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논문 기본 정보

자료유형
학술저널
저자정보
Kaushik Deb (Chittagong University of Engineering & Technology (CUET)) Ashraful Huq Suny (Chittagong University of Engineering & Technology (CUET))
저널정보
한국산학기술학회 SmartCR Smart Computing Review 제4권 제1호
발행연도
2014.2
수록면
23 - 33 (11page)

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초록· 키워드

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Shadows in an image can reveal information about the object’s shape and orientation, and even about the light source. Thus shadow detection and removal is a very crucial and inevitable task of some computer vision algorithms for applications such as image segmentation and object detection and tracking. This paper proposes a simple framework using the luminance, chroma: blue, chroma: red (YCbCr) color space to detect and remove shadows from images. Initially, an approach based on statistics of intensity in the YCbCr color space is proposed for detecting shadows. After the shadows are identified, a shadow density model is applied. According to the shadow density model, the image is segmented into several regions that have the same density. Finally, the shadows are removed by relighting each pixel in the YCbCr color space and correcting the color of the shadowed regions in the red-green-blue (RGB) color space. The most salient feature of our proposed framework is that after removing shadows, there is no harsh transition between the shadowed parts and non-shadowed parts, and all the details in the shadowed regions remain intact. Various shadow images were used with a variety of conditions (i.e. outdoor and semi-indoor) to test the proposed framework, and results are presented to prove its effectiveness.

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Abstract
Introduction
Related Terms
Related Works
Proposed Framework
Experiment Results and Analysis
Conclusion
References

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UCI(KEPA) : I410-ECN-0101-2015-500-002466786