Multiple View Geometry in Computer Vision


Author: Richard Hartley,Andrew Zisserman
Publisher: Cambridge University Press
ISBN: 1139449141
Category: Computers
Page: N.A
View: 3954

Continue Reading →

A basic problem in computer vision is to understand the structure of a real world scene given several images of it. Techniques for solving this problem are taken from projective geometry and photogrammetry. Here, the authors cover the geometric principles and their algebraic representation in terms of camera projection matrices, the fundamental matrix and the trifocal tensor. The theory and methods of computation of these entities are discussed with real examples, as is their use in the reconstruction of scenes from multiple images. The new edition features an extended introduction covering the key ideas in the book (which itself has been updated with additional examples and appendices) and significant new results which have appeared since the first edition. Comprehensive background material is provided, so readers familiar with linear algebra and basic numerical methods can understand the projective geometry and estimation algorithms presented, and implement the algorithms directly from the book.

Multiple View Geometry in Computer Vision


Author: Richard Hartley,Andrew Zisserman
Publisher: Cambridge University Press
ISBN: 9780521540513
Category: Computers
Page: 655
View: 3164

Continue Reading →

A basic problem in computer vision is to understand the structure of a real world scene given several images of it. Techniques for solving this problem are taken from projective geometry and photogrammetry. Here, the authors cover the geometric principles and their algebraic representation in terms of camera projection matrices, the fundamental matrix and the trifocal tensor. The theory and methods of computation of these entities are discussed with real examples, as is their use in the reconstruction of scenes from multiple images. The new edition features an extended introduction covering the key ideas in the book (which itself has been updated with additional examples and appendices) and significant new results which have appeared since the first edition. Comprehensive background material is provided, so readers familiar with linear algebra and basic numerical methods can understand the projective geometry and estimation algorithms presented, and implement the algorithms directly from the book.

Geometric Invariance in Computer Vision


Author: Joseph L. Mundy,Andrew Zisserman
Publisher: Mit Press
ISBN: N.A
Category: Computers
Page: 540
View: 2234

Continue Reading →

These twenty-three contributions focus on the most recent developments in the rapidly evolving field of geometric invariants and their application to computer vision. The introduction summarizes the basics of invariant theory, discusses how invariants are related to problems in computer vision, and looks at the future possibilities, particularly the notion that invariant analysis might provide a solution to the elusive problem of recognizing general curved 3D objects from an arbitrary viewpoint. The remaining chapters consist of original papers that present important developments as well as tutorial articles that provide useful background material. These chapters are grouped into categories covering algebraic invariants, nonalgebraic invariants, invariants of multiple views, and applications. An appendix provides an extensive introduction to projective geometry and its applications to basic problems in computer vision. Joseph Mundy is a Coolidge Fellow at GE Corporate Research & Development. Andrew Zisserman is a Research Fellow in the Robotics Research Group at Oxford University.

Programming Computer Vision with Python

Tools and algorithms for analyzing images
Author: Jan Erik Solem
Publisher: "O'Reilly Media, Inc."
ISBN: 1449341934
Category: Computers
Page: 264
View: 2245

Continue Reading →

If you want a basic understanding of computer vision’s underlying theory and algorithms, this hands-on introduction is the ideal place to start. You’ll learn techniques for object recognition, 3D reconstruction, stereo imaging, augmented reality, and other computer vision applications as you follow clear examples written in Python. Programming Computer Vision with Python explains computer vision in broad terms that won’t bog you down in theory. You get complete code samples with explanations on how to reproduce and build upon each example, along with exercises to help you apply what you’ve learned. This book is ideal for students, researchers, and enthusiasts with basic programming and standard mathematical skills. Learn techniques used in robot navigation, medical image analysis, and other computer vision applications Work with image mappings and transforms, such as texture warping and panorama creation Compute 3D reconstructions from several images of the same scene Organize images based on similarity or content, using clustering methods Build efficient image retrieval techniques to search for images based on visual content Use algorithms to classify image content and recognize objects Access the popular OpenCV library through a Python interface

An Invitation to 3-D Vision

From Images to Geometric Models
Author: Yi Ma,Stefano Soatto,Jana Kosecká,S. Shankar Sastry
Publisher: Springer Science & Business Media
ISBN: 0387217797
Category: Computers
Page: 528
View: 8018

Continue Reading →

This book introduces the geometry of 3-D vision, that is, the reconstruction of 3-D models of objects from a collection of 2-D images. It details the classic theory of two view geometry and shows that a more proper tool for studying the geometry of multiple views is the so-called rank consideration of the multiple view matrix. It also develops practical reconstruction algorithms and discusses possible extensions of the theory.

