Shadow Detection and Removal using Artificial Neural Network

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Chhabirani Mahapatra, Prasant Kumar Dash

Abstract

Shadow detection and removal is an important task when dealing with colour images. Shadows are generated by a local and relative absence of light or a shadow appears on an area when the light from a source cannot reach the area due to obstruction by an object. Shadows are, first of all, a local decrease in the amount of light that reaches a surface. Secondly, they are a local change in the amount of light rejected by a surface toward the observer. However, they cause problems in computer vision applications, such as segmentation, object detection and object counting. Thus shadow detection and removal is a preprocessing task in computer vision. This thesis work proposes a simple method to detect and remove shadow from a single RGB image using artificial neural network. A shadow detection method is selected based on the phenomena of back propagation algorithm. Back propagation artificial neural network classifier has been used to train and test the neural network based on the extracted feature. The shadow removal is done by multiplying the shadow region by a constant.

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How to Cite
, C. M. P. K. D. (2018). Shadow Detection and Removal using Artificial Neural Network. International Journal on Future Revolution in Computer Science &Amp; Communication Engineering, 4(4), 732–737. Retrieved from http://ijfrcsce.org/index.php/ijfrcsce/article/view/1602
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