04118nam 22007455 450 991029985440332120200701075936.03-319-11325-910.1007/978-3-319-11325-8(CKB)3710000000281281(EBL)1967673(OCoLC)895661066(SSID)ssj0001386259(PQKBManifestationID)11817441(PQKBTitleCode)TC0001386259(PQKBWorkID)11350211(PQKB)11642349(DE-He213)978-3-319-11325-8(MiAaPQ)EBC1967673(PPN)183093771(EXLCZ)99371000000028128120141113d2015 u| 0engur|n|---|||||txtccrOn Hierarchical Models for Visual Recognition and Learning of Objects, Scenes, and Activities[electronic resource] /by Jens Spehr1st ed. 2015.Cham :Springer International Publishing :Imprint: Springer,2015.1 online resource (210 p.)Studies in Systems, Decision and Control,2198-4182 ;11Description based upon print version of record.3-319-11324-0 Includes bibliographical references.Introduction -- Probabilistic Graphical Models -- Hierarchical Graphical Models -- Learning of Hierarchical Models.-Object Recognition -- Human Pose Estimation -- Scene Understanding for Intelligent Vehicles -- Conclusion.In many computer vision applications, objects have to be learned and recognized in images or image sequences. This book presents new probabilistic hierarchical models that allow an efficient representation of multiple objects of different categories, scales, rotations, and views. The idea is to exploit similarities between objects and object parts in order to share calculations and avoid redundant information. Furthermore inference approaches for fast and robust detection are presented. These new approaches combine the idea of compositional and similarity hierarchies and overcome limitations of previous methods. Besides classical object recognition the book shows the use for detection of human poses in a project for gait analysis. The use of activity detection is presented for the design of environments for ageing, to identify activities and behavior patterns in smart homes. In a presented project for parking spot detection using an intelligent vehicle, the proposed approaches are used to hierarchically model the environment of the vehicle for an efficient and robust interpretation of the scene in real-time.Studies in Systems, Decision and Control,2198-4182 ;11RoboticsAutomationComputational intelligenceOptical data processingPattern recognitionRobotics and Automationhttps://scigraph.springernature.com/ontologies/product-market-codes/T19020Computational Intelligencehttps://scigraph.springernature.com/ontologies/product-market-codes/T11014Image Processing and Computer Visionhttps://scigraph.springernature.com/ontologies/product-market-codes/I22021Pattern Recognitionhttps://scigraph.springernature.com/ontologies/product-market-codes/I2203XRobotics.Automation.Computational intelligence.Optical data processing.Pattern recognition.Robotics and Automation.Computational Intelligence.Image Processing and Computer Vision.Pattern Recognition.006.3006.37006.4006.6Spehr Jensauthttp://id.loc.gov/vocabulary/relators/aut720650BOOK9910299854403321On Hierarchical Models for Visual Recognition and Learning of Objects, Scenes, and Activities1412416UNINA