An Efficient Sky Detection Algorithm From Fisheye Image Based on region classification and segment analysis

  • Houssam Bour LASTID Laboratory, Department of Physics, Faculty of Sciences, Ibn Tofail University Kenitra, Morocco
  • Youssef El Merabet LASTID Laboratory, Department of Physics, Faculty of Sciences, Ibn Tofail University Kenitra, Morocco
  • Yassine Ruichek Le2i FRE2005, CNRS, Arts et Metiers, Univ. Bourgogne Franche-Comte Belfort,France
  • Rochdi Messoussi LASTID Laboratory, Department of Physics, Faculty of Sciences, Ibn Tofail University Kenitra, Morocco
  • Ibtissam Benmiloud LASTID Laboratory, Department of Physics, Faculty of Sciences, Ibn Tofail University Kenitra, Morocco
Keywords: Region classification, RGB color descriptor, Segment analysis, LSD, Hellinger kernel-based distance.

Abstract

In this paper, an efficient approach for automatic and accurate sky region detection from fisheye images is proposed. The proposed approach starts by segmenting the acquired image into regions using Statistical Region Merging method. After that, the segmented regions are characterized using local RGB color descriptor using image quantization. The next step consists of classifying the characterized regions into sky and non-sky regions by using maximal similarity based region classification through Hellinger kernel-based distance. In order to improve the obtained region classification results, a segment analysis based technique using Line Segment Detector is proposed. Experimental results prove the robustness and performance of the proposed procedure.

 

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Published
2017-09-01
Section
Special Issue : 1st International Conference on Affective computing, Machine Learning and Intelligent Systems