LEADER 02619nam 2200481 450 001 9910813900703321 005 20230808204625.0 010 $a3-8325-8810-8 035 $a(CKB)4100000010135439 035 $a(MiAaPQ)EBC6032855 035 $a5e469732-0144-4b63-ad93-4e00b0dd2d03 035 $a(EXLCZ)994100000010135439 100 $a20200317d2016 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aContent-based microscopic image analysis /$fvorgelegt von M. Sc. Chen Li 210 1$aBerlin :$cLogos Verlag Berlin,$d[2016] 210 4$dİ2016 215 $a1 online resource (xxiv, 171 pages) $cillustrations 225 1 $aStudien zur Mustererkennung ;$vBand 39 300 $aPublicationDate: 20160515 311 $a3-8325-4253-1 320 $aIncludes bibliographical references (pages 147-167). 330 $aLong description: In this dissertation, novel Content-based Microscopic Image Analysis (CBMIA) methods, including Weakly Supervised Learning (WSL), are proposed to aid biological studies. In a CBMIA task, noisy image, image rotation, and object recognition problems need to be addressed. To this end, the first approach is a general supervised learning method, which consists of image segmentation, shape feature extraction, classification, and feature fusion, leading to a semi-automatic approach. In contrast, the second approach is a WSL method, which contains Sparse Coding (SC) feature extraction, classification, and feature fusion, leading to a full-automatic approach. In this WSL approach, the problems of noisy image and object recognition are jointly resolved by a region-based classifier, and the image rotation problem is figured out through SC features. To demonstrate the usefulness and potential of the proposed methods, experiments are implemented on different practical biological tasks, including environmental microorganism classification, stem cell analysis, and insect tracking. 410 0$aStudien zur Mustererkennung ;$vBand 39. 606 $aImage processing 606 $aImage analysis$xData processing 606 $aMicroscopy$xData processing 615 0$aImage processing. 615 0$aImage analysis$xData processing. 615 0$aMicroscopy$xData processing. 676 $a621.367 700 $aLi$b Chen$f1985 April 22-$01678540 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910813900703321 996 $aContent-based microscopic image analysis$94046272 997 $aUNINA