information on the three-dimensional position of the head with respect to the camera and the rotation axis/angle of the head's movement. This is the largest error of all FME algorithms presented in this paper when the subject does not wear eyeglasses. In addition, our schemes do not require precise camera parameters and avoid tedious camera calibration, thereby, simplifying the facial model extraction.

This paper proposes a polynomial search (PS) algorithm and architecture to solve the motion estimation problem in video coding. Simulation results show that the proposed method is not only flexible, but also requires fewer computations to achieve the same mean absolute error results (for QCIF and sub-QCIF video) compared with the existing fast-search algorithms. Finally, a VLSI architecture is also developed to efficiently implement the PS algorithm.

Set-membership (SM) identification, which refers to a class of algorithms using certain a priori knowledge about a parametric model to constrain the solutions to certain sets, is considered. The focus is on a class of SM-based techniques that are of particular interest in applications requiring real-time processing. The optimal bounding ellipsoid (OBE) algorithms are interpreted as a blending of the classical least-square error minimization approach with knowledge of bounds on model errors arising from SM considerations. Using this interpretation, a general framework embracing all currently used OBE algorithms is developed, and strategies for adaptation and for implementation on parallel machines are discussed. Computational complexity benefits are considered for the various algorithms. The treatment is tutorial, leaving many of the formal details to an appendix that presents an archival theoretical treatment of the key results. A second appendix gives an overview of current research in the general SM identification field.>

A strategy is developed which can be applied to any version, adaptive or non-adaptive, of the set membership weighted recursive least squares (SM-WRLS) algorithm to improve the computational efficiency. A significant reduction in computational complexity can be achieved by employing a suboptimal test for information content in the incoming data. The main issue is to avoid the computations of an O(m/sup 2/) checking procedure, where m is the number of parameters to be estimated, which is required to check for the existence of useful data. Since most of the time these computations result in the rejection of incoming data, a more efficient test which reduces the complexity of the algorithm to O(m) is presented.>

An adaptive set membership identification algorithm with a very flexible forgetting scheme is presented. In preliminary experiments, the method yields highly accurate estimates using very few of the data, and quickly adapts to fast-changing dynamics. A compact systolic architecture to implement this algorithm is developed which uses O(m) cells and reduces the computational complexity to O(m) operations per observation, where m represents the number of parameters to be estimated in a linear system or signal model.>

It is shown that the optimal bounding ellipsoid (OBE) algorithm for identifying an ARMAX system can be formulated as a conventional weighted recursive least squares estimator with special weights. In this framework the OBE can be implemented using contemporary algorithms developed for least squares solutions on systolic machines. An example of a systolic processor for OBE is given, and computational complexity issues are considered.>