A mean shift segmentation scheme using several pixel characteristics. (March 2021)

• Record Type: Journal Article
• Authors: Cuevas, Erik; Gálvez, Jorge; Avalos, Omar; Chavarin, Ángel
• Is Part Of: Computers & electrical engineering. Volume 90(2021)
• Publication Date: 2021-03
• Subjects: Segmentation algorithm -- Mean shift method -- Non-local mean -- Kernel density estimator (KDE) -- Clustering methods; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2021.107022
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: In this paper, a new segmentation method based on the mean shift (MS) algorithm is presented. The proposed approach divides the image into two sets of pixels: operative elements and inactive elements. In its first phase, the MS scheme considers only the operative elements. In its second stage, the results obtained by the MS method with the operative data are used to include the inactive data. During this stage, each inactive pixel is assigned to the cluster corresponding to the nearest operative pixel. As a final operation, clusters that maintain the minimal number of elements are blended with other nearby clusters. Our method has been tested against other current segmentation methods using test images extracted from the Berkley dataset. Numerical experiments demonstrate that our approach exhibits better performance in terms of consistency, quality, velocity and accuracy.