LEADER 04107oam 2200925 450 001 9910140504503321 005 20230125193932.0 010 $a1-927356-82-2 010 $a1-927356-80-6 010 $a1-927356-81-4 035 $a(CKB)2670000000570226 035 $a(EBL)1810525 035 $a(SSID)ssj0001400882 035 $a(PQKBManifestationID)12510305 035 $a(PQKBTitleCode)TC0001400882 035 $a(PQKBWorkID)11344793 035 $a(PQKB)10701067 035 $a(MiAaPQ)EBC4839948 035 $a(CEL)448837 035 $a(OCoLC)895193178 035 $a(CaBNVSL)slc00235271 035 $a(MiAaPQ)EBC3295335 035 $a(MiAaPQ)EBC1810525 035 $a(Au-PeEL)EBL1810525 035 $a(OCoLC)871227973 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/60514 035 $a(VaAlCD)20.500.12592/9wt8nz 035 $a(MnU)OTLid0000355 035 $a(EXLCZ)992670000000570226 100 $a20140225d2014 uy 0 101 0 $aeng 135 $aurbn#---|u||u 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aTeaching crowds $elearning and social media /$fJon Dron, Terry Anderson 210 $cAthabasca University Press$d2014 210 1$aEdmonton, Alberta]:$cAU Press,$d[2014]. 210 4$dŠ2014 215 $a1 online resource (353 pages) $cillustrations 225 1 $aIssues in distance education series 311 $a1-322-18023-7 311 $a1-77199-000-7 320 $aIncludes bibliographical references and index. 327 $aCover Page -- Title Page -- Copyright Page -- Dedication -- Contents -- List of Figures and Tables -- Preface -- Chapter 1 On the Nature and Value of Social Software for Learning -- Chapter 2 Social Learning Theories -- Chapter 3 A Typology of Social Forms for Learning -- Chapter 4 Learning in Groups -- Chapter 5 Learning in Networks -- Chapter 6 Learning in Sets -- Chapter 7 Learning with Collectives -- Chapter 8 Stories From the Field -- Chapter 9 Issues and Challenges in Educational Uses of Social Software -- Chapter 10 The Shape of Things and of Things to Come -- References -- Index -- Footnote -- Chapter 8. 330 $a"[Authors] introduce a new model for understanding and exploiting the pedagogical potential of Web-based technologies, one that rests on connections - on networks and collectives - rather than on separations. Recognizing that online learning both demands and affords new models of teaching and learning, the authors show how learners can engage with social media platforms to create an unbounded field of emergent connections. These connections empower learners, allowing them to draw from one another's expertise to formulate and fulfill their own educational goals. In an increasingly networked world, developing such skills will, they argue, better prepare students to become self-directed, lifelong learners"--Page [4] of cover. 410 0$aIssues in distance education series. 606 $aEducational technology 606 $aEducation$xSocial aspects 606 $aSocial learning 606 $aSocial media 606 $aGroup work in education 606 $aDistance education 606 $aCritical pedagogy 610 $aself-directed learning 610 $alifelong learners 610 $alearning management systems 610 $ablended learning 610 $anetworked learning 610 $aeducational technology 610 $asocial media 610 $alearning communities 615 0$aEducational technology. 615 0$aEducation$xSocial aspects. 615 0$aSocial learning. 615 0$aSocial media. 615 0$aGroup work in education. 615 0$aDistance education. 615 0$aCritical pedagogy. 676 $a371.33 700 $aDron$b Jon$f1961-$0906873 702 $aAnderson$b Terry$f1950- 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 801 2$bUkMaJRU 906 $aBOOK 912 $a9910140504503321 996 $aTeaching crowds$92028547 997 $aUNINA LEADER 05025nam 22008415 450 001 9910878978103321 005 20251103093322.0 010 $a9789819730230 024 7 $a10.1007/978-981-97-3023-0 035 $a(CKB)33734460900041 035 $a(MiAaPQ)EBC31588400 035 $a(Au-PeEL)EBL31588400 035 $a(DE-He213)978-981-97-3023-0 035 $a(EXLCZ)9933734460900041 100 $a20240806d2024 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAnomaly Detection in Video Surveillance /$fby Xiaochun Wang 205 $a1st ed. 2024. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2024. 