Photogrammetric Computer Vision

Statistics, Geometry, Orientation and Reconstruction
Author: Wolfgang Förstner,Bernhard P. Wrobel
Publisher: Springer
ISBN: 3319115502
Category: Computers
Page: 816
View: 4969

Continue Reading →

This textbook offers a statistical view on the geometry of multiple view analysis, required for camera calibration and orientation and for geometric scene reconstruction based on geometric image features. The authors have backgrounds in geodesy and also long experience with development and research in computer vision, and this is the first book to present a joint approach from the converging fields of photogrammetry and computer vision. Part I of the book provides an introduction to estimation theory, covering aspects such as Bayesian estimation, variance components, and sequential estimation, with a focus on the statistically sound diagnostics of estimation results essential in vision metrology. Part II provides tools for 2D and 3D geometric reasoning using projective geometry. This includes oriented projective geometry and tools for statistically optimal estimation and test of geometric entities and transformations and their relations, tools that are useful also in the context of uncertain reasoning in point clouds. Part III is devoted to modelling the geometry of single and multiple cameras, addressing calibration and orientation, including statistical evaluation and reconstruction of corresponding scene features and surfaces based on geometric image features. The authors provide algorithms for various geometric computation problems in vision metrology, together with mathematical justifications and statistical analysis, thus enabling thorough evaluations. The chapters are self-contained with numerous figures and exercises, and they are supported by an appendix that explains the basic mathematical notation and a detailed index. The book can serve as the basis for undergraduate and graduate courses in photogrammetry, computer vision, and computer graphics. It is also appropriate for researchers, engineers, and software developers in the photogrammetry and GIS industries, particularly those engaged with statistically based geometric computer vision methods.

Computer Vision

Models, Learning, and Inference
Author: Simon J. D. Prince
Publisher: Cambridge University Press
ISBN: 1107011795
Category: Computers
Page: 580
View: 5273

Continue Reading →

A modern treatment focusing on learning and inference, with minimal prerequisites, real-world examples and implementable algorithms.

Three-dimensional Computer Vision

A Geometric Viewpoint
Author: Olivier Faugeras
Publisher: MIT Press
ISBN: 9780262061582
Category: Computers
Page: 663
View: 5538

Continue Reading →

This monograph by one of the world's leading vision researchers provides a thorough, mathematically rigorous exposition of a broad and vital area in computer vision: the problems and techniques related to three-dimensional (stereo) vision and motion. The emphasis is on using geometry to solve problems in stereo and motion, with examples from navigation and object recognition.Faugeras takes up such important problems in computer vision as projective geometry, camera calibration, edge detection, stereo vision (with many examples on real images), different kinds of representations and transformations (especially 3-D rotations), uncertainty and methods of addressing it, and object representation and recognition. His theoretical account is illustrated with the results of actual working programs.Three-Dimensional Computer Vision proposes solutions to problems arising from a specific robotics scenario in which a system must perceive and act. Moving about an unknown environment, the system has to avoid static and mobile obstacles, build models of objects and places in order to be able to recognize and locate them, and characterize its own motion and that of moving objects, by providing descriptions of the corresponding three-dimensional motions. The ideas generated, however, can be used indifferent settings, resulting in a general book on computer vision that reveals the fascinating relationship of three-dimensional geometry and the imaging process.Olivier Faugeras is Research Director of the Computer Vision and Robotics Laboratory at INRIA Sophia-Antipolis and a Professor of Applied Mathematics at the Ecole Polytechnique in Paris.

