We present a novel off-line algorithm for target segmentation and tracking in video. In our approach, video data is represented by a multi-label Markov Random Field model, and segmentation is accomplished by finding the minimum energy label assignment. We propose a novel energy formulation which incorporates both segmentation and motion estimation in a single framework. Our energy functions enforce motion coherence both within and across frames. We utilize state-of-the-art methods to efficiently optimize over a large number of discrete labels. In addition, we introduce a new ground-truth dataset, called SegTrack, for the evaluation of segmentation accuracy in video tracking. We compare our method with two recent on-line tracking algorithms and provide quantitative and qualitative performance comparisons.
We present a novel multi-planar display system based on an uncalibrated projector-camera pair. Our system exploits the juxtaposition of planar surfaces in a room to create ad-hoc visualization and display capabilities. In an office setting, for example, a desk pushed against a wall provides two perpendicular surfaces that can simultaneously display elevation and plan views of an architectural model. In contrast to previous room-level projector-camera systems, our method is based on a flexible calibration procedure that requires a minimum amount of information for the geometry of the multi-planar surface scenario. A number of display configurations can be created on any available planar surfaces using a single commodity projector and camera. The key to our calibration approach is an efficient technique for simultaneously localizing multiple planes and a robust planar metric rectification method which can tolerate a restricted camera field-of-view and requires no special calibration objects. We demonstrate the robustness of our calibration method using real and synthetic images and present several applications of our display system.
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