215 $a1 online resource (396 pages) 225 1 $aCognitive Intelligence and Robotics,$x2520-1964 311 08$a9789819730223 320 $aIncludes bibliographical references. 327 $aChapter 1 Introduction -- Chapter 2 Mathematical Preliminaries for Video Anomaly Detection Techniques -- Chapter 3 Probability Based Video Anomaly Detection Approaches -- Chapter 4 k-Nearest Neighbor Based Video Anomaly Detection Approaches -- Chapter 5 Gaussian Mixture Model Based Video Anomaly Detection. 330 $aAnomaly detection in video surveillance stands at the core of numerous real-world applications that have broad impact and generate significant academic and industrial value. The key advantage of writing the book at this point in time is that the vast amount of work done by computer scientists over the last few decades has remained largely untouched by a formal book on the subject, although these techniques significantly advance existing methods of image and video analysis and understanding by taking advantage of anomaly detection in the data mining community and visual analysis in the computer vision community. The proposed book provides a comprehensive coverage of the advances in video based anomaly detection, including topics such as the theories of anomaly detection and machine perception for the functional analysis of abnormal events in general, the identification of abnormal behaviour and crowd abnormal behaviour in particular, the current understanding of computer vision development, and the application of this present understanding towards improving video-based anomaly detection in theory and coding with OpenCV. The book also provides a perspective on deep learning on human action recognition and behaviour analysis, laying the groundwork for future advances in these areas. Overall, the chapters of this book have been carefully organized with extensive bibliographic notes attached to each chapter. One of the goals is to provide the first systematic and comprehensive description of the range of data-driven solutions currently being developed up to date for such purposes. Another is to serve a dual purpose so that students and practitioners can use it as a textbook while researchers can use it as a reference book. A final goal is to provide a comprehensive exposition of the topic of anomaly detection in video media from multiple points of view. 410 0$aCognitive Intelligence and Robotics,$x2520-1964 606 $aComputer vision 606 $aData mining 606 $aImage processing$xDigital techniques 606 $aMachine learning 606 $aPattern recognition systems 606 $aComputer science 606 $aComputer Vision 606 $aData Mining and Knowledge Discovery 606 $aComputer Imaging, Vision, Pattern Recognition and Graphics 606 $aMachine Learning 606 $aAutomated Pattern Recognition 606 $aTheory and Algorithms for Application Domains 606 $aVisiķ per ordinador$2thub 606 $aMineria de dades$2thub 606 $aAprenentatge automātic$2thub 606 $aProcessament digital d'imatges$2thub 606 $aReconeixement de formes (Informātica)$2thub 608 $aLlibres electrōnics$2thub 615 0$aComputer vision. 615 0$aData mining. 615 0$aImage processing$xDigital techniques. 615 0$aMachine learning. 615 0$aPattern recognition systems. 615 0$aComputer science. 615 14$aComputer Vision. 615 24$aData Mining and Knowledge Discovery. 615 24$aComputer Imaging, Vision, Pattern Recognition and Graphics. 615 24$aMachine Learning. 615 24$aAutomated Pattern Recognition. 615 24$aTheory and Algorithms for Application Domains. 615 7$aVisiķ per ordinador 615 7$aMineria de dades 615 7$aAprenentatge automātic 615 7$aProcessament digital d'imatges 615 7$aReconeixement de formes (Informātica) 676 $a621.38928 700 $aWang$b Xiaochun$f1954-$01772060 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 912 $a9910878978103321 996 $aAnomaly Detection in Video Surveillance$94271047 997 $aUNINA