Approach for shadow detection and removal using machine learning techniques. Issue 13 (5th October 2020)

• Record Type: Journal Article
• Authors: Shilpa, Mohankumar; Gopalakrishna, Madigondanahalli Thimmaiah; Naveena, Chikkaguddaiah
• Is Part Of: IET image processing. Volume 14:Issue 13(2020)
• Page Start: 2998
• Page End: 3005
• Publication Date: 2020-10-05
• Subjects: pattern clustering -- learning (artificial intelligence) -- image segmentation -- video signal processing -- feature selection; shadow detection -- machine learning -- two‐stage segmentation techniques -- shadow removal -- k‐means clustering -- feature selection; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2020.0001
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : In this work, the authors have proposed a method for shadow detection and removal from videos by utilising methods of machine learning. From literature, various algorithms on shadow detection and removal have been accounted with advantages and disadvantages. Here some algorithms have a need for manual alignment and predefined explicit parameters, but fail to give precise outcome in different lighting and ecological surroundings. In this work, the authors propose a three‐phase framework. In first stage, key frames are chosen by utilising features based K‐means clustering which selects the key frames using features like colour, shape and surface. In second stage, they utilised two‐stage segmentation techniques to segment the shadows by marking the region of interest. In the final step, they use threshold based segmentation to remove the shadow in videos. The performance of the proposed method is compared by performance evaluation of all state‐of‐the‐art methods. The proposed strategies are established to achieve superior results in comparison to other state‐of‐the‐art methods.