Exploiting high-level coherence information to optimize distributed shared state
Proceedings of the ninth ACM SIGPLAN symposium on Principles and practice of parallel programming
Using Interaction Signatures to Find and Label Chairs and Floors
IEEE Pervasive Computing
Learning Functional Object-Categories from a Relational Spatio-Temporal Representation
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Integrating multiple viewpoints for articulated scene model aquisition
ICVS'13 Proceedings of the 9th international conference on Computer Vision Systems
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We present the theory behind a novel unsupervised method for discovering quasi-static objects, objects that are stationary during some interval of observation, within image sequences acquired by any number of uncalibrated cameras. For each pixel 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 arrivals and departures explain the events. The paper specifies observability conditions under which the global schedule can be established and presents the QSL algorithm that generates the maximally-informative mapping of pixels' observations onto the objects they stem from. 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.