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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics
Hdioud, Boutaina
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Computer Science & IT Electrical & Electronics Engineering

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Notice of Retraction: Detecting and Shadows in the HSV Color Space using Dynamic Thresholds Hdioud, Boutaina; Haj Tirari, Mohammed El; Haj Thami, Rachid Oulad; Faizi, Rdouan
International Journal of Electrical and Computer Engineering (IJECE) Vol 8, No 3: June 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (313.83 KB) | DOI: 10.11591/ijece.v8i3.pp1513-1521

Abstract

Notice of Retraction-----------------------------------------------------------------------After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IAES's Publication Principles.We hereby retract the content of this paper. Reasonable effort should be made to remove all previous references to this paper.The presenting author of this paper has the option to appeal this decision by contacting ijece@iaesjournal.com.-----------------------------------------------------------------------The detection of moving objects in a video sequence is an essential step in almost all the systems of vision by computer. However, because of the dynamic change in natural scenes, the detection of movement becomes a more difficult task. In this work, we propose a new method for the detection moving objects that is robust to shadows, noise and illumination changes. For this purpose, the detection phase of the proposed method is an adaptation of the MOG approach where the foreground is extracted by considering the HSV color space. To allow the method not to take shadows into consideration during the detection process, we developed a new shade removal technique based on a dynamic thresholding of detected pixels of the foreground. The calculation model of the threshold is established by two statistical analysis tools that take into account the degree of the shadow in the scene and the robustness to noise.  Experiments undertaken on a set of video sequences showed that the method put forward provides better results compared to existing methods that are limited to using static thresholds.
Notice of Retraction: Detecting and Shadows in the HSV Color Space Using Dynamic Thresholds Hdioud, Boutaina; El Haj Tirari, Mohammed; Oulad Haj Thami, Rachid; Faizi, Rdouan
Bulletin of Electrical Engineering and Informatics Vol 7, No 1: March 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (499.567 KB) | DOI: 10.11591/eei.v7i1.893

Abstract

Notice of Retraction-----------------------------------------------------------------------After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IAES's Publication Principles.We hereby retract the content of this paper. Reasonable effort should be made to remove all previous references to this paper.The presenting author of this paper has the option to appeal this decision by contacting beei@iaescore.com.-----------------------------------------------------------------------The detection of moving objects in a video sequence is an essential step in almost all the systems of vision by computer. However, because of the dynamic change in natural scenes, the detection of movement becomes a more difficult task. In this work, we propose a new method for the detection moving objects that is robust to shadows, noise and illumination changes. For this purpose, the detection phase of the proposed method is an adaptation of the MOG approach where the foreground is extracted by considering the HSV color space. To allow the method not to take shadows into consideration during the detection process, we developed a new shade removal technique based on a dynamic thresholding of detected pixels of the foreground. The calculation model of the threshold is established by two statistical analysis tools that take into account the degree of the shadow in the scene and the robustness to noise.  Experiments undertaken on a set of video sequences showed that the method put forward provides better results compared to existing methods that are limited to using static thresholds.
Co-Authors El Haj Tirari, Mohammed Faizi, Rdouan Haj Thami, Rachid Oulad Haj Tirari, Mohammed El Oulad Haj Thami, Rachid