The role of perceptual organization in motion analysis has heretofore been minimal. In this work we present a simple but powerful computational model and associated algorithms based on the use of perceptual organizational principles, such as temporal coherence (or common fate) and spatial proximity, for motion segmentation. The computational model does not use the traditional frame by frame motion analysis; rather it treats an image sequence as a single 3D spatio-temporal volume. It endeavors to find organizations in this volume of data over three levels-signal, primitive, and structural. The signal level is concerned with detecting individual image pixels that are probably part of a moving object. The primitive level groups these individual pixels into planar patches, which we call the temporal envelopes. Compositions of these temporal envelopes describe the spatio-temporal surfaces that result from object motion. At the structural level, we detect these compositions of temporal envelopes by utilizing the structure and organization among them. The algorithms employed to realize the computational model include 3D edge detection, Hough transformation, and graph based methods to group the temporal envelopes based on Gestalt principles. The significance of the Gestalt relationships between any two temporal envelopes is expressed in probabilistic terms. One of the attractive features of the adopted algorithm is that it does not require the detection of special 2D features or the tracking of these features across frames. We demonstrate that even with simple grouping strategies, we can easily handle drastic illumination changes, occlusion events, and multiple moving objects, without the use of training and specific object or illumination models. We present results on a large variety of motion sequences to demonstrate this robustness. c 2002 Elsevier Science (USA)

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This paper reports the design and implementation of a secure, wide area network, distributed filesystem by the ExTENCI project (Extending Science Through Enhanced National Cyberinfrastructure) based on lustre. The filesystem is used for remote access to analysis data from the Compact Muon Solenoid (CMS) experiment at the Large Hadron Collider (LHC), and from the Lattice Quantum ChromoDynamics (LQCD) project. Security is provided for by kerberos and reinforced with additional finegrained control using lustre ACLs and quotas. We show the impact of using kerberized lustre on the IO rates of CMS and LQCD applications on client nodes, both real and virtual. Preconfigured images of lustre virtual clients containing the complete software stack ease the difficulty of managing these systems.

The front end of many motion analysis algorithms is usually a process that generates bounding boxes around each moving object, roughly segmenting the objects from the background. Processing to finely define the moving object boundary can follow, but only within these rough bounding boxes. In this paper, we consider a method that exploits the structure and organization in the spatio-temporal block (XYT) of motion data to create bounding boxes around moving objects. This method has been shown to be robust with respect to illumination changes,noise, and occlusion events. This algorithm, however, begins with a 3D edge detection step across a sequence of images, which is a time consuming process. We have mapped this 3D edge detection to run on any MPI enabled parallel computer, thus achieving significant speedups especially for large image frames. We present results on sequences of various sizes and lengths from the recently formulated Human ID Gait Challenge Problem dataset. We compare the quality of the automatically created bounding boxes with the semi-autonomously generated boxes that come with the Gait Challenge dataset.