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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 pixel 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 trackings systems.