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Best multiple view geometry in computer vision

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Multiple View Geometry in Computer Vision Multiple View Geometry in Computer Vision
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Computer Vision: Algorithms and Applications (Texts in Computer Science) Computer Vision: Algorithms and Applications (Texts in Computer Science)
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Algebraic Curves in Multiple-View Geometry: An algebraic geometry approach to computer vision Algebraic Curves in Multiple-View Geometry: An algebraic geometry approach to computer vision
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Uncertain Projective Geometry: Statistical Reasoning for Polyhedral Object Reconstruction (Lecture Notes in Computer Science) Uncertain Projective Geometry: Statistical Reasoning for Polyhedral Object Reconstruction (Lecture Notes in Computer Science)
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Multiple View Geometry in Computer Vision Second Edition by Richard Hartley (2004-03-25) Multiple View Geometry in Computer Vision Second Edition by Richard Hartley (2004-03-25)
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Innovating: A Doer's Manifesto for Starting from a Hunch, Prototyping Problems, Scaling Up, and Learning to Be Productively Wrong (The MIT Press) Innovating: A Doer's Manifesto for Starting from a Hunch, Prototyping Problems, Scaling Up, and Learning to Be Productively Wrong (The MIT Press)
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Control of Multiple Robots Using Vision Sensors (Advances in Industrial Control) Control of Multiple Robots Using Vision Sensors (Advances in Industrial Control)
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Computer Vision - ECCV'98: 5th European Conference on Computer Vision, Freiburg, Germany, June 2-6, 1998, Proceedings, Volume I (Lecture Notes in Computer Science) Computer Vision - ECCV'98: 5th European Conference on Computer Vision, Freiburg, Germany, June 2-6, 1998, Proceedings, Volume I (Lecture Notes in Computer Science)
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1. Multiple View Geometry in Computer Vision

Feature

Cambridge University Press

Description

A basic problem in computer vision is to understand the structure of a real world scene. This book covers relevant geometric principles and how to represent objects algebraically so they can be computed and applied. Recent major developments in the theory and practice of scene reconstruction are described in detail in a unified framework. Richard Hartley and Andrew Zisserman provide comprehensive background material and explain how to apply the methods and implement the algorithms. First Edition HB (2000): 0-521-62304-9

2. Computer Vision: Algorithms and Applications (Texts in Computer Science)

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Springer

Description

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.

3. Algebraic Curves in Multiple-View Geometry: An algebraic geometry approach to computer vision

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Algebraic Curves in Multiple View Geometry

Description

We introduce multiple-view geometry for algebraic curves,with applications in both static and dynamic scenes. More precisely, we show how the epipolar geometry can be recovered from algebraic curves. For that purpose, we introduce a generalization of Kruppas equations, which express the epipolar constraint for algebraic curves. Reconstruction from a single image based on symmetry is also considered and we show how this relates to algebraic curves for a simple example. We also investigate the question of three-dimensional reconstruction of an algebraic curve from two or more views. In the case of two views, we show that for a generic situation, there are two solutions for the reconstruction, which allows extracting the right solution, provided the degree of the curve is greater or equal to 3. When more than two views are available,we show that there construction can be done by linear computations, using either the dual curve or the variety of intersecting lines. In both cases, no curve tting is necessary in the image space. Finally we focus on dynamic scenes and show when and how the trajectory of a moving point can be recovered from a moving camera.

4. Uncertain Projective Geometry: Statistical Reasoning for Polyhedral Object Reconstruction (Lecture Notes in Computer Science)

Description

Algebraic projective geometry, with its multilinear relations and its embedding into Grassmann-Cayley algebra, has become the basic representation of multiple view geometry, resulting in deep insights into the algebraic structure of geometric relations, as well as in efficient and versatile algorithms for computer vision and image analysis.

This book provides a coherent integration of algebraic projective geometry and spatial reasoning under uncertainty with applications in computer vision. Beyond systematically introducing the theoretical foundations from geometry and statistics and clear rules for performing geometric reasoning under uncertainty, the author provides a collection of detailed algorithms.

