LEADER 00703nam2 22002171i 450 001 990002361040403321 035 $a000236104 035 $aFED01000236104 035 $a(Aleph)000236104FED01 035 $a000236104 100 $a20030801d--------km-y0itay50------ba 200 1 $a<>historical perspective of natural products chemistry. Amsterdam, 1999, v. 2, p. xxi-xxxviii. 463 0$1001000222967 701 1$aNakanishi,$bKoji$f<1925- >$075886 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990002361040403321 959 $aFFABC 996 $aHistorical perspective of natural products chemistry. Amsterdam, 1999, v. 2, p. xxi-xxxviii$9441686 997 $aUNINA DB $aING01 LEADER 05355nam 22007455 450 001 9910523760803321 005 20230810165916.0 010 $a9783030343729$b(electronic bk.) 010 $z9783030343712 024 7 $a10.1007/978-3-030-34372-9 035 $a(MiAaPQ)EBC6839940 035 $a(Au-PeEL)EBL6839940 035 $a(CKB)20443804500041 035 $a(OCoLC)1291316737 035 $a(DE-He213)978-3-030-34372-9 035 $a(PPN)269153594 035 $a(EXLCZ)9920443804500041 100 $a20220103d2022 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aComputer Vision $eAlgorithms and Applications /$fby Richard Szeliski 205 $a2nd ed. 2022. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2022. 215 $a1 online resource (925 pages) 225 1 $aTexts in Computer Science,$x1868-095X 311 08$aPrint version: Szeliski, Richard Computer Vision Cham : Springer International Publishing AG,c2022 9783030343712 327 $a1 Introduction -- 2 Image Formation -- 3 Image Processing -- 4 Model Fitting and Optimization -- 5 Deep Learning -- 6 Recognition -- 7 Feature Detection and Matching -- 8 Image Alignment and Stitching -- 9 Motion Estimation -- 10 Computational Photography -- 11 Structure from Motion and SLAM -- 12 Depth Estimation -- 13 3D Reconstruction -- 14 Image-Based Rendering -- 15 Conclusion -- Appendix A: Linear Algebra and Numerical Techniques -- Appendix B: Bayesian Modeling and Inference -- Appendix C: Supplementary Material. 330 $aComputer Vision: Algorithms and Applications explores the variety of techniques used to analyze and interpret images. It also describes challenging real-world applications where vision is being successfully used, both in specialized applications such as image search and autonomous navigation, as well as for fun, consumer-level tasks that students can apply to their own personal photos and videos. More than just a source of ?recipes,? this exceptionally authoritative and comprehensive textbook/reference takes a scientific approach to the formulation of computer vision problems. These problems are then analyzed using the latest classical and deep learning models and solved using rigorous engineering principles. Topics and features: Structured to support active curricula and project-oriented courses, with tips in the Introduction for using the book in a variety of customized courses Incorporates totally new material on deep learning and applications such as mobile computational photography, autonomous navigation, and augmented reality Presents exercises at the end of each chapter with a heavy emphasis on testing algorithms and containing numerous suggestions for small mid-term projects Includes 1,500 new citations and 200 new figures that cover the tremendous developments from the last decade Provides additional material and more detailed mathematical topics in the Appendices, which cover linear algebra, numerical techniques, estimation theory, datasets, and software Suitable for an upper-level undergraduate or graduate-level course in computer science or engineering, this textbook focuses on basic techniques that work under real-world conditions and encourages students to push their creative boundaries. Its design and exposition also make it eminently suitable as a unique reference to the fundamental techniques and current research literature in computer vision. About the Author Dr. Richard Szeliski has more than 40 years? experience in computer vision research, most recently at Facebook and Microsoft Research, where he led the Computational Photography and Interactive Visual Media groups. He is currently an Affiliate Professor at the University of Washington where he co-developed (with Steve Seitz) the widely adopted computer vision curriculum on which this book is based. 410 0$aTexts in Computer Science,$x1868-095X 606 $aComputer vision 606 $aImage processing$xDigital techniques 606 $aMachine learning 606 $aSignal processing 606 $aMaterials$xAnalysis 606 $aImaging systems 606 $aComputer Vision 606 $aComputer Imaging, Vision, Pattern Recognition and Graphics 606 $aMachine Learning 606 $aSignal, Speech and Image Processing 606 $aImaging Techniques 615 0$aComputer vision. 615 0$aImage processing$xDigital techniques. 615 0$aMachine learning. 615 0$aSignal processing. 615 0$aMaterials$xAnalysis. 615 0$aImaging systems. 615 14$aComputer Vision. 615 24$aComputer Imaging, Vision, Pattern Recognition and Graphics. 615 24$aMachine Learning. 615 24$aSignal, Speech and Image Processing . 615 24$aImaging Techniques. 676 $a006.37 676 $a006.37 700 $aSzeliski$b Richard$f1958-$066335 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 856 40$ahttps://www.hit.ac.il/Library/booksGateway.php?url=https%3A%2F%2Febookcentral.proquest.com%2Flib%2Fhitil%2Fdetail.action%3FdocID%3D6839940 912 $a9910523760803321 996 $aComputer vision$91536161 997 $aUNINA 999 $aEBOOK