Kenichi Kanatani's Books

Ellipse Fitting for Computer Vision

Implementation and Applications

By: Margrit Betke

Write a review Read reviews Take a Quiz Solve Book Puzzle

Ellipse Fitting for Computer Vision

Implementation and Applications

By: Margrit Betke

Because circular objects are projected to ellipses in images, ellipse fitting is a first step for 3-D analysis of circular objects in computer vision applications. For this reason, the study of ellipse fitting began as soon as computers came into use for image analysis in the 1970s, but it is only recently that optimal computation techniques based on the statistical properties of noise were established. These include renormalization (1993), which was then improved as FNS (2000) and HEIV (2000). Later, further improvements, called hyperaccurate correction (2006), HyperLS (2009), and hyper-renormalization (2012), were presented. Today, these are regarded as the most accurate fitting methods among all known techniques. This book describes these algorithms as well implementation details and applications to 3-D scene analysis. We also present general mathematical theories of statistical optimization underlying all ellipse fitting algorithms, including rigorous covariance and bias analyses and the theoretical accuracy limit. The results can be directly applied to other computer vision tasks including computing fundamental matrices and homographies between images. This book can serve not simply as a reference of ellipse fitting algorithms for researchers, but also as learning material for beginners who want to start computer vision research. The sample program codes are downloadable from the website: https://sites.google.com/a/morganclaypool.com/ellipse-fitting-for-computer-vision-implementation-and-applications.

Average ratings

N/A

Write a review

Solve jigsaw puzzle

Age

Page Count

128

Publisher

Springer Nature

Published Date

ISBN 10

303101815X

ISBN 13

9783031018152

Statistical Optimization for Geometric Computation

Theory and Practice

By: Kenichi Kanatani

Write a review Read reviews Take a Quiz Solve Book Puzzle

Statistical Optimization for Geometric Computation

Theory and Practice

By: Kenichi Kanatani

This text for graduate students discusses the mathematical foundations of statistical inference for building three-dimensional models from image and sensor data that contain noise--a task involving autonomous robots guided by video cameras and sensors. The text employs a theoretical accuracy for the optimization procedure, which maximizes the reliability of estimations based on noise data. The numerous mathematical prerequisites for developing the theories are explained systematically in separate chapters. These methods range from linear algebra, optimization, and geometry to a detailed statistical theory of geometric patterns, fitting estimates, and model selection. In addition, examples drawn from both synthetic and real data demonstrate the insufficiencies of conventional procedures and the improvements in accuracy that result from the use of optimal methods.

Average ratings

N/A

Write a review

Solve jigsaw puzzle

Age

Page Count

548

Publisher

Courier Corporation

Published Date

ISBN 10

0486443086

ISBN 13

9780486443089

3D Rotations

Parameter Computation and Lie Algebra based Optimization

By: Kenichi Kanatani

Write a review Read reviews Take a Quiz Solve Book Puzzle

3D Rotations

Parameter Computation and Lie Algebra based Optimization

By: Kenichi Kanatani

3D rotation analysis is widely encountered in everyday problems thanks to the development of computers. Sensing 3D using cameras and sensors, analyzing and modeling 3D for computer vision and computer graphics, and controlling and simulating robot motion all require 3D rotation computation. This book focuses on the computational analysis of 3D rotation, rather than classical motion analysis. It regards noise as random variables and models their probability distributions. It also pursues statistically optimal computation for maximizing the expected accuracy, as is typical of nonlinear optimization. All concepts are illustrated using computer vision applications as examples. Mathematically, the set of all 3D rotations forms a group denoted by SO(3). Exploiting this group property, we obtain an optimal solution analytical or numerically, depending on the problem. Our numerical scheme, which we call the "Lie algebra method," is based on the Lie group structure of SO(3). This book also proposes computing projects for readers who want to code the theories presented in this book, describing necessary 3D simulation setting as well as providing real GPS 3D measurement data. To help readers not very familiar with abstract mathematics, a brief overview of quaternion algebra, matrix analysis, Lie groups, and Lie algebras is provided as Appendix at the end of the volume.

