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Has Fulltext
- yes (2) (remove)
Institute
- Junior-Professur Augmented Reality (2) (remove)
Keywords
- 54.73 (1)
- Bildverarbeitung (1)
- CAMShift (1)
- CGI <Computergraphik> (1)
- Computer graphics (1)
- Computer vision (1)
- GPU Programming (1)
- Image processing (1)
- Kernel-Based Tracking (1)
Year of publication
- 2009 (2) (remove)
CAMShift is a well-established and fundamental algorithm for kernel-based visual object tracking. While it performs well with objects that have a simple and constant appearance, it is not robust in more complex cases. As it solely relies on back projected probabilities it can fail in cases when the object's appearance changes (e.g. due to object or camera movement, or due to lighting changes), when similarly colored objects have to be re-detected or when they cross their trajectories. We propose extensions to CAMShift that address and resolve all of these problems. They allow the accumulation of multiple histograms to model more complex object appearance and the continuous monitoring of object identi- ties to handle ambiguous cases of partial or full occlusion. Most steps of our method are carried out on the GPU for achieving real-time tracking of multiple targets simultaneously. We explain an ecient GPU implementations of histogram generation, probability back projection, im- age moments computations, and histogram intersection. All of these techniques make full use of a GPU's high parallelization.
Superimposing Dynamic Range
(2009)
Replacing a uniform illumination by a high-frequent illumination enhances the contrast of observed and captured images. We modulate spatially and temporally multiplexed (projected) light with reflective or transmissive matter to achieve high dynamic range visualizations of radiological images on printed paper or ePaper, and to boost the optical contrast of images viewed or imaged with light microscopes.