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Adaptive mean shift for skin image segmentation


Article Information

Title: Adaptive mean shift for skin image segmentation

Authors: Kyoung-Mi Lee

Journal: ARPN Journal of Engineering and Applied Sciences

HEC Recognition History
Category From To
Y 2023-07-01 2024-09-30
Y 2022-07-01 2023-06-30
Y 2021-07-01 2022-06-30
X 2020-07-01 2021-06-30

Publisher: Khyber Medical College, Peshawar

Country: Pakistan

Year: 2015

Volume: 10

Issue: 16

Language: English

Categories

Abstract

The mean-shift clustering is an efficient technique for color image segmentation by dividing an image into homogeneous regions. The main drawback of mean-shift clustering is to use a fixed scale, which directly determines to use a fixed homogeneity. Since each region could have different homogeneity, using a fixed scale has a problem to segment well. To resolve this problem, we incorporate multi-resolution search by providing different scales to regions. The proposed algorithm starts initially at lowest resolution first and then proceeds to higher resolution where the search results are only refined. The proposed algorithm is applied to skin color segmentation.


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