Computer Vision for Visual Effects


Author: Richard J. Radke
Publisher: Cambridge University Press
ISBN: 0521766877
Category: Business & Economics
Page: 405
View: 5466

Continue Reading →

This book explores the fundamental computer vision principles and state-of-the-art algorithms used to create cutting-edge visual effects for movies and television. It describes classical computer vision algorithms and recent developments, features more than 200 original images, and contains in-depth interviews with Hollywood visual effects artists that tie the mathematical concepts to real-world filmmaking.

The Geometry of Multiple Images

The Laws that Govern the Formation of Multiple Images of a Scene and Some of Their Applications
Author: Olivier Faugeras,Quang-Tuan Luong,Théo Papadopoulo
Publisher: MIT Press
ISBN: 9780262562041
Category: Mathematics
Page: 644
View: 2009

Continue Reading →

This book formalizes and analyzes the relations between multiple views of a scene from the perspective of various types of geometries. A key feature is that it considers Euclidean and affine geometries as special cases of projective geometry.

Computer Vision

Algorithms and Applications
Author: Richard Szeliski
Publisher: Springer Science & Business Media
ISBN: 9781848829350
Category: Computers
Page: 812
View: 8735

Continue Reading →

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.

An Introduction to 3D Computer Vision Techniques and Algorithms


Author: Boguslaw Cyganek,J. Paul Siebert
Publisher: John Wiley & Sons
ISBN: 1119964474
Category: Science
Page: 504
View: 4497

Continue Reading →

Computer vision encompasses the construction of integrated vision systems and the application of vision to problems of real-world importance. The process of creating 3D models is still rather difficult, requiring mechanical measurement of the camera positions or manual alignment of partial 3D views of a scene. However using algorithms, it is possible to take a collection of stereo-pair images of a scene and then automatically produce a photo-realistic, geometrically accurate digital 3D model. This book provides a comprehensive introduction to the methods, theories and algorithms of 3D computer vision. Almost every theoretical issue is underpinned with practical implementation or a working algorithm using pseudo-code and complete code written in C++ and MatLab®. There is the additional clarification of an accompanying website with downloadable software, case studies and exercises. Organised in three parts, Cyganek and Siebert give a brief history of vision research, and subsequently: present basic low-level image processing operations for image matching, including a separate chapter on image matching algorithms; explain scale-space vision, as well as space reconstruction and multiview integration; demonstrate a variety of practical applications for 3D surface imaging and analysis; provide concise appendices on topics such as the basics of projective geometry and tensor calculus for image processing, distortion and noise in images plus image warping procedures. An Introduction to 3D Computer Vision Algorithms and Techniques is a valuable reference for practitioners and programmers working in 3D computer vision, image processing and analysis as well as computer visualisation. It would also be of interest to advanced students and researchers in the fields of engineering, computer science, clinical photography, robotics, graphics and mathematics.

Modern Mathematics and Applications in Computer Graphics and Vision


Author: Hongyu Guo
Publisher: World Scientific Publishing Company
ISBN: 9814449350
Category: Computers
Page: 524
View: 2493

Continue Reading →

This book presents a concise exposition of modern mathematical concepts, models and methods with applications in computer graphics, vision and machine learning. The compendium is organized in four parts — Algebra, Geometry, Topology, and Applications. One of the features is a unique treatment of tensor and manifold topics to make them easier for the students. All proofs are omitted to give an emphasis on the exposition of the concepts. Effort is made to help students to build intuition and avoid parrot-like learning. There is minimal inter-chapter dependency. Each chapter can be used as an independent crash course and the reader can start reading from any chapter — almost. This book is intended for upper level undergraduate students, graduate students and researchers in computer graphics, geometric modeling, computer vision, pattern recognition and machine learning. It can be used as a reference book, or a textbook for a selected topics course with the instructor's choice of any of the topics.

Introductory Techniques for 3-D Computer Vision


Author: Emanuele Trucco,Alessandro Verri
Publisher: N.A
ISBN: N.A
Category: Computers
Page: 343
View: 5343

Continue Reading →

This text provides readers with a starting point to understand and investigate the literature of computer vision, listing conferences, journals and Internet sites.

