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Quasi-static Object Discovery (QsOD)
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Quasi-static Object Discovery (QsOD) icon
We discover quasi-static objects, objects that are stationary during some interval of observation, across image sequences acquired by any number of completely uncalibrated cameras using only temporal (no spatial) information.

Brandon Sanders (Ph.D. Thesis Work) Randal Nelson (Thesis Advisor) Rahul Sukthankar (Summer Advisor 2001) Dana Ballard (Thesis Committee) Chris Brown (Thesis Committee) Kyros Kutulakos (Thesis Committee) A. Murat Tekalp (Thesis Committee)
Quasi-static Object Discovery (QsOD) figure
Brandon Sanders
OD Results on a 3 camera sequence in which the spoon and can arrive simultaneously, the keyboard is always partially occluded by either the bowl or the can, and the can and keyboard mutually occlude each other. Temporal information alone is sufficient to group the pixels in and across the uncalibrated cameras.

The Quasi-static Object Discovery project is part of a larger effort to equip computers with spatial awareness. Our temporal object discoverer (TOD) provides percepts of objects to other processes that reason about relationships between objects and actions involving objects. TOD discovers quasi-static objects, objects that are stationary during some interval of observation, within image sequences acquired by any number of completely uncalibrated cameras. In this project we ignore spatial information (for example neighborhood relationships) and concentrate on a theoretically well founded treatment of temporal information. Our framework ignores distracting motion, correctly deals with complicated occlusions, and naturally groups observations across cameras. The sets of 2D masks we recover are suitable for unsupervised training and initialization of object recognition and tracking systems. For each pixel (sensel) we generate a signature that encodes the pixel's temporal structure. Using the set of temporal signatures gathered across views, we hypothesize a global schedule of events and a small set of objects whose arrival and departures explain the events. Under certain observability conditions, we provably create a valid and complete schedule of all quasi-static objects in the scene. Likewise, our Quasi-Static Labeling algorithm generates the maximally-informative mapping of pixels' observations onto the objects they stem from.
http://www.cs.rochester.edu/~sanders/objectdiscovery
Object Discovery, OD, vision, TCC, temporally coherent, temporally coherent cluster, Brandon Sanders, Rahul Sukthankar, Randal Nelson, Segmentation, Quasi static, temporal, home, homepage, home page, URCS, University of Rochester Computer Science Department, Computer Vision

Sanders_A_Theory_of_the_Quasi-static_World_ICPR_2002 Sanders_The_OD_Theory_of_TOD_AAAI_2002

Active (February 06, 2003)