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This paper addresses the challenge of 3-D skeleton recovery by exploiting the spatio-temporal correlations of corrupted 3D skeleton sequences. A skeleton sequence is represented as a matrix. We propose a novel low-rank solution that effectively integrates both a low-rank model for robust skeleton recovery based on temporal coherence, and an articulation-graph-based isometric constraint for spatial...
Partially occluded or illuminated faces pose a significant obstacle for robust, real-world face recognition. The problem of how to characterize the error caused by occlusion or illumination is still a challenging task. There must exist some close relationship between the error metric and error distribution. However, some metric (e.g. Z2-norm) can't characterize this error distribution completely....
This paper proposes a novel image compression scheme based on the local feature descriptor - Scale Invariant Feature Transform (SIFT). The SIFT descriptor characterizes an image region invariantly to scale and rotation. It is used widely in image retrieval. By using SIFT descriptors, our compression scheme is able to make use of external image contents to reduce visual redundancy among images. The...
The paper targets denoising of multi-view images with both intra-view and inter-view redundancy exploited under the guidance of 3-D geometry constraints. A graphical model of surface patches from each view of the multi-view image sequence is proposed to model the redundancy more effectively and efficiently. Patches are clustered according to their similarities between each other measured by the geodesic...
This paper proposes an efficient image fusion scheme for compressed sensing (CS) imaging, in which fusion is performed on the random projections before reconstruction. Specifically, the measurements of multiple input images are fused into composite measurements via weighted average, in which the weights are calculated based on entropy metrics of the original measurements. Then the fused image with...
Various of manifold learning methods have been proposed to capture the intrinsic characteristic of nonlinear data. However, when confronting highly nonlinear data sets, existing algorithms may fail to discover the correct inner structure of data sets. In this paper, we proposed a new locality-based manifold learning method Neighborhood Balance Embedding. The proposed method share the same 'neighborhood...
Base on multi-resolution characteristic of wavelet transformation, a dynamic reconstruction method of image pyramids for remote sensing imagery is proposed in this paper. The method can solve the data redundant problems caused by traditional reconstruction methods, for which the correlation characteristic of the data between different pyramid layers is not used. It can effectively reduce the amount...
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