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In this paper, we propose a novel generative tracking approach based on discriminative sparse feature selection. The sparse features are the discriminative sparse representation of samples, which are achieved by learning a compact and discriminative dictionary. Besides the target templates, the proposed approach also incorporates the close-background(More)
The current state-of-the-art edge-preserving decomposition techniques may not be able to fully separate textures while preserving edges. This may generate artifacts in some applications, e.g., edge detection, texture transfer, etc. To solve this problem, a novel image decomposition approach based on explicit texture separation from large scale components of(More)
Example-based color transfer is a critical operation in image editing but easily suffers from some corruptive artifacts in themapping process. In this paper, we propose a novel unified color transfer framework with corruptive artifacts suppression, which performs iterative probabilistic color mapping with self-learning filtering scheme andmultiscale detail(More)
We track the object by separating it from the surrounding with an ensemble of boosted classifiers, which are trained in a discriminative feature space that is determined on the fly. Contour refinement and weight thresholding techniques are used to select good examples for training. While tracking, location calibration and scale adaptation are used to(More)
According to the anisotropic property in most real-world cloth for virtual fitting, this paper proposes a novel dynamic cloth simulation method via geometric deformation energy model that preserves geometric features well to achieve cloth behaviors with various material effects. We first construct an objective deformation energy with the terms including(More)
In this paper, we propose a novel collaborative appearance model for robust human tracking by exploiting both object and motion information in the bayesian framework. In contrast to most existing methods which use low or high-level visual cues, we use mid-level visual cues via superpixel with sufficient structure information to represent the object. In our(More)
Human visual attention system tends to be attracted to perceptual feature points on 3D model surfaces. However, purely geometric-based feature metrics may be insufficient to extract perceptual features, because they tend to detect local structure details. Intuitively, the perceptual importance degree of vertex is associated with the height of its geometry(More)