Concise Computer Vision

An Introduction into Theory and Algorithms
Author: Reinhard Klette
Publisher: Springer Science & Business Media
ISBN: 1447163206
Category: Computers
Page: 429
View: 4732

Continue Reading →

This textbook provides an accessible general introduction to the essential topics in computer vision. Classroom-tested programming exercises and review questions are also supplied at the end of each chapter. Features: provides an introduction to the basic notation and mathematical concepts for describing an image and the key concepts for mapping an image into an image; explains the topologic and geometric basics for analysing image regions and distributions of image values and discusses identifying patterns in an image; introduces optic flow for representing dense motion and various topics in sparse motion analysis; describes special approaches for image binarization and segmentation of still images or video frames; examines the basic components of a computer vision system; reviews different techniques for vision-based 3D shape reconstruction; includes a discussion of stereo matchers and the phase-congruency model for image features; presents an introduction into classification and learning.

Applications of Invariance in Computer Vision

Second Joint European - US Workshop, Ponta Delgada, Azores, Portugal, October 9 - 14, 1993. Proceedings
Author: Joseph L. Mundy,Andrew Zisserman,David Forsyth
Publisher: Springer Science & Business Media
ISBN: 9783540582403
Category: Computers
Page: 510
View: 1329

Continue Reading →

This book is the proceedings of the Second Joint European-US Workshop on Applications of Invariance to Computer Vision, held at Ponta Delgada, Azores, Portugal in October 1993. The book contains 25 carefully refereed papers by distinguished researchers. The papers cover all relevant foundational aspects of geometric and algebraic invariance as well as applications to computer vision, particularly to recovery and reconstruction, object recognition, scene analysis, robotic navigation, and statistical analysis. In total, the collection of papers, together with an introductory survey by the editors, impressively documents that geometry, in its different variants, is the most successful and ubiquitous tool in computer vision.

Introduction to Visual Computing

Core Concepts in Computer Vision, Graphics, and Image Processing
Author: Aditi Majumder,M. Gopi
Publisher: CRC Press
ISBN: 1482244926
Category: Computers
Page: 376
View: 6920

Continue Reading →

Introduction to Visual Computing: Core Concepts in Computer Vision, Graphics, and Image Processing covers the fundamental concepts of visual computing. Whereas past books have treated these concepts within the context of specific fields such as computer graphics, computer vision or image processing, this book offers a unified view of these core concepts, thereby providing a unified treatment of computational and mathematical methods for creating, capturing, analyzing and manipulating visual data (e.g. 2D images, 3D models). Fundamentals covered in the book include convolution, Fourier transform, filters, geometric transformations, epipolar geometry, 3D reconstruction, color and the image synthesis pipeline. The book is organized in four parts. The first part provides an exposure to different kinds of visual data (e.g. 2D images, videos and 3D geometry) and the core mathematical techniques that are required for their processing (e.g. interpolation and linear regression.) The second part of the book on Image Based Visual Computing deals with several fundamental techniques to process 2D images (e.g. convolution, spectral analysis and feature detection) and corresponds to the low level retinal image processing that happens in the eye in the human visual system pathway. The next part of the book on Geometric Visual Computing deals with the fundamental techniques used to combine the geometric information from multiple eyes creating a 3D interpretation of the object and world around us (e.g. transformations, projective and epipolar geometry, and 3D reconstruction). This corresponds to the higher level processing that happens in the brain combining information from both the eyes thereby helping us to navigate through the 3D world around us. The last two parts of the book cover Radiometric Visual Computing and Visual Content Synthesis. These parts focus on the fundamental techniques for processing information arising from the interaction of light with objects around us, as well as the fundamentals of creating virtual computer generated worlds that mimic all the processing presented in the prior sections. The book is written for a 16 week long semester course and can be used for both undergraduate and graduate teaching, as well as a reference for professionals.

