Over the last decades several approaches were introduced to deal with cast shadows in background subtraction applications. However, very few algorithms exist that address the same problem for still images. In this paper we propose a figure ground segmentation algorithm to segment objects in still images affected by shadows. Instead of modeling the shadow directly in the segmentation process our approach works actively by first segmenting an object and then testing the resulting boundary for the presence of shadows and resegmenting again with modified segmentation parameters. In order to get better shadow boundary detection results we introduce a novel image preprocessing technique based on the notion of the image density map. This map improves the illumination invariance of classical filterbank based texture description methods. We demonstrate that this texture feature improves shadow detection results. The resulting segmentation algorithm achieves good results on a new figure ground segmentation dataset with challenging illumination conditions.
A. Ecins, C. Fermüller, Y. Aloimonos.
Shadow-Free Segmentation in Still Images Using Local Density Measure
International Conference on Computational Photography (ICCP), May 2014
[Paper]
[Poster]
[Slides]
[Bibtex]
A. Ecins, C. Fermüller, Y. Aloimonos.
Shadow-Free Segmentation in Still Images Using Local Density Measure
Perceptual Organization Workshop (CVPR), June 2014
[Slides]