9786200224668 - Design of Unsupervised Learning for Automatic Segmentation: Medical Image Segmentation de Kumar, B. Suresh (10 resultados)

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Taschenbuch. Condición: Neu. Design of Unsupervised Learning for Automatic Segmentation | Medical Image Segmentation | B. Suresh Kumar | Taschenbuch | 136 S. | Englisch | 2019 | LAP LAMBERT Academic Publishing | EAN 9786200224668 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The image segmentation is used to change or simplify the image representation for the purpose of easy understanding or quicker analysis. Image segmentation is a process of segmenting an image into groups of pixels based on some criterions. Image segmentation is the process of partitioning a digital image into multiple segments. The purpose of image segmentation is to partition an image into meaningful regions with respect to particular application. The image segmentation is used for various applications such as medical images, Satellite images, content based image retrieval, machine vision, Recognition Tasks and Video Surveillance. There are so many methods used for segmentations such as compression based methods, thresholding, and clustering. The clustering methods can be divided into two parts namely supervised and unsupervised. Supervised clustering involves predefining the cluster size for segmenting whereas unsupervised segmentation segments by its own cluster values. 136 pp. Englisch.

Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Kumar B. SureshMr.B.Suresh Kumar is doing Ph.D in Computer Science at Karpagam University, Coimbatore-21. He obtained M.Phil in Computer Science from Manonmaniam Sundaranar University, in 2003 and M.Sc. in Computer Science from Bhara.

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Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -The image segmentation is used to change or simplify the image representation for the purpose of easy understanding or quicker analysis. Image segmentation is a process of segmenting an image into groups of pixels based on some criterions. Image segmentation is the process of partitioning a digital image into multiple segments. The purpose of image segmentation is to partition an image into meaningful regions with respect to particular application. The image segmentation is used for various applications such as medical images, Satellite images, content based image retrieval, machine vision, Recognition Tasks and Video Surveillance. There are so many methods used for segmentations such as compression based methods, thresholding, and clustering. The clustering methods can be divided into two parts namely supervised and unsupervised. Supervised clustering involves predefining the cluster size for segmenting whereas unsupervised segmentation segments by its own cluster values.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 136 pp. Englisch.

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The image segmentation is used to change or simplify the image representation for the purpose of easy understanding or quicker analysis. Image segmentation is a process of segmenting an image into groups of pixels based on some criterions. Image segmentation is the process of partitioning a digital image into multiple segments. The purpose of image segmentation is to partition an image into meaningful regions with respect to particular application. The image segmentation is used for various applications such as medical images, Satellite images, content based image retrieval, machine vision, Recognition Tasks and Video Surveillance. There are so many methods used for segmentations such as compression based methods, thresholding, and clustering. The clustering methods can be divided into two parts namely supervised and unsupervised. Supervised clustering involves predefining the cluster size for segmenting whereas unsupervised segmentation segments by its own cluster values.