Illumination estimation and cast shadow detection through a higher-order graphical model

CVPR 2011 ◽  
2011 ◽  
Author(s):  
Alexandros Panagopoulos ◽  
Chaohui Wang ◽  
Dimitris Samaras ◽  
Nikos Paragios
Keyword(s):  
Graphical Model ◽  
Higher Order ◽  
Shadow Detection ◽  
Illumination Estimation ◽  
Cast Shadow

Adaptive Moving Cast Shadow Detection

2012 ◽  
pp. 399-409 ◽  
Author(s):  
Guizhi Li ◽  
Lei Qin ◽  
Qingming Huang
Keyword(s):  
Shadow Detection ◽  
Cast Shadow

Cast Shadow Detection Based on Semi-supervised Learning

2012 ◽  
pp. 19-26 ◽  
Author(s):  
Salma Kammoun Jarraya ◽  
Rania Rebai Boukhriss ◽  
Mohamed Hammami ◽  
Hanene Ben-Abdallah
Keyword(s):  
Supervised Learning ◽  
Shadow Detection ◽  
Cast Shadow

scholarly journals Moving Cast Shadow Detection

10.5772/4953 ◽  
2007 ◽  
Author(s):  
Wei Zhang ◽  
Q.M. Jonathan ◽  
Xiangzhong Fang
Keyword(s):  
Shadow Detection ◽  
Cast Shadow

scholarly journals Use of Gradient-Based Shadow Detection for Estimating Environmental Illumination Distribution

2018 ◽  
Vol 8 (11) ◽  
pp. 2255 ◽  
Author(s):  
Sangyoon Lee ◽  
Hyunki Hong
Keyword(s):  
Three Dimensional ◽  
Local Features ◽  
Shadow Detection ◽  
Reference Object ◽  
Light Sources ◽  
Illumination Estimation ◽  
Shadow Boundary ◽  
Sampling Position ◽  
Gradient Based ◽  
Edge Based

Environmental illumination information is necessary to achieve a consistent integration of virtual objects in a given image. In this paper, we present a gradient-based shadow detection method for estimating the environmental illumination distribution of a given scene, in which a three-dimensional (3-D) augmented reality (AR) marker, a cubic reference object of a known size, is employed. The geometric elements (the corners and sides) of the AR marker constitute the candidate’s shadow boundary; they are obtained on a flat surface according to the relationship between the camera and the candidate’s light sources. We can then extract the shadow regions by collecting the local features that support the candidate’s shadow boundary in the image. To further verify the shadows passed by the local features-based matching, we examine whether significant brightness changes occurred in the intersection region between the shadows. Our proposed method can reduce the unwanted effects caused by the threshold values during edge-based shadow detection, as well as those caused by the sampling position during point-based illumination estimation.

Cast shadow detection in video segmentation

2005 ◽  
Vol 26 (1) ◽  
pp. 91-99 ◽  
Author(s):  
Dong Xu ◽  
Xuelong Li ◽  
Zhengkai Liu ◽  
Yuan Yuan
Keyword(s):  
Video Segmentation ◽  
Shadow Detection ◽  
Cast Shadow
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