2020
DOI: 10.5565/rev/elcvia.1176
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Development of transition region based methods for image segmentation

Abstract: In this thesis, some transition region based segmentation approaches have developed to perform image segmentation for grayscale and colour images.In computer vision and image understanding applications, image segmentation is an important pre-processing step. The main goal of the segmentation process is the separation of foreground region from background region. The segmentation approaches are application specific and do not work well for both grayscale and colour image segmentation. For any image consisting of… Show more

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Cited by 1 publication
(1 citation statement)
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“…In recent years, researches on fish image detection segmentation based on deep learning have attracted great attention. Chen, Sun & Shang (2017) proposed an automatic fish classification system based on deep learning, Akgül, Çalik & Töreyın (2020) proposed fish detection in turbid underwater, Li, Tang & Gao (2017) proposed a deep and lightweight network for detecting fish, Wang et al (2020) proposed a detection of abnormal behaviors of underwater fish using artificial intelligence techniques, Alshdaifat, Talib & Osman (2020) proposed a deep learning framework for segmentation of fish with underwater videos, Knausgård et al (2022) proposed a method for detecting and classificating temperate fish, Parida (2019) proposed a hybrid transition region color image segmentation method based on dual transition region extraction for fish image segmentation applications. Lei, Ouyang & Xu (2018) proposed an image segmentation method based on equivalent 3D entropy and artificial fish population optimization algorithm, which is more efficient than the traditional 3D entropy method and the equivalent 3D entropy method.…”
Section: Introductionmentioning
confidence: 99%
“…In recent years, researches on fish image detection segmentation based on deep learning have attracted great attention. Chen, Sun & Shang (2017) proposed an automatic fish classification system based on deep learning, Akgül, Çalik & Töreyın (2020) proposed fish detection in turbid underwater, Li, Tang & Gao (2017) proposed a deep and lightweight network for detecting fish, Wang et al (2020) proposed a detection of abnormal behaviors of underwater fish using artificial intelligence techniques, Alshdaifat, Talib & Osman (2020) proposed a deep learning framework for segmentation of fish with underwater videos, Knausgård et al (2022) proposed a method for detecting and classificating temperate fish, Parida (2019) proposed a hybrid transition region color image segmentation method based on dual transition region extraction for fish image segmentation applications. Lei, Ouyang & Xu (2018) proposed an image segmentation method based on equivalent 3D entropy and artificial fish population optimization algorithm, which is more efficient than the traditional 3D entropy method and the equivalent 3D entropy method.…”
Section: Introductionmentioning
confidence: 99%