Introduction to Autonomous Mobile Robots


Author: Roland Siegwart,Illah Reza Nourbakhsh,Davide Scaramuzza
Publisher: MIT Press
ISBN: 0262015358
Category: Computers
Page: 453
View: 2553

Continue Reading →

Machine generated contents note: |g 1. |t Introduction -- |g 1.1. |t Introduction -- |g 1.2. |t An Overview of the Book -- |g 2. |t Locomotion -- |g 2.1. |t Introduction -- |g 2.1.1. |t Key issues for locomotion -- |g 2.2. |t Legged Mobile Robots -- |g 2.2.1. |t Leg configurations and stability -- |g 2.2.2. |t Consideration of dynamics -- |g 2.2.3. |t Examples of legged robot locomotion -- |g 2.3. |t Wheeled Mobile Robots -- |g 2.3.1. |t Wheeled locomotion: The design space -- |g 2.3.2. |t Wheeled locomotion: Case studies -- |g 2.4. |t Aerial Mobile Robots -- |g 2.4.1. |t Introduction -- |g 2.4.2. |t Aircraft configurations -- |g 2.4.3. |t State of the art in autonomous VTOL -- |g 2.5. |t Problems -- |g 3. |t Mobile Robot Kinematics -- |g 3.1. |t Introduction -- |g 3.2. |t Kinematic Models and Constraints -- |g 3.2.1. |t Representing robot position -- |g 3.2.2. |t Forward kinematic models -- |g 3.2.3. |t Wheel kinematic constraints -- |g 3.2.4. |t Robot kinematic constraints -- |g 3.g 3.3. |t Mobile Robot Maneuverability -- |g 3.3.1. |t Degree of mobility -- |g 3.3.2. |t Degree of steerability -- |g 3.3.3. |t Robot maneuverability -- |g 3.4. |t Mobile Robot Workspace -- |g 3.4.1. |t Degrees of freedom -- |g 3.4.2. |t Holonomic robots -- |g 3.4.3. |t Path and trajectory considerations -- |g 3.5. |t Beyond Basic Kinematics -- |g 3.6. |t Motion Control (Kinematic Control) -- |g 3.6.1. |t Open loop control (trajectory-following) -- |g 3.6.2. |t Feedback control -- |g 3.7. |t Problems -- |g 4. |t Perception -- |g 4.1. |t Sensors for Mobile Robots -- |g 4.1.1. |t Sensor classification -- |g 4.1.2. |t Characterizing sensor performance -- |g 4.1.3. |t Representing uncertainty -- |g 4.1.4. |t Wheel/motor sensors -- |g 4.1.5. |t Heading sensors -- |g 4.1.6. |t Accelerometers -- |g 4.1.7. |t Inertial measurement unit (IMU) -- |g 4.1.8. |t Ground beacons -- |g 4.1.9. |t Active ranging -- |g 4.1.10. |t Motion/speed sensors -- |g 4.1.11. |t Vision sensors -- |g 4.2. |t Fundameng 4.2.5. |t Structure from stereo -- |g 4.2.6. |t Structure from motion -- |g 4.2.7. |t Motion and optical flow -- |g 4.2.8. |t Color tracking -- |g 4.3. |t Fundamentals of Image Processing -- |g 4.3.1. |t Image filtering -- |g 4.3.2. |t Edge detection -- |g 4.3.3. |t Computing image similarity -- |g 4.4. |t Feature Extraction -- |g 4.5. |t Image Feature Extraction: Interest Point Detectors -- |g 4.5.1. |t Introduction -- |g 4.5.2. |t Properties of the ideal feature detector -- |g 4.5.3. |t Corner detectors -- |g 4.5.4. |t Invariance to photometric and geometric changes -- |g 4.5.5. |t Blob detectors -- |g 4.6. |t Place Recognition -- |g 4.6.1. |t Introduction -- |g 4.6.2. |t From bag of features to visual words -- |g 4.6.3. |t Efficient location recognition by using an inverted file -- |g 4.6.4. |t Geometric verification for robust place recognition -- |g 4.6.5. |t Applications -- |g 4.6.6. |t Other image representations for place recognition -- |g 4.7. |t Feature Extraction Based ong 4.7.3. |t Range histogram features -- |g 4.7.4. |t Extracting other geometric features -- |g 4.8. |t Problems -- |g 5. |t Mobile Robot Localization -- |g 5.1. |t Introduction -- |g 5.2. |t The Challenge of Localization: Noise and Aliasing -- |g 5.2.1. |t Sensor noise -- |g 5.2.2. |t Sensor aliasing -- |g 5.2.3. |t Effector noise -- |g 5.2.4. |t