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Moving Object Detection from Optical Flow without Empirical Thresholds
Naoya OHTA Kenichi KANATANI Kazuhiro KIMURA
IEICE TRANSACTIONS on Information and Systems
Publication Date: 1998/02/25
Print ISSN: 0916-8532
Type of Manuscript: LETTER
Category: Image Processing,Computer Graphics and Pattern Recognition
geometric AIC, model selection, optical flow, moving object detection, statistical inference,
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We show that moving objects can be detected from optical flow without using any knowledge about the magnitude of the noise in the flow or any thresholds to be adjusted empirically. The underlying principle is viewing a particular interpretation about the flow as a geometric model and comparing the relative "goodness" of candidate models measured by the geometric AIC.