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1 May 2017Motion-seeded object-based attention for dynamic visual imagery
This paper† describes a novel system that finds and segments “objects of interest” from dynamic imagery (video) that (1)
processes each frame using an advanced motion algorithm that pulls out regions that exhibit anomalous motion, and (2)
extracts the boundary of each object of interest using a biologically-inspired segmentation algorithm based on feature
contours. The system uses a series of modular, parallel algorithms, which allows many complicated operations to be carried
out by the system in a very short time, and can be used as a front-end to a larger system that includes object recognition
and scene understanding modules. Using this method, we show 90% accuracy with fewer than 0.1 false positives per frame
of video, which represents a significant improvement over detection using a baseline attention algorithm.
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David J. Huber, Deepak Khosla, Kyungnam Kim, "Motion-seeded object-based attention for dynamic visual imagery," Proc. SPIE 10202, Automatic Target Recognition XXVII, 102020I (1 May 2017); https://doi.org/10.1117/12.2262916