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基于支持向量回歸的灰度圖像三維形狀重構(gòu)
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國家青年科學(xué)基金資助項(xiàng)目(51005204)和內(nèi)蒙古工業(yè)大學(xué)科學(xué)研究重點(diǎn)資助項(xiàng)目(ZD201110)


Shape from Shading Based on Support Vector Regression Algorithm
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    摘要:

    為實(shí)現(xiàn)未知光源的灰度圖像高效重構(gòu)形狀,提出一種基于支持向量回歸機(jī)和粒子群優(yōu)化算法相結(jié)合的灰度圖像重構(gòu)三維表面形狀的方法,。通過研究和分析灰度重構(gòu)形狀(SFS)問題,,基于支持向量回歸機(jī)理論,,構(gòu)建了物體表面形狀與其灰度圖像間的非線性映射模型,。對未知光照方向的實(shí)際圖像進(jìn)行光源方向估計(jì),,生成對應(yīng)光照方向的訓(xùn)練樣本以提高任意光照方向下的圖像的形狀恢復(fù)精度,。為克服支持向量回歸機(jī)中各參數(shù)選取無依據(jù)的不足,,引入粒子群優(yōu)化算法主動對各參數(shù)進(jìn)行飛行尋優(yōu),,使得回歸模型為最優(yōu),,以提高形狀重構(gòu)精度。最后,,通過實(shí)例分析驗(yàn)證了所提方法的可行性及有效性,。

    Abstract:

    In order to realize the shape from shading (SFS) under unknown light source parameters of single grayscale image, an efficient SFS method was proposed based on support vector regression(SVR) and particle swarm optimization(PSO) algorithm. Based on the SVR theory, a nonlinear mapping model was constructed between the grayscale image and its 3-D surface by researching into the SFS problem. The light source of the tested actual image should be estimated to generate the training samples corresponding to the light direction. It was important to select the proper SVR parameters for improvement on 3-D reconstruction precision, and the PSO algorithm was introduced. Finally, the case study had verified the feasibility and effectiveness of the proposed SFS method.

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胡志勇,張秀芬,徐敬華.基于支持向量回歸的灰度圖像三維形狀重構(gòu)[J].農(nóng)業(yè)機(jī)械學(xué)報(bào),2012,43(2):216-220. Hu Zhiyong, Zhang Xiufen, Xu Jinghua. Shape from Shading Based on Support Vector Regression Algorithm[J]. Transactions of the Chinese Society for Agricultural Machinery,2012,43(2):216-220.

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  • 在線發(fā)布日期: 2012-02-17
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