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Recognizing human action from low-resolution (LR) videos is essential for many applications including large-scale video surveillance, sports video analysis and intelligent aerial vehicles. Currently, state-of-the-art performance in action recognition is achieved by the use of dense trajectories which are extracted by optical flow algorithms. However, the optical flow algorithms are far from perfect...
This paper proposes a novel motion model for classifying general non-rigid motion into piecewise elastic motion, so as to achieve the non-rigid motion estimation without any priori shape models. Three interrelated sub-problems have to be addressed: classifying the whole motion sequence, estimating motion inside each segment and connecting the piecewise motions. In this paper, a Markov chain is used...
We present a multi-feature based object extraction algorithm in this paper. Cutting in from the angle of multi-feature, this algorithm combines the high-efficient and good performance Graph Cut framework. We add foreground shape information and motion estimation as a compensation of the instability of using single color information.
Convexity (concavity) is a bottom-up cue to assign figure-ground relation in the perceptual organization [18]. It suggests that region on the convex side of a curved boundary tend to be figural. To explore the validity of this cue in the task of salient object detection, we segment the images in a test dataset into superpixels, and then locate the concave arcs and their bounding boxes along boundary...
In this paper, we propose a new algorithm with an adaptive arbitrary support-pixel set, with an arbitrary shape and size, and adaptive support-weight according to perceptual grouping principle in the Markov random field framework. Adaptive arbitrary support-pixel set is a set of pixels which are selected based on the similarity law of the perceptual grouping principle from neighboring pixels of the...
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