Shadow Detection Method Based on Shadow Model with Normalized Vector Distance and Edge

2013 ◽  
pp. 103-113
Author(s):  
Shuya Ishida ◽  
Shinji Fukui ◽  
Yuji Iwahori ◽  
M. K. Bhuyan ◽  
Robert J. Woodham
Keyword(s):  
Detection Method ◽  
Shadow Detection

Construction of Shadow Model by Robust Features to Illumination Changes

2013 ◽  
Vol 1 (4) ◽  
pp. 45-55 ◽  
Author(s):  
Shuya Ishida ◽  
Shinji Fukui ◽  
Yuji Iwahori ◽  
M. K. Bhuyan ◽  
Robert J. Woodham
Keyword(s):  
Detection Method ◽  
Harmful Effect ◽  
Color Space ◽  
Shadow Detection ◽  
Edge Information ◽  
Correlation Image ◽  
Illumination Changes ◽  
Sign Correlation ◽  
The Difference ◽  
Background Image

Methods in the field of computer vision need a shadow detection because shadows often have a harmful effect on a result. A new shadow detection method is proposed in this paper. The proposed method is based on the shadow model. The model is constructed by robust features to illumination changes. The proposed method uses the difference of chrominance (UV) components of luma chrominance (YUV) color space between the background image and the observed image, Normalized Vector Distance, Peripheral Increment Sign Correlation image and edge information. These features remove shadow effects in part. The proposed method can construct the effective shadow model by using the features. In addition, the result is improved by the region based method and the shadow model is updated. The proposed method can extract shadows accurately. Results are demonstrated by the experiments using the real videos.

Cat Swarm Based Shadow Detection Method

2018 ◽  
Author(s):  
Kazim Firildak ◽  
Mucahit Karaduman ◽  
Muhammed Fatih Talu ◽  
Celaleddin Yeroglu
Keyword(s):  
Detection Method ◽  
Shadow Detection
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