001471802 000__ 05176cam\\2200637\i\4500
001471802 001__ 1471802
001471802 003__ OCoLC
001471802 005__ 20230908003315.0
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001471802 008__ 230717s2023\\\\si\a\\\\o\\\\\000\0\eng\d
001471802 019__ $$a1390117203$$a1390560204
001471802 020__ $$a9789819937844$$q(electronic bk.)
001471802 020__ $$a9819937841$$q(electronic bk.)
001471802 020__ $$z9819937833
001471802 020__ $$z9789819937837
001471802 0247_ $$a10.1007/978-981-99-3784-4$$2doi
001471802 035__ $$aSP(OCoLC)1390614372
001471802 040__ $$aGW5XE$$beng$$erda$$epn$$cGW5XE$$dEBLCP$$dYDX$$dOCLCQ
001471802 049__ $$aISEA
001471802 050_4 $$aTA1637
001471802 08204 $$a621.36/7$$223/eng/20230717
001471802 24500 $$aDeep learning applications in image analysis /$$cSanjiban Sekhar Roy, Ching-Hsien Hsu, Venkateshwara Kagita, editors.
001471802 264_1 $$aSingapore :$$bSpringer,$$c[2023]
001471802 264_4 $$c©2023
001471802 300__ $$a1 online resource (xii, 210 pages) :$$billustrations (some color).
001471802 336__ $$atext$$btxt$$2rdacontent
001471802 337__ $$acomputer$$bc$$2rdamedia
001471802 338__ $$aonline resource$$bcr$$2rdacarrier
001471802 4901_ $$aStudies in big data ;$$vvolume 129
001471802 5050_ $$aClassification and segmentation of images using deep learning -- Image reconstruction, image super-resolution and image synthesis by deep learning techniques -- Deep learning for cancer images -- Deep Learning in Gastrointestinal Endoscopy -- Tumor detection using deep learning -- Deep learning for image analysis using multimodality fusion -- Image quality recognition methods inspired by deep learning -- Advanced Deep Learning methods in computer vision with 3D data -- Deep Learning models to solve the task of MOT(Multiple Object Tracking) -- Deep learning techniques for semantic segmentation of images -- Applications of deep learning for image forensics -- Human action recognition using deep learning -- Application of deep learning in satellite image classification and segmentation.
001471802 506__ $$aAccess limited to authorized users.
001471802 520__ $$aThis book provides state-of-the-art coverage of deep learning applications in image analysis. The book demonstrates various deep learning algorithms that can offer practical solutions for various image-related problems; also how these algorithms are used by scientists and scholars in industry and academia. This includes autoencoder and deep convolutional generative adversarial network in improving classification performance of Bangla handwritten characters, dealing with deep learning-based approaches using feature selection methods for automatic diagnosis of covid-19 disease from x-ray images, imbalance image data sets of classification, image captioning using deep transfer learning, developing a vehicle over speed detection system, creating an intelligent system for video-based proximity analysis, building a melanoma cancer detection system using deep learning, plant diseases classification using AlexNet, dealing with hyperspectral images using deep learning, chest x-ray image classification of pneumonia disease using efficient net and inceptionv3. The book also addresses the difficulty of implementing deep learning in terms of computation time and the complexity of reasoning and modelling different types of data where information is currently encoded. Each chapter has the application of various new or existing deep learning models such as Deep Neural Network (DNN) and Deep Convolutional Neural Networks (DCNN). The detailed utilization of deep learning packages that are available in MATLAB, Python and R programming environments have also been discussed, therefore, the readers will get to know about the practical implementation of deep learning as well. The content of this book is presented in a simple and lucid style for professionals, nonprofessionals, scientists, and students interested in the research area of deep learning applications in image analysis.
001471802 588__ $$aDescription based on print version record.
001471802 650_0 $$aImage analysis$$xData processing.
001471802 650_0 $$aDeep learning (Machine learning)
001471802 655_0 $$aElectronic books.
001471802 7001_ $$aRoy, Sanjiban Sekhar,$$eeditor.
001471802 7001_ $$aHsu, Ching-Hsien,$$eeditor.
001471802 7001_ $$aKagita, Venkateshwara,$$eeditor.
001471802 77608 $$iPrint version:$$tDEEP LEARNING APPLICATIONS IN IMAGE ANALYSIS.$$d[Place of publication not identified] : SPRINGER, 2023$$z9819937833$$w(OCoLC)1380385832
001471802 830_0 $$aStudies in big data ;$$vv. 129.
001471802 852__ $$bebk
001471802 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-99-3784-4$$zOnline Access$$91397441.1
001471802 909CO $$ooai:library.usi.edu:1471802$$pGLOBAL_SET
001471802 980__ $$aBIB
001471802 980__ $$aEBOOK
001471802 982__ $$aEbook
001471802 983__ $$aOnline
001471802 994__ $$a92$$bISE