文献类型：期刊文献

英文题名：Outdoor Shadow Estimating Using Multiclass Geometric Decomposition Based on BLS

作者：Chen, Zhihua[1];Gao, Ting[1];Sheng, Bin[2];Li, Ping[3];Chen, C. L. Philip[4,5,6]

机构：[1]East China Univ Sci & Technol, Dept Comp Sci & Engn, Shanghai 200237, Peoples R China;[2]Shanghai Jiao Tong Univ, Dept Comp Sci & Engn, Shanghai 200240, Peoples R China;[3]Macau Univ Sci & Technol, Fac Informat Technol, Macau 999078, Peoples R China;[4]Univ Macau, Fac Sci & Technol, Dept Comp & Informat Sci, Macau 999078, Peoples R China;[5]Dalian Maritime Univ, Coll Nav, Dalian 116026, Peoples R China;[6]Chinese Acad Sci, State Key Lab Management & Control Complex Syst, Inst Automat, Beijing 100190, Peoples R China

年份：2020

卷号：50

期号：5

起止页码：2152

外文期刊名：IEEE TRANSACTIONS ON CYBERNETICS

收录：;EI(收录号：20184606058115);WOS:【SCI-EXPANDED(收录号:WOS:000528622000032)】；

基金：This work was supported in part by the National Natural Science Foundation of China under Grant 61672228, Grant 61872241, Grant 61671290, Grant 61572316, Grant 61751202, Grant 61751205, Grant 61572540, and Grant 61370174, in part by the National Key Research and Development Program of China under Grant 2016YFC1300302 and Grant 2017YFE0104000, in part by the Macau Science and Technology Development Fund (FDCT) under Grant 0027/2018/A1, Grant 019/2015/A1, Grant 079/2017/A2, and Grant 024/2015/AMJ, in part by the Science and Technology Commission of Shanghai Municipality under Grant 16DZ0501100 and Grant 17411952600, in part by the Shanghai Automotive Industry Science and Technology Development Foundation under Grant 1837, and in part by the Multi-Year Research Grant of University of Macau. This paper was recommended by Associate Editor Q. Ji. (Zhihua Chen and Ting Gao contributed equally to this work.)

语种：英文

外文关键词：Lighting; Sun; Estimation; Learning systems; Feature extraction; Neural networks; Classification algorithms; Broad learning system (BLS); illumination estimating; Markov random field (MRF); multiclass integrating; shadow synthesis

摘要：Illumination is a significant component of an image, and illumination estimation of an outdoor scene from given images is still challenging yet it has wide applications. Most of the traditional illumination estimating methods require prior knowledge or fixed objects within the scene, which makes them often limited by the scene of a given image. We propose an optimization approach that integrates the multiclass cues of the image(s) [a main input image and optional auxiliary input image(s)]. First, Sun visibility is estimated by the efficient broad learning system. And then for the scene with visible Sun, we classify the information in the image by the proposed classification algorithm, which combines the geometric information and shadow information to make the most of the information. And we apply a respective algorithm for every class to estimate the illumination parameters. Finally, our approach integrates all of the estimating results by the Markov random field. We make full use of the cues in the given image instead of an extra requirement for the scene, and the qualitative results are presented and show that our approach outperformed other methods with similar conditions.