05070nam 22008415 450 99646577730331620200704045537.03-540-45124-210.1007/3-540-45124-2(CKB)1000000000016854(SSID)ssj0000324352(PQKBManifestationID)11236841(PQKBTitleCode)TC0000324352(PQKBWorkID)10322745(PQKB)10458854(DE-He213)978-3-540-45124-2(MiAaPQ)EBC3072573(PPN)155204777(EXLCZ)99100000000001685420121227d2001 u| 0engurnn|008mamaatxtccrLearning-Based Robot Vision[electronic resource] Principles and Applications /by Josef Pauli1st ed. 2001.Berlin, Heidelberg :Springer Berlin Heidelberg :Imprint: Springer,2001.1 online resource (IX, 292 p.) Lecture Notes in Computer Science,0302-9743 ;2048Bibliographic Level Mode of Issuance: Monograph3-540-42108-4 Includes bibliographical references and index.Compatibilities for Object Boundary Detection -- Manifolds for Object and Situation Recognition -- Learning-Based Achievement of RV Competences -- Summary and Discussion.Industrial robots carry out simple tasks in customized environments for which it is typical that nearly all e?ector movements can be planned during an - line phase. A continual control based on sensory feedback is at most necessary at e?ector positions near target locations utilizing torque or haptic sensors. It is desirable to develop new-generation robots showing higher degrees of autonomy for solving high-level deliberate tasks in natural and dynamic en- ronments. Obviously, camera-equipped robot systems, which take and process images and make use of the visual data, can solve more sophisticated robotic tasks. The development of a (semi-) autonomous camera-equipped robot must be grounded on an infrastructure, based on which the system can acquire and/or adapt task-relevant competences autonomously. This infrastructure consists of technical equipment to support the presentation of real world training samples, various learning mechanisms for automatically acquiring function approximations, and testing methods for evaluating the quality of the learned functions. Accordingly, to develop autonomous camera-equipped robot systems one must ?rst demonstrate relevant objects, critical situations, and purposive situation-action pairs in an experimental phase prior to the application phase. Secondly, the learning mechanisms are responsible for - quiring image operators and mechanisms of visual feedback control based on supervised experiences in the task-relevant, real environment. This paradigm of learning-based development leads to the concepts of compatibilities and manifolds. Compatibilities are general constraints on the process of image formation which hold more or less under task-relevant or accidental variations of the imaging conditions.Lecture Notes in Computer Science,0302-9743 ;2048Application softwareOptical data processingRoboticsAutomationComputer graphicsArtificial intelligenceControl engineeringMechatronicsComputer Applicationshttps://scigraph.springernature.com/ontologies/product-market-codes/I23001Image Processing and Computer Visionhttps://scigraph.springernature.com/ontologies/product-market-codes/I22021Robotics and Automationhttps://scigraph.springernature.com/ontologies/product-market-codes/T19020Computer Graphicshttps://scigraph.springernature.com/ontologies/product-market-codes/I22013Artificial Intelligencehttps://scigraph.springernature.com/ontologies/product-market-codes/I21000Control, Robotics, Mechatronicshttps://scigraph.springernature.com/ontologies/product-market-codes/T19000Application software.Optical data processing.Robotics.Automation.Computer graphics.Artificial intelligence.Control engineering.Mechatronics.Computer Applications.Image Processing and Computer Vision.Robotics and Automation.Computer Graphics.Artificial Intelligence.Control, Robotics, Mechatronics.629.892637Pauli Josefauthttp://id.loc.gov/vocabulary/relators/aut545742MiAaPQMiAaPQMiAaPQBOOK996465777303316Learning-based robot vision887573UNISA