RESUMO
With the increasing availability of high-resolution images, videos, and 3D models, the demand for scalable large data processing techniques increases. We introduce a method of sparse dictionary learning for edit propagation of large input data. Previous approaches for edit propagation typically employ a global optimization over the whole set of pixels (or vertexes), incurring a prohibitively high memory and time-consumption for large input data. Rather than propagating an edit pixel by pixel, we follow the principle of sparse representation to obtain a representative and compact dictionary and perform edit propagation on the dictionary instead. The sparse dictionary provides an intrinsic basis for input data, and the coding coefficients capture the linear relationship between all pixels and the dictionary atoms. The learned dictionary is then optimized by a novel scheme, which maximizes the Kullback-Leibler divergence between each atom pair to remove redundant atoms. To enable local edit propagation for images or videos with similar appearance, a dictionary learning strategy is proposed by considering range constraint to better account for the global distribution of pixels in their feature space. We show several applications of the sparsity-based edit propagation, including video recoloring, theme editing, and seamless cloning, operating on both color and texture features. Our approach can also be applied to computer graphics tasks, such as 3D surface deformation. We demonstrate that with an atom-to-pixel ratio in the order of 0.01% signifying a significant reduction on memory consumption, our method still maintains a high degree of visual fidelity.
RESUMO
BACKGROUND: At present, bone defects usually repaired by autologous bone, allogenic bone, synthetic bone substitutes and other methods, which received poor clinical results. Preliminary studies have shown that adipose-derived mesenchymal stem cells (ADSCs) possess strong proliferation ability and differentiation potential, and can be induced differentiate into bone. OBJECTIVE: To analyze the application of ADSCs in bone tissue engineering, and to identify whether ADSCs can be used as seed cells in bone tissue engineering. METHODS: The databases of PubMed (1999-01/2008-12) and Tongfang (2003-01/2008-12) was retrieved using key words of "adipose tissue-derived mesenchymal stem cells, adipose mesenchymal stem cells, adipose stem cell; osteogenic induction, osteogenic inducement, bone induction, osteoblastic induced; chondroblast induction, cartilage induction; bone tissue engineering, tissue engineering bone, tissue engineering of bone". RESULTS AND CONCLUSION: A total of 361 literatures were collected, including 246 in Chinese and 115 in English. Totally 29 literatures were accordant with the study criteria. ADSCs is a truly multi-directional differentiation potential cells, which possess strong amplification and self-renewal potential, and can be directional differentiated into osteoblasts, cartilage cells, bone cells and muscle cells. It can be used as seed cells in bone tissue engineering when matching appropriate stents.
