5/30/2023 0 Comments Smart sentry detecting shadows![]() ![]() Shadow detection is used during the image segmentation. Object oriented shadow detection and removal methods are used in this paper. The objective of this paper is detect and removal of shadows play an important role in application of urban high resolution remote sensing images. We cannot get clear and quality picture for obtain the shadow in the images. Shadow will occur by sunlight or any light sources. outdoor and semi-indoor) to test the proposed framework, and results are presented to prove its effectiveness. Various shadow images were used with a variety of conditions (i.e. 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. 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. According to the shadow density model, the image is segmented into several regions that have the same density. After the shadows are identified, a shadow density model is applied. Initially, an approach based on statistics of intensity in the YCbCr color space is proposed for detecting shadows. This paper proposes a simple framework using the luminance, chroma: blue, chroma: red (YCbCr) color space to detect and remove shadows from images. ![]() 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. ![]() Shadows in an image can reveal information about the object's shape and orientation, and even about the light source. The incorporation of AHE significantly improved thequality of the detected shadow region. In order to enhance thecontrast, adaptive histogram equalization is used. But this resulting image willbe of low contrast due to the high correlation among R, G and B color channels. Itoriginates from the idea that if the minimum attenuated color channel is subtracted from the maximumattenuated one, the shadow areas become darker in the resulting TAM image. TAM uses the concept ofintensity attenuation of pixels in the shadow region which is different for the three color channels. This paper implements a shadow detection method, which is based on TricolorAttenuation Model (TAM) enhanced with adaptive histogram equalization (AHE). So the detection of shadows in images will enhance the performance of manymachine vision tasks. As a result, an object recognition system may incorrectly recognize ashadow region as an object. For a machine, it is very difficult to distinguishbetween a shadow and a real object. Shadows create significant problems in many computer vision and image analysis tasks such as objectrecognition, object tracking, and image segmentation. ![]()
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