New methods are presented to model, visualize and automatically recognize wide-area activities, which essentially are activities that span large areas (such as a facility or urban neighborhood) and that usually span long time intervals (such as hours and weeks). We introduce the no-go topology method and the chokepoint-observation interaction method, and then show how new algorithms can be built on them to recognize a category of wide-area activity, called process-type activities. Experimental results are presented for recognizing a manufacturing process observed via persistent GMTI sensor data. Then we present experimental results illustrating how an interesting activity can be detected as a deviation from a learned wide-area normalcy model, and how new widearea activity patterns can be discovered using simple visualizations of the results. One objective of this paper is to demonstrate that it is theoretically possible to recognize wide-area and process-type activities in built-up environments using GMTI data. The results presented here use somewhat ideal sensor data (small positional error ellipses, continuous GMTI observations, repetitive activities) and our approach is to move toward more operationally realistic data.

TEA is a task-oriented computer vision system that uses Bayes nets and a maximum expected-utility decision rule to choose a sequence of task-dependent and opportunistic visual operations on the basis of their cost and (present and future) benefit. The authors discuss technical problems regarding utilities, present TEA-1's utility function (which approximates a two-step lookahead), and compare it to various simpler utility functions in experiments with real and simulated scenes.>

Sequences of symbols generated by a visual and action sequence provide information about the natural structure of the world. HMMs (hidden Markov models) provide one way to learn (recover), store, produce, manipulate, and analyze both visual sequences and associated knowledge structures for computer vision.>

The problem of segmenting a range of image into homogeneous regions, in each of which the range data corresponds to a different surface, is discussed. A maximum-likelihood (ML) segmentation is sought. As most manufactured parts are well approximated by patches of planes, cylinders, and spheres, these three simple surfaces are considered to be the only surfaces in the image. The basic approach to segmentation is to divide the range image into windows, classify each window as a particular surface primitive, and group like windows into surface regions. Grouping windows of the same surface types is cast as a weighted ML clustering problem. Mixed windows are segmented using a ML hierarchical segmentation algorithm. The resulting regions and their associated ML surface parameter and boundary estimates can then be used to perform ML object position estimation and matching. A similar approach is taken for segmenting visible-light images of Lambertian objects illuminated by a point source at infinity.>

The problem of segmenting a range image into homogeneous regions in each of which the range data correspond to a different surface is considered. The segmentation sought is a maximum-likelihood (ML) segmentation. Only planes, cylinders, and spheres are considered as presented in the image. The basic approach to segmentation is to divide the range image into windows, classify each window as a particular surface primitive, and group like windows into surface regions. Mixed windows are detected by testing the hypothesis that a window is homogeneous. Homogeneous windows are classified according to a generalized likelihood ratio test which is computationally simple and incorporates information from adjacent windows. Grouping windows of the same surface types is cast as a weighted ML clustering problem. Finally, mixed windows are segmented using an ML hierarchical segmentation algorithm. A similar approach is taken for segmenting visible-light images of Lambertian objects illuminated by a point source at infinity.>

A new approach to segmentation of range image data into quadric surfaces is treated here. The segmentation consists of three stages. First, an initial segmentation produces coarse surface regions classed as plane, cylinder, or sphere. The algorithm operates on contours, or 2D slices, of the data rather than the 3D range image data. Second, constrained surface fitting is used to obtain surface parameter estimates for each segmented region. And third, a high accuracy segmentation results in both a high resolution boundary between surface regions and accurate quadric surface parameters. The algorithm is hierarchical and works in stages from low to high image resolution while updating surface parameter estimates at each stage. Experimental results with synthetic and real range images are given.