Average ratings

N/A

Write a review

Solve jigsaw puzzle

Age

Page Count

171

Publisher

CRC Press

Published Date

ISBN 10

1000070662

ISBN 13

9781000070668

Linear Algebra for Pattern Processing

Projection, Singular Value Decomposition, and Pseudoinverse

By: Kenichi Kanatani

Write a review Read reviews Take a Quiz Solve Book Puzzle

Linear Algebra for Pattern Processing

Projection, Singular Value Decomposition, and Pseudoinverse

By: Kenichi Kanatani

Linear algebra is one of the most basic foundations of a wide range of scientific domains, and most textbooks of linear algebra are written by mathematicians. However, this book is specifically intended to students and researchers of pattern information processing, analyzing signals such as images and exploring computer vision and computer graphics applications. The author himself is a researcher of this domain. Such pattern information processing deals with a large amount of data, which are represented by high-dimensional vectors and matrices. There, the role of linear algebra is not merely numerical computation of large-scale vectors and matrices. In fact, data processing is usually accompanied with "geometric interpretation." For example, we can think of one data set being "orthogonal" to another and define a "distance" between them or invoke geometric relationships such as "projecting" some data onto some space. Such geometric concepts not only help us mentally visualize abstract high-dimensional spaces in intuitive terms but also lead us to find what kind of processing is appropriate for what kind of goals. First, we take up the concept of "projection" of linear spaces and describe "spectral decomposition," "singular value decomposition," and "pseudoinverse" in terms of projection. As their applications, we discuss least-squares solutions of simultaneous linear equations and covariance matrices of probability distributions of vector random variables that are not necessarily positive definite. We also discuss fitting subspaces to point data and factorizing matrices in high dimensions in relation to motion image analysis. Finally, we introduce a computer vision application of reconstructing the 3D location of a point from three camera views to illustrate the role of linear algebra in dealing with data with noise. This book is expected to help students and researchers of pattern information processing deepen the geometric understanding of linear algebra.

Average ratings

N/A

Write a review

Solve jigsaw puzzle

Age

Page Count

141

Publisher

Springer Nature

Published Date

ISBN 10

303102544X

ISBN 13

9783031025440

Guide to 3D Vision Computation

Geometric Analysis and Implementation

By: Kenichi Kanatani,Yasuyuki Sugaya,Yasushi Kanazawa

Write a review Read reviews Take a Quiz Solve Book Puzzle

Guide to 3D Vision Computation

Geometric Analysis and Implementation

By: Kenichi Kanatani,Yasuyuki Sugaya,Yasushi Kanazawa

This classroom-tested and easy-to-understand textbook/reference describes the state of the art in 3D reconstruction from multiple images, taking into consideration all aspects of programming and implementation. Unlike other computer vision textbooks, this guide takes a unique approach in which the initial focus is on practical application and the procedures necessary to actually build a computer vision system. The theoretical background is then briefly explained afterwards, highlighting how one can quickly and simply obtain the desired result without knowing the derivation of the mathematical detail. Features: reviews the fundamental algorithms underlying computer vision; describes the latest techniques for 3D reconstruction from multiple images; summarizes the mathematical theory behind statistical error analysis for general geometric estimation problems; presents derivations at the end of each chapter, with solutions supplied at the end of the book; provides additional material at an associated website.

Average ratings

N/A

Write a review

Solve jigsaw puzzle

Age

Page Count

322

Publisher

Springer

Published Date

ISBN 10

3319484931

ISBN 13

9783319484938

Understanding Geometric Algebra

Hamilton, Grassmann, and Clifford for Computer Vision and Graphics

By: Kenichi Kanatani

Write a review Read reviews Take a Quiz Solve Book Puzzle

Understanding Geometric Algebra

Hamilton, Grassmann, and Clifford for Computer Vision and Graphics

By: Kenichi Kanatani

Understanding Geometric Algebra: Hamilton, Grassmann, and Clifford for Computer Vision and Graphics introduces geometric algebra with an emphasis on the background mathematics of Hamilton, Grassmann, and Clifford. It shows how to describe and compute geometry for 3D modeling applications in computer graphics and computer vision.Unlike similar texts

