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The role of perceptual organization in motion analysis has heretofore been minimal. In this work we demonstrate that the use of perceptual organization principles of temporal coherence (common fate) and spatial proximity can result in a robust motion segmentation algorithm that is able to handle drastic illumination changes, occlusion events, and multiple moving objects, without the use of object models. The adopted algorithm does not employ the traditional frame by frame motion analysis, but rather treats the image sequence as a single 3D spatio-temporal block of data. We describe motion using spatio-temporal surfaces, which we, in turn, describe as compositions of finite planar patches. These planar patches, referred to as temporal envelopes, capture the local nature of the motions. We detect these temporal envelopes using 3D-edge detection followed by Hough transform, and represent them with convex hulls. We present a graph-based method to group these temporal envelopes arising from one object based on Gestalt organizational principles. A probabilistic Bayesian network quantifies the saliencies of the relationships between temporal envelopes. We present results on sequences with multiple moving persons, significant occlusions, and scene illumination changes.more » « less
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In this paper we demonstrate that it is possible to discriminate between high level motion types such as walking, jogging, or running based on just the change in the relational statistics among the detected image features, without the need for object models, perfect segmentation, or tracking. Instead of the statistics of the feature attributes themselves, we consider the distribution of the statistics of the relations among the features. We represent the observed distribution of feature relations in an image as a point in a space where the Euclidean distance is related to the Bhattacharya distance between probability functions. Different motion types sweep out different traces in this Space of Probability Functions (SoPF). We demonstrate the effectiveness of this representation on image sequences of human in motion, gathered using a digital video camera. We show that it is not only possible to distinguish between motion types but also to discriminate between persons based on the SoPF traces.more » « less
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Perceptual organization offers an elegant framework to group low-level features that are likely to come from a single object. We offer a novel strategy to adapt this grouping process to objects in a domain. Given a set of training images of objects in context, the associated learning process decides on the relative importance of the basic salient relationships such as proximity, parallelness, continuity, junctions, and common region toward segregating the objects from the background. The parameters of the grouping process are cast as probabilistic specifications of Bayesian networks that need to be learned. This learning is accomplished using a team of stochastic automata in an N-player cooperative game framework. The grouping process, which is based on graph partitioning is able to form large groups from relationships defined over a small set of primitives and is fast. We statistically demonstrate the robust performance of the grouping and the learning frameworks on a variety of real images. Among the interesting conclusions is the significant role of photometric attributes in grouping and the ability to form large salient groups from a set of local relations, each defined over a small number of primitives.more » « less