Content-based microscopic image analysis / / vorgelegt von M. Sc. Chen Li

• Autore: Li Chen <1985 April 22->
• Pubblicazione: Berlin : , : Logos Verlag Berlin, , [2016]; ©2016
• Descrizione fisica: 1 online resource (xxiv, 171 pages) : illustrations
• Disciplina: 621.367
• Soggetto topico: Image processing; Image analysis - Data processing; Microscopy - Data processing
• Note generali: PublicationDate: 20160515
• Nota di bibliografia: Includes bibliographical references (pages 147-167).
• Sommario/riassunto: Long description: In this dissertation, novel Content-based Microscopic Image Analysis (CBMIA) methods, including Weakly Supervised Learning (WSL), are proposed to aid biological studies. In a CBMIA task, noisy image, image rotation, and object recognition problems need to be addressed. To this end, the first approach is a general supervised learning method, which consists of image segmentation, shape feature extraction, classification, and feature fusion, leading to a semi-automatic approach. In contrast, the second approach is a WSL method, which contains Sparse Coding (SC) feature extraction, classification, and feature fusion, leading to a full-automatic approach. In this WSL approach, the problems of noisy image and object recognition are jointly resolved by a region-based classifier, and the image rotation problem is figured out through SC features. To demonstrate the usefulness and potential of the proposed methods, experiments are implemented on different practical biological tasks, including environmental microorganism classification, stem cell analysis, and insect tracking.
• Titolo autorizzato: Content-based microscopic image analysis
• ISBN: 3-8325-8810-8
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910813900703321
• Lo trovi qui: Univ. Federico II
• Serie: Studien zur Mustererkennung ; ; Band 39.