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Please use this identifier to cite or link to this item: https://libeldoc.bsuir.by/handle/123456789/42095
Title: Multiple seeded region growing algorithm for image segmentation using local extrema
Authors: Nguyen, A. T.
Dai, X. L.
Tsviatkou, V. Yu.
Цветков, В. Ю.
Keywords: материалы конференций
local extrema
image segmentation
Issue Date: 2020
Publisher: БГУИР, РБ
Citation: Nguyen, A. T. Multiple seeded region growing algorithm for image segmentation using local extrema / A. T. Nguyen, X. L. Dai, V. Yu. Tsviatkou // Телекоммуникации: сети и технологии, алгебраическое кодирование и безопасность данных = Telecommunications: Networks and Technologies, Algebraic Coding and Data Security : материалы междунар. науч.-техн. семинара, Минск, ноябрь–декабрь 2020 г. / Белорусский государственный университет информатики и радиоэлектроники ; редкол.: М. Н. Бобов [и др.]. – Минск, 2020. – С. 5–12.
Abstract: In this paper, a multiple seeded region growing technique for image segmentation is presented. Conventional image segmentation techniques using region growing requires initial seeds selection, which increases computational cost and execution time. To overcome this problem, a seeded region growing technique for image segmentation is proposed, which starts from searching for local extrema of the image using morphology as the initial seeds, whose coordinates are saved in a pair of static FIFO queues, used for wave region growing. It grows regions according to the extreme values quasi-parallel. We use intensity based similarity index for the grow regions and adaptive threshold is used to calculate the criteria for the grow new waves. We apply the proposed algorithm to the Berkley segmentation dataset and discuss results based on F and SSIM evaluation functions that show efficient segmentation.
URI: https://libeldoc.bsuir.by/handle/123456789/42095
Appears in Collections:Телекоммуникации 2020

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