Auxiliary variables and two-step iterative algorithms in computer vision problems

Optimal Parallel Algorithms for Computer Vision Problems

The computational model on which the algorithms are developed is the arrays with reconfigurable optical buses (abbreviated to AROB). It integrates the advantages of both optical transmission and electronic computation. In this paper, instead of using the radix-2 system, a radix-x system can be used to represent the numbers of the intermediate results of algorithms. We first design two Oð1Þ time...

Idiap Geometric Matching in Computer Vision Algorithms and Open Problems

In this paper I review and discuss recent advances in the devel opment of algorithms for solving the kinds of geometric match ing problems that occur in computer vision in particular under bounded error models I examine both the one model case recog nition and the many model case indexing and I discuss some open problems and directions for research

Auxiliary Variables in Mixture Modeling: 3-Step Approaches Using Mplus

This paper discusses alternatives to single-step mixture modeling. A 3step method for latent class predictor variables is studied in several different settings including latent class analysis, latent transition analysis, and growth mixture modeling. It is explored under violations of its assumptions such as with direct effects from predictors to latent class indicators. The 3-step method is als...

Iterative Algorithms in Inverse Problems

Computer Vision - Algorithms and Applications

This paper presents a technique for estimating the threedimensional velocity vector field that describes the motion of each visible scene point (scene flow). The technique presented uses two consecutive image pairs from a stereo sequence. The main contribution is to decouple the position and velocity estimation steps, and to estimate dense velocities using a variational approach. We enforce the...