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Paper WeA09.4

Wu, Qi (Carnegie Mellon Universtiy), Zhang, Wende (General Motors), Vijaya Kumar, B.V.K (Carnegie Mellon University)

Strong Shadow Removal Via Patch-Based Shadow Edge Detection

Scheduled for presentation during the Regular Session "Surveillance" (WeA09), Wednesday, May 16, 2012, 09:15−09:30, Meeting Room 9 (Sa)

2012 IEEE International Conference on Robotics and Automation, May 14-18, 2012, RiverCentre, Saint Paul, Minnesota, USA

This information is tentative and subject to change. Compiled on June 18, 2018

Keywords Computer Vision for Robotics and Automation, Intelligent Transportation Systems, Visual Learning

Abstract

Detecting objects in shadows is a challenging task in computer vision. For example, in clear path detection application, strong shadows on the road confound the detection of the boundary between clear path and obstacles, making clear path detection algorithms less robust. Shadow removal, relies on the classification of edges as shadow edges or non-shadow edges. We present an algorithm to detect strong shadow edges, which enables us to remove shadows. By analyzing the patch-based characteristics of shadow edges and non-shadow edges (e.g., object edges), the proposed detector can discriminate strong shadow edges from other edges in images by learning the distinguishing characteristics. In addition, spatial smoothing is used to further improve shadow edge detection. Numerical experiments show convincing results that shadows on the road are either removed or attenuated with few visual artifacts, which benefits the clear path detection. In addition, we show that the proposed method outperforms the state-of-art algorithms in different conditions.

 

 

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