02619nam 2200481 450 991081390070332120230808204625.03-8325-8810-8(CKB)4100000010135439(MiAaPQ)EBC60328555e469732-0144-4b63-ad93-4e00b0dd2d03(EXLCZ)99410000001013543920200317d2016 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierContent-based microscopic image analysis /vorgelegt von M. Sc. Chen LiBerlin :Logos Verlag Berlin,[2016]©20161 online resource (xxiv, 171 pages) illustrationsStudien zur Mustererkennung ;Band 39PublicationDate: 201605153-8325-4253-1 Includes bibliographical references (pages 147-167).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.Studien zur Mustererkennung ;Band 39.Image processingImage analysisData processingMicroscopyData processingImage processing.Image analysisData processing.MicroscopyData processing.621.367Li Chen1985 April 22-1678540MiAaPQMiAaPQMiAaPQBOOK9910813900703321Content-based microscopic image analysis4046272UNINA