Average ratings

N/A

Write a review

Solve jigsaw puzzle

Age

Page Count

207

Publisher

CRC Press

Published Date

ISBN 10

1482259516

ISBN 13

9781482259513

Understanding Geometric Algebra

Hamilton, Grassmann, and Clifford for Computer Vision and Graphics

By: Kenichi Kanatani

Write a review Read reviews Take a Quiz Solve Book Puzzle

Understanding Geometric Algebra

Hamilton, Grassmann, and Clifford for Computer Vision and Graphics

By: Kenichi Kanatani

Average ratings

N/A

Write a review

Solve jigsaw puzzle

Age

Page Count

207

Publisher

CRC Press

Published Date

ISBN 10

1482259516

ISBN 13

9781482259513

Linear Algebra for Pattern Processing

Projection, Singular Value Decomposition, and Pseudoinverse

By: Kenichi Kanatani

Write a review Read reviews Take a Quiz Solve Book Puzzle

Linear Algebra for Pattern Processing

Projection, Singular Value Decomposition, and Pseudoinverse

By: Kenichi Kanatani

Average ratings

N/A

Write a review

Solve jigsaw puzzle

Age

Page Count

157

Publisher

Morgan & Claypool Publishers

Published Date

ISBN 10

1636391087

ISBN 13

9781636391083

3D Rotations

Parameter Computation and Lie Algebra based Optimization

By: Kenichi Kanatani

Write a review Read reviews Take a Quiz Solve Book Puzzle

3D Rotations

Parameter Computation and Lie Algebra based Optimization

By: Kenichi Kanatani

Average ratings

N/A

Write a review

Solve jigsaw puzzle

Age

Page Count

119

Publisher

CRC Press

Published Date

ISBN 10

1000071650

ISBN 13

9781000071658

Ellipse Fitting for Computer Vision

Implementation and Applications

By: Kenichi Kanatani,Yasuyuki Sugaya,Yasushi Kanazawa

Write a review Read reviews Take a Quiz Solve Book Puzzle

Ellipse Fitting for Computer Vision

Implementation and Applications

By: Kenichi Kanatani,Yasuyuki Sugaya,Yasushi Kanazawa

Average ratings

N/A

Write a review

Solve jigsaw puzzle

Age

Page Count

143

Publisher

Morgan & Claypool Publishers

Published Date

ISBN 10

1627054987

ISBN 13

9781627054980

Computational Methods for Integrating Vision and Language

By: Kenichi Kanatani,Yasuyuki Sugaya

Write a review Read reviews Take a Quiz Solve Book Puzzle

Computational Methods for Integrating Vision and Language

By: Kenichi Kanatani,Yasuyuki Sugaya

Modeling data from visual and linguistic modalities together creates opportunities for better understanding of both, and supports many useful applications. Examples of dual visual-linguistic data includes images with keywords, video with narrative, and figures in documents. We consider two key task-driven themes: translating from one modality to another (e.g., inferring annotations for images) and understanding the data using all modalities, where one modality can help disambiguate information in another. The multiple modalities can either be essentially semantically redundant (e.g., keywords provided by a person looking at the image), or largely complementary (e.g., meta data such as the camera used). Redundancy and complementarity are two endpoints of a scale, and we observe that good performance on translation requires some redundancy, and that joint inference is most useful where some information is complementary. Computational methods discussed are broadly organized into ones for simple keywords, ones going beyond keywords toward natural language, and ones considering sequential aspects of natural language. Methods for keywords are further organized based on localization of semantics, going from words about the scene taken as whole, to words that apply to specific parts of the scene, to relationships between parts. Methods going beyond keywords are organized by the linguistic roles that are learned, exploited, or generated. These include proper nouns, adjectives, spatial and comparative prepositions, and verbs. More recent developments in dealing with sequential structure include automated captioning of scenes and video, alignment of video and text, and automated answering of questions about scenes depicted in images.

Average ratings

N/A

Write a review

Solve jigsaw puzzle

Age

Page Count

211

Publisher

Springer Nature

Published Date

ISBN 10

3031018141

ISBN 13

9783031018145

Statistical Optimization for Geometric Computation

Theory and Practice

By: Kenichi Kanatani

Write a review Read reviews Take a Quiz Solve Book Puzzle

Statistical Optimization for Geometric Computation

Theory and Practice

By: Kenichi Kanatani

Average ratings

N/A

Write a review

Solve jigsaw puzzle

Age

Page Count

548

Publisher

Courier Corporation

Published Date

ISBN 10

0486443086

ISBN 13

9780486443089

Computational Methods for Integrating Vision and Language

By: Kenichi Kanatani,Yasuyuki Sugaya

Write a review Read reviews Take a Quiz Solve Book Puzzle

Computational Methods for Integrating Vision and Language

By: Kenichi Kanatani,Yasuyuki Sugaya

Average ratings

N/A

Write a review

Solve jigsaw puzzle

Age

Page Count

211

Publisher

Springer

Published Date

ISBN 10

3031006860

ISBN 13

9783031006869

Book's by Kenichi Kanatani

Ellipse Fitting for Computer Vision

Ellipse Fitting for Computer Vision

Statistical Optimization for Geometric Computation

Statistical Optimization for Geometric Computation

3D Rotations

3D Rotations

Linear Algebra for Pattern Processing

Linear Algebra for Pattern Processing

Guide to 3D Vision Computation

Guide to 3D Vision Computation

Understanding Geometric Algebra

Understanding Geometric Algebra

Understanding Geometric Algebra

Understanding Geometric Algebra

Linear Algebra for Pattern Processing

Linear Algebra for Pattern Processing

3D Rotations

3D Rotations

Ellipse Fitting for Computer Vision

Ellipse Fitting for Computer Vision

Computational Methods for Integrating Vision and Language

Computational Methods for Integrating Vision and Language

Statistical Optimization for Geometric Computation

Statistical Optimization for Geometric Computation

Computational Methods for Integrating Vision and Language

Computational Methods for Integrating Vision and Language

Username

Password

Username

Password

Password again

Birthday

Email