Showing papers by "Ishwar K. Sethi published in 1988"
TL;DR: This paper proposes a relaxation algorithm for feature point matching where the formation of smooth trajectories over space and time is favored and is presented to demonstrate the merit of out approach.
13 citations
29 Mar 1988
TL;DR: In this article, a new paradigm called motion filtering is presented for exploiting motion to extract and track targets in high clutter, low resolution, low contrast thermal imagery, and two sequences of tactical situations are presented to exemplify the capabilities of the proposed approach.
Abstract: A critical problem in automatic target recognition is the extraction and tracking of moving targets in high clutter, low resolution, low contrast thermal imagery. This problem is further exacerbated by the large distances that typically exist between the sensor and the targets. In this paper, a new paradigm called motion filtering is presented for exploiting motion to extract and track targets. The main feature of the present approach is the emphasis on the motion information. Experimental results from two sequences of tactical situations are presented to exemplify the capabilities of the proposed approach.
6 citations
12 Oct 1988
TL;DR: In this paper, the authors used the equidistance contours of a range image to further characterize the regions obtained from segmentation, which is an integral part of an object recognition process, finding the geometries of the corresponding 3D object surfaces of the regions from the contours.
Abstract: Recently we have proposed a range image segmentation method based on the equidistance contour map extracted from a range image. An equidistance contour of a range image is formed by pixels on the range image whose corresponding scene points are all at a same specified distance from the sensor. We have observed that in different ways the contours reflect the existence of object surface edges, the geometries of object surfaces, and the orientations of these surfaces in the 3-D space. In this paper we present a method which uses the equidistance contours to further characterize the regions obtained from segmentation. This is an integral part of an object recognition process. The meaning of characterization is to find the geometries of the corresponding 3-D object surfaces of the regions from the contours. If a surface has nice analytic form such as planar, spherical, cylindrical, or conical, we determine not only its type but also the values of the parameters which describe its geometry.
1 citations