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| 008 | 190919s2011 enka b 001 0 eng d | |
| 010 | ▼a 2010936817 | |
| 015 | ▼a GBB056381 ▼2 bnb | |
| 020 | ▼a 9781848829343 (hbk.) | |
| 020 | ▼a 1848829345 (hbk.) | |
| 035 | ▼a (KERIS)REF000016814376 | |
| 040 | ▼a UKM ▼c UKM ▼d BTCTA ▼d YDXCP ▼d HEBIS ▼d CDX ▼d OIP ▼d MUU ▼d WRM ▼d WEA ▼d WSL ▼d DLC ▼d 211009 | |
| 050 | 0 0 | ▼a TA1634 ▼b .S97 2011 |
| 082 | 0 4 | ▼a 006.37 ▼2 23 |
| 084 | ▼a 006.37 ▼2 DDCK | |
| 090 | ▼a 006.37 ▼b S997c | |
| 100 | 1 | ▼a Szeliski, Richard, ▼d 1958-. |
| 245 | 1 0 | ▼a Computer vision : ▼b algorithms and applications / ▼c Richard Szeliski. |
| 260 | ▼a London ; ▼a New York : ▼b Springer, ▼c c2011. | |
| 300 | ▼a xx, 812 p. : ▼b ill. (some col.) ; ▼c 29 cm. | |
| 490 | 1 | ▼a Texts in computer science |
| 504 | ▼a Includes bibliographical references (p. [691]-792) and index. | |
| 505 | 0 0 | ▼t Introduction. ▼t What is computer vision? ; ▼t A brief history ; ▼t Book overview ; ▼t Sample syllabus ; ▼t Notation -- ▼t Image formation. ▼t Geometric primitives and transformations ; ▼t Photometric image formation ; ▼t The digital camera -- ▼t Image processing. ▼t Point operators ; ▼t Linear filtering ; ▼t More neighborhood operators ; ▼t Fourier transforms ; ▼t Pyramids and wavelets ; ▼t Geometric transformations ; ▼t Global optimization -- ▼t Feature detection and matching. ▼t Points and patches ; ▼t Edges ; ▼t Lines -- ▼t Segmentation. ▼t Active contours ; ▼t Split and merge ; ▼t Mean shift and mode finding ; ▼t Normalized cuts ; ▼t Graph cuts and energy-based methods -- ▼t Feature-based alignment. ▼t 2D and 3D feature-based alignment ; ▼t Pose estimation ; ▼t Geometric intrinsic calibration -- ▼t Structure from motion. ▼t Triangulation ; ▼t Two-frame structure from motion ; ▼t Factorization ; ▼t Bundle adjustment ; ▼t Constrained structure and motion -- ▼t Dense motion estimation. ▼t Translational alignment ; ▼t Parametric motion ; ▼t Spline-based motion ; ▼t Optical flow ; ▼t Layered motion -- ▼t Image stitching. ▼t Motion models ; ▼t Global alignment ; ▼t Compositing -- ▼t Computational photography. ▼t Photometric calibration ; ▼t High dynamic range imaging ; ▼t Super-resolution and blur removal ; ▼t Image matting and compositing ; ▼t Texture analysis and synthesis -- ▼t Stereo correspondence. ▼t Epipolar geometry ; ▼t Sparse correspondence ; ▼t Dense correspondence ; ▼t Local methods ; ▼t Global optimization ; ▼t Multi-view stereo -- ▼t 3D reconstruction. ▼t Shape from X ; ▼t Active rangefinding ; ▼t Surface representations ; ▼t Point-base representations ; ▼t Volumetric representations ; ▼t Model-based reconstruction ; ▼t Recovering texture maps and albedos -- ▼t Image-based rendering. ▼t View interpolation ; ▼t Layered depth images ; ▼t Light fields and Lumigraphs ; ▼t Environment mattes ; ▼t Video-base rendering -- ▼t Recognition. ▼t Object detection ; ▼t Face recognition ; ▼t Instance recognition ; ▼t Category recognition ; ▼t Context and scene understanding ; ▼t Recognition databases and test sets. |
| 650 | 0 | ▼a Computer vision. |
| 650 | 0 | ▼a Image processing. |
| 650 | 0 | ▼a Computer algorithms. |
| 830 | 0 | ▼a Texts in computer science. |
| 945 | ▼a KLPA |
소장정보
| No. | 소장처 | 청구기호 | 등록번호 | 도서상태 | 반납예정일 | 예약 | 서비스 |
|---|---|---|---|---|---|---|---|
| No. 1 | 소장처 과학도서관/Sci-Info/지정도서 | 청구기호 006.37 S997c | 등록번호 121250337 (10회 대출) | 도서상태 지정도서 | 반납예정일 | 예약 | 서비스 |
컨텐츠정보
책소개
Computer Vision: Algorithms and Applications explores the variety of techniques commonly used to analyze and interpret images. It also describes challenging real-world applications where vision is being successfully used, both for specialized applications such as medical imaging, and for fun, consumer-level tasks such as image editing and stitching, which students can apply to their own personal photos and videos.
