A face image contains geometric cues in the form of configurational information and contours that can be used to estimate 3D face shape. While it is clear that 3D reconstruction from 2D points is highly ambiguous if no further constraints are enforced, one might expect that the face-space constraint solves this problem. We show that this is not the case and that geometric information is an ambi...

Human vision is adept at inferr ing surface shape from a projection of curves lying across a surface, part icular ly from paral lel undulating curves. Since the image projection loses 3-D information, the interpretation must be constrained by certain a p r io r i assumptions. A theory of constraint on this problem [Stevens 1981a] proposes three assumptions, namely that neither the viewpoint nor...

In this paper, we present a novel postprocessing technique based on the theory of the projection onto convex sets (POCS) in order to reduce the blocking artifacts in digital high definition television (HDTV) images. By detecting and eliminating the undesired high-frequency components, mainly caused by blocking artifacts, we propose a new smoothness constraint set (SCS) and its projection operat...

In this work, we present results from a new formulation for determining image velocities from a time-sequence of X-ray projection images of owing uid. Starting with the conservation of mass principle, and physics of X-ray projection, we derive a motion constraint equation for projection imaging, a practical special case of which is shown to be the Horn and Schunck's optical ow constraint. We ar...

Principal component analysis (PCA) is an unsupervised method for learning low-dimensional features with orthogonal projections. Multilinear PCA methods extend PCA to deal with multidimensional data (tensors) directly via tensor-to-tensor projection or tensor-to-vector projection (TVP). However, under the TVP setting, it is difficult to develop an effective multilinear PCA method with the orthog...

Very recently, a solution to the kernel-based online classification problem has been given by the adaptive projected subgradient method (APSM). The developed algorithm can be considered as a generalization of a kernel affine projection algorithm (APA) and the kernel normalized least mean squares (NLMS). Furthermore, sparsification of the resulting kernel series expansion was achieved by imposin...

In this paper, we study the strong convergence of the Halpern type algorithms for a strongly quasi-nonexpansive sequence of operators. These results extend the results of Saejung [11]. Some applications in infinite family of firmly quasi-nonexpansive mappings, multiparameter proximal point algorithm, constraint minimization and subgradient projection are presented.

New iterative methods for solving systems of linear inequalities are presented. Each step in these methods consists of finding the orthogonat projection of the current point onto a hyperplane corresponding to a surrogate constraint which is constructed through a positive combination of a group of violated constraints. Both sequential and parallel-implementations are discussed.

We study an equivalent optimization problem with an inequality constraint and boundary conditions, whose necessary condition for the optimality is the variational inequality presentation of American options. To solve the problem, we use the gradient projection method, with discretizations both in time and space. We tested the algorithm and compared with the projective successive over-relaxation...

We consider fully distributed constrained convex optimization problems over a network, where each network agent has a distinct objective and constraint set. We discuss a gossipbased random projection algorithm (GRP) with uncoordinated diminishing stepsizes. We prove that, when the problem has a solution, the iterates of all network agents converge to the same optimal point with probability 1.