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Integrating multiple different yet complementary feature representations has been proved to be an effective way for boosting tracking performance. This paper investigates how to perform robust object tracking in challenging scenarios by adaptively incorporating information from grayscale and thermal videos, and proposes a novel collaborative algorithm for online tracking. In particular, an adaptive...
Inspirited by the human vision mechanism, this paper discusses a hierarchical grammar model for 3D inference of man-made object from a single image. This model decomposes an object with two layers: (i) 3D parts (primitives) with 3D spatial relationship and (ii) 2D aspects with prediction (production) rules. Thus each object is represented by a set of co-related 3D primitives that are generated by...
This article presents a novel integrated approach to object of interest extraction, including learning to define target pattern and extracting by combining detection and segmentation. The learning stage captures both shape sketch and appearance information of target pattern as prior knowledge. The extraction stage utilizes a stochastic Markov Chain Monte Carlo (MCMC) algorithm under the Bayesian framework...
To automatically register foreground target in cluttered images, we present a novel hierarchical graph representation and a stochastic computing strategy in Bayesian framework. The graph representation, which contains point-(image primitives), seedgraph-, and subgraph- three levels, are built up following the primal sketch theory to capture geometric, topological, and spatial information both in local...
The skyrocketing of the oil price aroused the attention to the supply-demand relationship of the world oil market. Aiming at the incomplete information between suppliers and consumers, this paper studied the production strategy of the suppliers and the judgment of the consumers of the oil market with a signaling game model to deduce the sufficient and essential conditions of the three kinds of market...
Many vision tasks are posed as either graph partitioning (coloring) or graph matching (correspondence) problems. The former include segmentation and grouping, and the latter include wide baseline stereo, large motion, object tracking and recognition. In this paper, we present an integrated solution for both graph matching and graph partition using an effective sampling algorithm in a Bayesian framework...
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