TY - GEN
T1 - Intrinsic image decomposition from pair-wise shading ordering
AU - Liu, Yuanliu
AU - Yuan, Zejian
AU - Zheng, Nanning
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2015.
PY - 2015
Y1 - 2015
N2 - An image is composed by several intrinsic images including the reflectance and the shading. In this paper, we propose a novel approach to infer the shading image from shading orders between pairs of pixels. The pairwise shading orders are measured by two types of methods: the brightness order and the low-order fittings of local shading field. The brightness order is a non-local measure, which does not rely on local gradients, and can be applied to any pair of pixels. In contrast, the loworder fittings are effective for pixel pairs within local regions of smooth shading. These methods are complementary, and they together can capture both the local smoothness and non-local order structure of shading. Further, we evaluate the reliability of these methods by their robustness to perturbations, including the errors in reflectance clustering, the variations of reflectance and shading, and the spatial distances. We adopt a strategy of local competition and global Angular Embedding to integrate pairwise orders into a globally consistent order, taking their reliability into account. Experiments on the MIT Intrinsic Image dataset and the UIUC Shadow dataset show that our model can effectively recover the shading image including those deeply shadowed areas.
AB - An image is composed by several intrinsic images including the reflectance and the shading. In this paper, we propose a novel approach to infer the shading image from shading orders between pairs of pixels. The pairwise shading orders are measured by two types of methods: the brightness order and the low-order fittings of local shading field. The brightness order is a non-local measure, which does not rely on local gradients, and can be applied to any pair of pixels. In contrast, the loworder fittings are effective for pixel pairs within local regions of smooth shading. These methods are complementary, and they together can capture both the local smoothness and non-local order structure of shading. Further, we evaluate the reliability of these methods by their robustness to perturbations, including the errors in reflectance clustering, the variations of reflectance and shading, and the spatial distances. We adopt a strategy of local competition and global Angular Embedding to integrate pairwise orders into a globally consistent order, taking their reliability into account. Experiments on the MIT Intrinsic Image dataset and the UIUC Shadow dataset show that our model can effectively recover the shading image including those deeply shadowed areas.
UR - https://www.scopus.com/pages/publications/84929616320
U2 - 10.1007/978-3-319-16814-2_6
DO - 10.1007/978-3-319-16814-2_6
M3 - 会议稿件
AN - SCOPUS:84929616320
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 83
EP - 98
BT - Computer Vision - ACCV 2014 - 12th Asian Conference on Computer Vision, Revised Selected Papers
A2 - Cremers, Daniel
A2 - Saito, Hideo
A2 - Reid, Ian
A2 - Yang, Ming-Hsuan
PB - Springer Verlag
T2 - 12th Asian Conference on Computer Vision, ACCV 2014
Y2 - 1 November 2014 through 5 November 2014
ER -