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Outdoor Shadow Estimating Using Multiclass Geometric Decomposition Based on BLS.
IEEE Transactions on Cybernetics ( IF 11.8 ) Pub Date : 2018-11-02 , DOI: 10.1109/tcyb.2018.2875983
Zhihua Chen , Ting Gao , Bin Sheng , Ping Li , C.L. Philip Chen

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.

中文翻译:

基于BLS的多类几何分解的户外阴影估计。

照明是图像的重要组成部分,并且根据给定图像对室外场景的照明估计仍然具有挑战性,但它具有广泛的应用。大多数传统的照明估计方法需要场景中的先验知识或固定对象,这使得它们通常受给定图像的场景限制。我们提出了一种优化方法,该方法整合了图像[主输入图像和可选的辅助输入图像]的多类提示。首先,通过有效的广泛学习系统估算太阳的能见度。然后针对可见太阳的场景,通过提出的分类算法对图像中的信息进行分类,该算法结合了几何信息和阴影信息以充分利用信息。并且我们为每个类别应用各自的算法来估计照明参数。最后,我们的方法通过马尔可夫随机场综合了所有估计结果。我们充分利用给定图像中的线索,而不是对场景的额外要求,并且给出了定性结果,表明我们的方法在类似条件下优于其他方法。
更新日期:2020-04-22
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