More than just a source of “recipes,” this exceptionally authoritative and comprehensive textbook/reference also takes a scientific approach to basic vision problems, formulating physical models of the imaging process before inverting them to produce descriptions of a scene. These problems are also analyzed using statistical models and solved using rigorous engineering techniques.
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; presents exercises at the end of each chapter with a heavy emphasis on testing algorithms and containing numerous suggestions for small mid-term projects; provides additional material and more detailed mathematical topics in the Appendices, which cover linear algebra, numerical techniques, and Bayesian estimation theory; suggests additional reading at the end of each chapter, including the latest research in each sub-field, in addition to a full Bibliography at the end of the book; supplies supplementary course material for students at the associated website, http://szeliski.org/Book/.
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.
Humans perceive the three-dimensional structure of the world with apparent ease. However, despite all of the recent advances in computer vision research, the dream of having a computer interpret an image at the same level as a two-year old remains elusive. Why is computer vision such a challenging problem and what is the current state of the art?
Computer Vision: Algorithms and Applications explores the variety of techniques commonly used to analyze and interpret images. It also describes challenging real-world applications where vision is being successfully used, both for specialized applications such as medical imaging, and for fun, consumer-level tasks such as image editing and stitching, which students can apply to their own personal photos and videos.
More than just a source of “recipes,” this exceptionally authoritative and comprehensive textbook/reference also takes a scientific approach to basic vision problems, formulating physical models of the imaging process before inverting them to produce descriptions of a scene. These problems are also analyzed using statistical models and solved using rigorous engineering techniques
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
- Presents exercises at the end of each chapter with a heavy emphasis on testing algorithms and containing numerous suggestions for small mid-term projects
- Provides additional material and more detailed mathematical topics in the Appendices, which cover linear algebra, numerical techniques, and Bayesian estimation theory
- Suggests additional reading at the end of each chapter, including the latest research in each sub-field, in addition to a full Bibliography at the end of the book
- Supplies supplementary course material for students at the associated website, http://szeliski.org/Book/
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.
New feature
Humans perceive the three-dimensional structure of the world with apparent ease. However, despite all of the recent advances in computer vision research, the dream of having a computer interpret an image at the same level as a two-year old remains elusive. Why is computer vision such a challenging problem and what is the current state of the art?
Computer Vision: Algorithms and Applications explores the variety of techniques commonly used to analyze and interpret images. It also describes challenging real-world applications where vision is being successfully used, both for specialized applications such as medical imaging, and for fun, consumer-level tasks such as image editing and stitching, which students can apply to their own personal photos and videos.
More than just a source of “recipes,” this exceptionally authoritative and comprehensive textbook/reference also takes a scientific approach to basic vision problems, formulating physical models of the imaging process before inverting them to produce descriptions of a scene. These problems are also analyzed using statistical models and solved using rigorous engineering techniques
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
- Presents exercises at the end of each chapter with a heavy emphasis on testing algorithms and containing numerous suggestions for small mid-term projects
- Provides additional material and more detailed mathematical topics in the Appendices, which cover linear algebra, numerical techniques, and Bayesian estimation theory
- Suggests additional reading at the end of each chapter, including the latest research in each sub-field, in addition to a full Bibliography at the end of the book
- Supplies supplementary course material for students at the associated website, http://szeliski.org/Book/
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.
Dr. Richard Szeliski has more than 25 years’ experience in computer vision research, most notably at Digital Equipment Corporation and Microsoft Research. This text draws on that experience, as well as on computer vision courses he has taught at the University of Washington and Stanford.
정보제공 :
목차
Introduction.- Image Formation.- Image Processing.- Feature Detection and Matching.- Segmentation.- Feature-based Alignment.- Structure from Motion.- Dense Motion Estimation.- Image Stitching.- Computational Photography.- Stereo Correspondence.- 3D Reconstruction.- Image-based Rendering.- Recognition.
정보제공 :