Browsing by Author "Lin, Hsueh-Yi Sean"

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Hierarchical tensor approximation of multidimensional visual data
(2008-01-01)
Wu, Qing
;
Xia, Tian
;
Chen, Chun
;
Lin, Hsueh-Yi Sean
;
Wang, Hongcheng
Visual data comprise of multiscale and inhomogeneous signals. In this paper, we exploit these characteristics and develop a compact data representation technique based on a hierarchical tensor-based transformation. In this technique, an original multidimensional data set is transformed into a hierarchy of signals to expose its multiscale structures. The signal at each level of the hierarchy is further divided into a number of smaller tensors to expose its spatially inhomogeneous structures. These smaller tensors are further transformed and pruned using a tensor approximation technique. Our hierarchical tensor approximation supports progressive transmission and partial decompression. Experimental results indicate that our technique can achieve higher compression ratios and quality than previous methods, including wavelet transforms, wavelet packet transforms, and single-level tensor approximation. We have successfully applied our technique to multiple tasks involving multidimensional visual data, including medical and scientific data visualization, data-driven rendering, and texture synthesis.
Visual salience-guided mesh decomposition
(2007-01-01)
Lin, Hsueh-Yi Sean
;
Liao, Hong-Yuan Mark
;
Lin, Ja-Chen
;
資訊工程學系
;
Department of Computer Science
In this paper, we propose a novel mesh-decomposition scheme called "visual salience-guided mesh decomposition." The concept of "part salience," which originated in cognitive Psychology, asserts that the salience of a part can be determined by (at least) three factors: the protrusion, the boundary strength, and the relative size of the part. We try to convert these conceptual rules into real computational processes, and use them to guide a three-dimensional (3-D) mesh decomposition process in such a way that the significant components can be precisely identified and efficiently extracted from a given 3-D mesh. The proposed decomposition scheme not only identifies the parts' boundaries defined by the minima rule, but also labels each part with a quantitative degree of visual salience during the mesh decomposition process. The experimental results show that the proposed scheme is indeed effective and powerful in decomposing a 3-D mesh into its significant components.