HOME > 상세정보

상세정보

Riemannian computing in computer vision [electronic resource]

Riemannian computing in computer vision [electronic resource]

자료유형
E-Book(소장)
개인저자
Turaga, Pavan. Srivastava, Anuj, 1968-.
서명 / 저자사항
Riemannian computing in computer vision [electronic resource] / Pavan K. Turaga, Anuj Srivastava, editors.
발행사항
Cham :   Springer International Publishing :   Imprint: Springer,   2016.  
형태사항
1 online resource (vi, 391 p.) : ill. (some col.).
ISBN
9783319229577
요약
This book presents a comprehensive treatise on Riemannian geometric computations and related statistical inferences in several computer vision problems. This edited volume includes chapter contributions from leading figures in the field of computer vision who are applying Riemannian geometric approaches in problems such as face recognition, activity recognition, object detection, biomedical image analysis, and structure-from-motion. Some of the mathematical entities that necessitate a geometric analysis include rotation matrices (e.g. in modeling camera motion), stick figures (e.g. for activity recognition), subspace comparisons (e.g. in face recognition), symmetric positive-definite matrices (e.g. in diffusion tensor imaging), and function-spaces (e.g. in studying shapes of closed contours). Illustrates Riemannian computing theory on applications in computer vision, machine learning, and robotics. Emphasis on algorithmic advances that will allow re-application in other contexts. Written by leading researchers in computer vision and Riemannian computing, from universities and industry.
일반주기
Title from e-Book title page.  
내용주기
Welcome to Riemannian Computing in Computer Vision -- Recursive Computation of the Fr´echet Mean on Non-Positively Curved Riemannian Manifolds with Applications -- Kernels on Riemannian Manifolds -- Canonical Correlation Analysis on SPD(n) manifolds -- Probabilistic Geodesic Models for Regression and Dimensionality Reduction on Riemannian Manifolds -- Robust Estimation for Computer Vision using Grassmann Manifolds -- Motion Averaging in 3D Reconstruction Problems -- Lie-Theoretic Multi-Robot Localization -- CovarianceWeighted Procrustes Analysis -- Elastic Shape Analysis of Functions, Curves and Trajectories -- Why Use Sobolev Metrics on the Space of Curves -- Elastic Shape Analysis of Surfaces and Images -- Designing a Boosted Classifier on Riemannian Manifolds -- A General Least Squares Regression Framework on Matrix Manifolds for Computer Vision -- Domain Adaptation Using the Grassmann Manifold -- Coordinate Coding on the Riemannian Manifold of Symmetric Positive Definite Matrices for Image Classification -- Summarization and Search over Geometric Spaces.
서지주기
Includes bibliographical references and index.
이용가능한 다른형태자료
Issued also as a book.  
일반주제명
Computer vision. Geometry, Riemannian.
바로가기
URL
000 00000nam u2200205 a 4500
001 000046024065
005 20200416133948
006 m d
007 cr
008 200414s2016 sz a ob 001 0 eng d
020 ▼a 9783319229577
040 ▼a 211009 ▼c 211009 ▼d 211009
050 4 ▼a TA1637-1638
082 0 4 ▼a 006.37 ▼2 23
084 ▼a 006.37 ▼2 DDCK
090 ▼a 006.37
245 0 0 ▼a Riemannian computing in computer vision ▼h [electronic resource] / ▼c Pavan K. Turaga, Anuj Srivastava, editors.
260 ▼a Cham : ▼b Springer International Publishing : ▼b Imprint: Springer, ▼c 2016.
300 ▼a 1 online resource (vi, 391 p.) : ▼b ill. (some col.).
500 ▼a Title from e-Book title page.
504 ▼a Includes bibliographical references and index.
505 0 ▼a Welcome to Riemannian Computing in Computer Vision -- Recursive Computation of the Fr´echet Mean on Non-Positively Curved Riemannian Manifolds with Applications -- Kernels on Riemannian Manifolds -- Canonical Correlation Analysis on SPD(n) manifolds -- Probabilistic Geodesic Models for Regression and Dimensionality Reduction on Riemannian Manifolds -- Robust Estimation for Computer Vision using Grassmann Manifolds -- Motion Averaging in 3D Reconstruction Problems -- Lie-Theoretic Multi-Robot Localization -- CovarianceWeighted Procrustes Analysis -- Elastic Shape Analysis of Functions, Curves and Trajectories -- Why Use Sobolev Metrics on the Space of Curves -- Elastic Shape Analysis of Surfaces and Images -- Designing a Boosted Classifier on Riemannian Manifolds -- A General Least Squares Regression Framework on Matrix Manifolds for Computer Vision -- Domain Adaptation Using the Grassmann Manifold -- Coordinate Coding on the Riemannian Manifold of Symmetric Positive Definite Matrices for Image Classification -- Summarization and Search over Geometric Spaces.
520 ▼a This book presents a comprehensive treatise on Riemannian geometric computations and related statistical inferences in several computer vision problems. This edited volume includes chapter contributions from leading figures in the field of computer vision who are applying Riemannian geometric approaches in problems such as face recognition, activity recognition, object detection, biomedical image analysis, and structure-from-motion. Some of the mathematical entities that necessitate a geometric analysis include rotation matrices (e.g. in modeling camera motion), stick figures (e.g. for activity recognition), subspace comparisons (e.g. in face recognition), symmetric positive-definite matrices (e.g. in diffusion tensor imaging), and function-spaces (e.g. in studying shapes of closed contours). Illustrates Riemannian computing theory on applications in computer vision, machine learning, and robotics. Emphasis on algorithmic advances that will allow re-application in other contexts. Written by leading researchers in computer vision and Riemannian computing, from universities and industry.
530 ▼a Issued also as a book.
538 ▼a Mode of access: World Wide Web.
650 0 ▼a Computer vision.
650 0 ▼a Geometry, Riemannian.
700 1 ▼a Turaga, Pavan.
700 1 ▼a Srivastava, Anuj, ▼d 1968-.
856 4 0 ▼u https://oca.korea.ac.kr/link.n2s?url=http://dx.doi.org/10.1007/978-3-319-22957-7
945 ▼a KLPA
991 ▼a E-Book(소장)

소장정보

No. 소장처 청구기호 등록번호 도서상태 반납예정일 예약 서비스
No. 1 소장처 중앙도서관/e-Book 컬렉션/ 청구기호 CR 006.37 등록번호 E14020428 도서상태 대출불가(열람가능) 반납예정일 예약 서비스 M

관련분야 신착자료

Deisenroth, Marc Peter (2020)