Minas E. Spetsakis

Learn More
A theory is presented for the computation of three-dimensional motion and structure from dynamic imagery, using only line correspondences. The traditional approach of corresponding microfeatures (interesting points-highlights, corners, high-curvature points, etc.) is reviewed and its shortcomings are discussed. Then, a theory is presented that describes a(More)
One of the main issues in the area of motion estimation given the correspondences of some features in a sequence of images is sensitivity to error in the input. The main way to attack the problem, as with several other problems in science and engineering, is redundancy in the data. Up to now all the algorithms developed either used two frames or depended on(More)
A theory is presented for the computation of three dimensional motion and structure from dynamic imagery, using only line correspondences. The traditional approach of corresponding microfeatures (interesting points-highlights, corners, high curvature points, etc.) is reviewed and its shortcomings are discussed. Then, a theory is presented that describes a(More)
We analyze the problem of estimating 3-D motion in an optimal manner using correspondences of features in two views. The importance of having an optimal estimator is twofold: first, for the estimation itself and, second, for the bound it offers on how much sensitivity one can expect from a two-frame, point-based motion algorithm. The optimal estimator turns(More)
We present a new algorithm that does motion segmentation by tracking small textured patches and then clustering them using EM. A small patch has the advantage that its motion is well modeled by uniform flow and runs a lower risk of boundary inclusion. Inherently, a small patch has less data so it is more susceptible to noise and it is not well suited to fit(More)
This paper presents a noveltracking based motion segmen-tation algorithm. The tracking is done by fitting successively more elaborate models of optical flowonthe tracked region and the segmentation is done by extracting the regions of the image that are consistent with the computed model of flow. The method can track objects in image sequences with moving(More)