LEADER 01788oam 2200433Ka 450 001 9910696157903321 005 20070920112931.0 035 $a(CKB)5470000002375231 035 $a(OCoLC)143659534 035 $a(EXLCZ)995470000002375231 100 $a20070612d2006 ua 0 101 0 $aeng 135 $aurmn||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aRegional field verification--operational results from four small wind turbines in the Pacific Northwest$b[electronic resource] $epreprint / K. Sinclair, National Renewable Energy Laboratory [and] J. Raker, Northwest Sustainable Energy for Economic Development 210 1$aGolden, CO :$cNational Renewable Energy Laboratory,$d[2006] 215 $a13 pages $cdigital, PDF file 225 1 $aNREL/CP ;$v500-38110 300 $aTitle from title screen (viewed Sept. 20, 2007). 300 $a"Presented at the American Wind Energy Association WindPower 2006, Pittsburgh, Pennsylvania, June 4-7, 2006." 300 $a"August 2006." 517 $aRegional field verification--operational results from four small wind turbines in the Pacific Northwest 606 $aWind power$xResearch$zNorthwest, Pacific 606 $aWind turbines$zNorthwest, Pacific$xTesting 615 0$aWind power$xResearch 615 0$aWind turbines$xTesting. 700 $aSinclair$b Karin$01382832 701 $aRaker$b Jessica$01401567 712 02$aNational Renewable Energy Laboratory (U.S.) 801 0$bSOE 801 1$bSOE 801 2$bSOE 801 2$bGPO 906 $aBOOK 912 $a9910696157903321 996 $aRegional field verification--operational results from four small wind turbines in the Pacific Northwest$93470376 997 $aUNINA LEADER 04293nam 22006495 450 001 9910298980003321 005 20251111100505.0 010 $a1-4471-6320-6 024 7 $a10.1007/978-1-4471-6320-6 035 $a(CKB)3710000000085735 035 $a(EBL)1636415 035 $a(SSID)ssj0001186522 035 $a(PQKBManifestationID)11701772 035 $a(PQKBTitleCode)TC0001186522 035 $a(PQKBWorkID)11219192 035 $a(PQKB)11609760 035 $a(DE-He213)978-1-4471-6320-6 035 $a(MiAaPQ)EBC6311782 035 $a(MiAaPQ)EBC1636415 035 $a(Au-PeEL)EBL1636415 035 $a(CaPaEBR)ebr10962451 035 $a(OCoLC)869222487 035 $a(PPN)176097503 035 $a(EXLCZ)993710000000085735 100 $a20140104d2014 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aConcise Computer Vision $eAn Introduction into Theory and Algorithms /$fby Reinhard Klette 205 $a1st ed. 2014. 210 1$aLondon :$cSpringer London :$cImprint: Springer,$d2014. 215 $a1 online resource (441 p.) 225 1 $aUndergraduate Topics in Computer Science,$x1863-7310 300 $aDescription based upon print version of record. 311 08$a1-4471-6319-2 327 $a1: Image Data -- 2: Image Processing -- 3: Image Analysis -- 4: Dense Motion Analysis -- 5: Image Segmentation -- 6: Cameras, Coordinates and Calibration -- 7: 3D Shape Reconstruction -- 8: Stereo Matching -- 9: Feature Detection and Tracking -- 10: Object Detection. 330 $aMany textbooks on computer vision can be unwieldy and intimidating in their coverage of this extensive discipline. This textbook addresses the need for a concise overview of the fundamentals of this field. Concise Computer Vision provides an accessible general introduction to the essential topics in computer vision, highlighting the role of important algorithms and mathematical concepts. Classroom-tested programming exercises and review questions are also supplied at the end of each chapter. Topics and features: Provides an introduction to the basic notation and mathematical concepts for describing an image, and the key concepts for mapping an image into an image Explains the topologic and geometric basics for analysing image regions and distributions of image values, and discusses identifying patterns in an image Introduces optic flow for representing dense motion, and such topics in sparse motion analysis as keypoint detection and descriptor definition, and feature tracking using the Kalman filter Describes special approaches for image binarization and segmentation of still images or video frames Examines the three basic components of a computer vision system, namely camera geometry and photometry, coordinate systems, and camera calibration Reviews different techniques for vision-based 3D shape reconstruction, including the use of structured lighting, stereo vision, and shading-based shape understanding Includes a discussion of stereo matchers, and the phase-congruency model for image features Presents an introduction into classification and learning, with a detailed description of basic AdaBoost and the use of random forests This concise and easy to read textbook/reference is ideal for an introductory course at third- or fourth-year level in an undergraduate computer science or engineering programme. 410 0$aUndergraduate Topics in Computer Science,$x1863-7310 606 $aOptical data processing 606 $aArtificial intelligence 606 $aImage Processing and Computer Vision$3https://scigraph.springernature.com/ontologies/product-market-codes/I22021 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 615 0$aOptical data processing. 615 0$aArtificial intelligence. 615 14$aImage Processing and Computer Vision. 615 24$aArtificial Intelligence. 676 $a006.37 700 $aKlette$b Reinhard$4aut$4http://id.loc.gov/vocabulary/relators/aut$0725642 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910298980003321 996 $aConcise Computer Vision$92174066 997 $aUNINA