LEADER 00777cam0-2200301---450- 001 990005870730403321 005 20120508155323.0 035 $a000587073 035 $aFED01000587073 035 $a(Aleph)000587073FED01 035 $a000587073 100 $a20000421d1988----km-y0itay50------ba 101 1 $aita$arus 102 $aIT 105 $a--------001cy 200 1 $aDall'esilio$fIosif Brodskij 210 $aMilano$d1988$cAdelphi 215 $a68 p.$d18 cm 225 1 $aPiccola biblioteca$v212 676 $a891.744 700 1$aBrodskij,$bIosif$0223344 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990005870730403321 952 $a891.744 BRO 1$bDip.f.m.10189$fFLFBC 959 $aFLFBC 996 $aDall'esilio$9564691 997 $aUNINA LEADER 00963nam0-22003491i-450- 001 990007068350403321 005 20060130105557.0 035 $a000706835 035 $aFED01000706835 035 $a(Aleph)000706835FED01 035 $a000706835 100 $a20020403d1930----km-y0itay50------ba 101 0 $ager 102 $aDE 105 $ay-------001yy 200 1 $aVolkswirtschaftliche Theorie des Bankkredits$fvon L. Albert Hahn 205 $a3. Aufl. 210 $aTübingen$cJ.C.B. Mohr$d1930 215 $aXIX, 156 p.$d24 cm 610 0 $aCredito$aTeoria 676 $a330$v20$zita 700 1$aHahn,$bL. Albert$0116003 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990007068350403321 952 $aXV M 534$b55002$fFGBC 952 $aJ/2.10 HAH$b12252/I$fSES 952 $aJ/2.10 HAH/1$b01211$fSES 959 $aFGBC 959 $aSES 996 $aVolkswirtschaftliche Theorie des Bankkredits$913328 997 $aUNINA LEADER 06283nam 22005895 450 001 9910151857103321 005 20251116173347.0 010 $a3-319-47629-7 024 7 $a10.1007/978-3-319-47629-2 035 $a(CKB)3710000000952907 035 $a(DE-He213)978-3-319-47629-2 035 $a(MiAaPQ)EBC4745996 035 $a(PPN)197141323 035 $a(EXLCZ)993710000000952907 100 $a20161118d2017 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aCellular Image Classification /$fby Xiang Xu, Xingkun Wu, Feng Lin 205 $a1st ed. 2017. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2017. 215 $a1 online resource (IX, 137 p. 60 illus.) 311 08$a3-319-47628-9 320 $aIncludes bibliographical references. 327 $aIntroduction -- Fundamentals -- Optical Systems for Cellular Imaging -- Image Representation with Bag-of-Words -- Image Coding -- Encoding Image Features -- Defining Feature Space for Image Classification -- Conclusions and Perspectives. 330 $aThis book introduces new techniques for cellular image feature extraction, pattern recognition and classification. The authors use the antinuclear antibodies (ANAs) in patient serum as the subjects and the Indirect Immunofluorescence (IIF) technique as the imaging protocol to illustrate the applications of the described methods. Throughout the book, the authors provide evaluations for the proposed methods on two publicly available human epithelial (HEp-2) cell datasets: ICPR2012 dataset from the ICPR'12 HEp-2 cell classification contest and ICIP2013 training dataset from the ICIP'13 Competition on cells classification by fluorescent image analysis. First, the reading of imaging results is significantly influenced by one?s qualification and reading systems, causing high intra- and inter-laboratory variance. The authors present a low-order LP21 fiber mode for optical single cell manipulation and imaging staining patterns of HEp-2 cells. A focused four-lobed mode distribution is stable and effective in optical tweezer applications, including selective cell pick-up, pairing, grouping or separation, as well as rotation of cell dimers and clusters. Both translational dragging force and rotational torque in the experiments are in good accordance with the theoretical model. With a simple all-fiber configuration, and low peak irradiation to targeted cells, instrumentation of this optical chuck technology will provide a powerful tool in the ANA-IIF laboratories. Chapters focus on the optical, mechanical and computing systems for the clinical trials. Computer programs for GUI and control of the optical tweezers are also discussed. to more discriminative local distance vector by searching for local neighbors of the local feature in the class-specific manifolds. Encoding and pooling the local distance vectors leads to salient image representation. Combined with the traditional coding methods, this method achieves higher classification accuracy. Then, a rotation invariant textural feature of Pairwise Local Ternary Patterns with Spatial Rotation Invariant (PLTP-SRI) is examined. It is invariant to image rotations, meanwhile it is robust to noise and weak illumination. By adding spatial pyramid structure, this method captures spatial layout information. While the proposed PLTP-SRI feature extracts local feature, the BoW framework builds a global image representation. It is reasonable to combine them together to achieve impressive classification performance, as the combined feature takes the advantages of the two kinds of features in different aspects. Finally, the authors design a Co-occurrence Differential Texton (CoDT) feature to represent the local image patches of HEp-2 cells. The CoDT feature reduces the information loss by ignoring the quantization while it utilizes the spatial relations among the differential micro-texton feature. Thus it can increase the discriminative power. A generative model adaptively characterizes the CoDT feature space of the training data. Furthermore, exploiting a discriminant representation allows for HEp-2 cell images based on the adaptive partitioned feature space. Therefore, the resulting representation is adapted to the classification task. By cooperating with linear Support Vector Machine (SVM) classifier, this framework can exploit the advantages of both generative and discriminative approaches for cellular image classification. The book is written for those researchers who would like to develop their own programs, and the working MatLab codes are included for all the important algorithms presented. It can also be used as a reference book for graduate students and senior undergraduates in the area of biomedical imaging, image feature extraction, pattern recognition and classification. Academics, researchers, and professional will find this to be an exceptional resource. 606 $aSignal processing 606 $aImage processing 606 $aSpeech processing systems 606 $aPattern perception 606 $aBiomathematics 606 $aSignal, Image and Speech Processing$3https://scigraph.springernature.com/ontologies/product-market-codes/T24051 606 $aPattern Recognition$3https://scigraph.springernature.com/ontologies/product-market-codes/I2203X 606 $aPhysiological, Cellular and Medical Topics$3https://scigraph.springernature.com/ontologies/product-market-codes/M31020 615 0$aSignal processing. 615 0$aImage processing. 615 0$aSpeech processing systems. 615 0$aPattern perception. 615 0$aBiomathematics. 615 14$aSignal, Image and Speech Processing. 615 24$aPattern Recognition. 615 24$aPhysiological, Cellular and Medical Topics. 676 $a621.382 700 $aXu$b Xiang$4aut$4http://id.loc.gov/vocabulary/relators/aut$0875331 702 $aWu$b Xingkun$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aLin$b Feng$4aut$4http://id.loc.gov/vocabulary/relators/aut 906 $aBOOK 912 $a9910151857103321 996 $aCellular Image Classification$91954293 997 $aUNINA