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020 _a9783540451242
_9978-3-540-45124-2
024 7 _a10.1007/3-540-45124-2
_2doi
050 4 _aQA76.76.A65
072 7 _aUB
_2bicssc
072 7 _aCOM005000
_2bisacsh
072 7 _aUX
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082 0 4 _a005.3
_223
100 1 _aPauli, Josef.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aLearning-Based Robot Vision
_h[electronic resource] :
_bPrinciples and Applications /
_cby Josef Pauli.
250 _a1st ed. 2001.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg :
_bImprint: Springer,
_c2001.
300 _aIX, 292 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aLecture Notes in Computer Science,
_x1611-3349 ;
_v2048
505 0 _aCompatibilities for Object Boundary Detection -- Manifolds for Object and Situation Recognition -- Learning-Based Achievement of RV Competences -- Summary and Discussion.
520 _aIndustrial 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.
650 0 _aApplication software.
650 0 _aComputer vision.
650 0 _aControl engineering.
650 0 _aRobotics.
650 0 _aAutomation.
650 0 _aComputer graphics.
650 0 _aArtificial intelligence.
650 1 4 _aComputer and Information Systems Applications.
650 2 4 _aComputer Vision.
650 2 4 _aControl, Robotics, Automation.
650 2 4 _aComputer Graphics.
650 2 4 _aArtificial Intelligence.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783540421085
776 0 8 _iPrinted edition:
_z9783662211878
830 0 _aLecture Notes in Computer Science,
_x1611-3349 ;
_v2048
856 4 0 _uhttps://doi.org/10.1007/3-540-45124-2
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912 _aZDB-2-SXCS
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942 _cSPRINGER
999 _c189244
_d189244