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Clustering-based shadow detection from images with texture and color analysis

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Abstract

Shadow is inexorable essential in a scene created due to the presence of illumination variation and obstructed object. Shadow depicts information of images such as shape, position, orientation, and camera parameters. But sometimes, shadow degrades the quality of the image while objection segmentation, merging, scene analysis, scene interpretation, object recognition, and tracking. The presented paper aims are to provide a comprehensive technique to detect both indistinct and hard shadows from images. Firstly, a unique combination of ‘luminance (L)’, ‘green-red (a*)’ components, and ‘blue-yellow (b*)’ components of CIELab color space is used to differentiate shadows from objects. After color transformation, shadow regions are differentiated from background and object with an amalgamation of clustering techniques with the help of texture information. According to the extracted regions classification, finally suspected shadow regions are obtained. Experimental results verify that it robustly detects vague and hard shadows in the image.

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Authors and Affiliations

  1. Electronics and Communication Engineering Department, Thapar Institute of Engineering and Technology, Patiala, Punjab, India

    Gittaly Dhingra, Vinay Kumar & Hem Dutt Joshi

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  1. Gittaly Dhingra
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  2. Vinay Kumar
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  3. Hem Dutt Joshi
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Correspondence to Gittaly Dhingra.

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Dhingra, G., Kumar, V. & Joshi, H.D. Clustering-based shadow detection from images with texture and color analysis. Multimed Tools Appl 80, 33763–33778 (2021). https://doi.org/10.1007/s11042-021-11427-5

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  • DOI: https://doi.org/10.1007/s11042-021-11427-5

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