An error model for odometric position estimation -- |g 5.3. |t To Localize or Not to Localize: Localization-Based Navigation Versus Programmed Solutions -- |g 5.4. |t Belief Representation -- |g 5.4.1. |t Single-hypothesis belief -- |g 5.4.2. |t Multiple-hypothesis belief -- |g 5.5. |t Map Representation -- |g 5.5.1. |t Continuous representations -- |g 5.5.2. |t Decomposition strategies -- |g 5.5.3. |t State of the art: Current challenges in map representation -- |g 5.6. |t Probabilistic Map-Based Localization -- |g 5.6.1. |t Introduction -- |g 5.6.2. |t The robot localization problem -- |g 5.6.3. |t Basic concepts of probability theory -- |gg 5.6.6. |t Classification of localization problems -- |g 5.6.7. |t Markov localization -- |g 5.6.8. |t Kalman filter localization -- |g 5.7. |t Other Examples of Localization Systems -- |g 5.7.1. |t Landmark-based navigation -- |g 5.7.2. |t Globally unique localization -- |g 5.7.3. |t Positioning beacon systems -- |g 5.7.4. |t Route-based localization -- |g 5.8. |t Autonomous Map Building -- |g 5.8.1. |t Introduction -- |g 5.8.2. |t SLAM: The simultaneous localization and mapping problem -- |g 5.8.3. |t Mathematical definition of SLAM -- |g 5.8.4. |t Extended Kalman Filter (EKF) SLAM -- |g 5.8.5. |t Visual SLAM with a single camera -- |g 5.8.6. |t Discussion on EKF SLAM -- |g 5.8.7. |t Graph-based SLAM -- |g 5.8.8. |t Particle filter SLAM -- |g 5.8.9. |t Open challenges in SLAM -- |g 5.8.10. |t Open source SLAM software and other resources -- |g 5.9. |t Problems -- |g 6. |t Planning and Navigation -- |g 6.1. |t Introduction -- |g 6.2. |t Competences for Navigation: Planning and Reactig 6.4. |t Obstacle avoidance -- |g 6.4.1. |t Bug algorithm -- |g 6.4.2. |t Vector field histogram -- |g 6.4.3. |t The bubble band technique -- |g 6.4.4. |t Curvature velocity techniques -- |g 6.4.5. |t Dynamic window approaches -- |g 6.4.6. |t The Schlegel approach to obstacle avoidance -- |g 6.4.7. |t Nearness diagram -- |g 6.4.8. |t Gradient method -- |g 6.4.9. |t Adding dynamic constraints -- |g 6.4.10. |t Other approaches -- |g 6.4.11. |t Overview -- |g 6.5. |t Navigation Architectures -- |g 6.5.1. |t Modularity for code reuse and sharing -- |g 6.5.2. |t Control localization -- |g 6.5.3. |t Techniques for decomposition -- |g 6.5.4. |t Case studies: tiered robot architectures -- |g 6.6. |t Problems -- |t Bibliography -- |t Books -- |t Papers -- |t Referenced Webpages.

Image Analysis and Recognition

11th International Conference, ICIAR 2014, Vilamoura, Portugal, October 22-24, 2014, Proceedings
Author: Aurélio Campilho,Mohamed Kamel
Publisher: Springer
ISBN: 3319117580
Category: Computers
Page: 518
View: 5396

Continue Reading →

The two volumes LNCS 8814 and 8815 constitute the thoroughly refereed proceedings of the 11th International Conference on Image Analysis and Recognition, ICIAR 2014, held in Vilamoura, Portugal, in October 2014. The 107 revised full papers presented were carefully reviewed and selected from 177 submissions. The papers are organized in the following topical sections: image representation and models; sparse representation; image restoration and enhancement; feature detection and image segmentation; classification and learning methods; document image analysis; image and video retrieval; remote sensing; applications; action, gestures and audio-visual recognition; biometrics; medical image processing and analysis; medical image segmentation; computer-aided diagnosis; retinal image analysis; 3D imaging; motion analysis and tracking; and robot vision.