The book addresses researchers and advanced students interested in algebraic projective geometry for image analysis, in statistical representation of objects and transformations, or in generic tools for testing and estimating within the context of geometric multiple-view analysis.

5. Multiple View Geometry in Computer Vision Second Edition by Richard Hartley (2004-03-25)

6. Innovating: A Doer's Manifesto for Starting from a Hunch, Prototyping Problems, Scaling Up, and Learning to Be Productively Wrong (The MIT Press)

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Mit Press

Description

Innovating is for doers: you don't need to wait for an earth-shattering idea, but can build one with a hunch and scale it up to impact.

Innovation is the subject of countless books and courses, but there's very little out there about how you actually innovate. Innovation and entrepreneurship are not one and the same, although aspiring innovators often think of them that way. They are told to get an idea and a team and to build a show-and-tell for potential investors. In Innovating, Luis Perez-Breva describes another approacha doer's approach developed over a decade at MIT and internationally in workshops, classes, and companies. He shows that to start innovating it doesn't require an earth-shattering idea; all it takes is a hunch. Anyone can do it. By prototyping a problem and learning by being wrong, innovating can be scaled up to make an impact. As Perez-Breva demonstrates, "no thing is new" at the outset of what we only later celebrate as innovation.

In Innovating, the processillustrated by unique and dynamic artworkis shown to be empirical, experimental, nonlinear, and incremental. You give your hunch the structure of a problem. Anything can be a part. Your innovating accrues other people's knowledge and skills. Perez-Breva describes how to create a kit for innovating, and outlines questions that will help you think in new ways. Finally, he shows how to systematize what you've learned: to advocate, communicate, scale up, manage innovating continuously, and documentyou need a notebook to converse with yourself, he advises. Everyone interested in innovating also needs to read this book.

7. Control of Multiple Robots Using Vision Sensors (Advances in Industrial Control)

Description

This monograph introduces novel methods for the control and navigation of mobile robots using multiple-1-d-view models obtained from omni-directional cameras. This approach overcomes field-of-view and robustness limitations, simultaneously enhancing accuracy and simplifying application on real platforms. The authors also address coordinated motion tasks for multiple robots, exploring different system architectures, particularly the use of multiple aerial cameras in driving robot formations on the ground. Again, this has benefits of simplicity, scalability and flexibility. Coverage includes details of:

  • a method for visual robot homing based on a memory of omni-directional images;
  • a novel vision-based pose stabilization methodology for non-holonomic ground robots based on sinusoidal-varying control inputs;
  • an algorithm to recover a generic motion between two 1-d views and which does not require a third view;
  • a novel multi-robot setup where multiple camera-carrying unmanned aerial vehicles are used to observe and control a formation of ground mobile robots; and
  • three coordinate-free methods for decentralized mobile robot formation stabilization.

The performance of the different methods is evaluated both in simulation and experimentally with real robotic platforms and vision sensors.

Control of Multiple Robots Using Vision Sensors will serve both academic researchers studying visual control of single and multiple robots and robotics engineers seeking to design control systems based on visual sensors.

8. Computer Vision - ECCV'98: 5th European Conference on Computer Vision, Freiburg, Germany, June 2-6, 1998, Proceedings, Volume I (Lecture Notes in Computer Science)

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Used Book in Good Condition

Description

This two-volume set constitutes the refereed proceedings of the 5th European Conference on Computer Vision, ECCV'98, held in Freiburg, Germany, in June 1998.
The 42 revised full papers and 70 revised posters presented were carefully selected from a total of 223 papers submitted. The papers are organized in sections on multiple-view geometry, stereo vision and calibration, geometry and invariances, structure from motion, colour and indexing, grouping and segmentation, tracking, condensation, matching and registration, image sequences and video, shape and shading, motion and flow, medical imaging, appearance and recognition, robotics and active vision, and motion segmentation.

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