Image, Video and 3D Data Registration
Image, Video and 3D Data Registration

Author: Vasileios Argyriou

Publisher: John Wiley & Sons

Published: 2015-07-01

Total Pages: 247

ISBN-13: 1118702433

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Data registration refers to a series of techniques for matching or bringing similar objects or datasets together into alignment. These techniques enjoy widespread use in a diverse variety of applications, such as video coding, tracking, object and face detection and recognition, surveillance and satellite imaging, medical image analysis and structure from motion. Registration methods are as numerous as their manifold uses, from pixel level and block or feature based methods to Fourier domain methods. This book is focused on providing algorithms and image and video techniques for registration and quality performance metrics. The authors provide various assessment metrics for measuring registration quality alongside analyses of registration techniques, introducing and explaining both familiar and state-of-the-art registration methodologies used in a variety of targeted applications. Key features: Provides a state-of-the-art review of image and video registration techniques, allowing readers to develop an understanding of how well the techniques perform by using specific quality assessment criteria Addresses a range of applications from familiar image and video processing domains to satellite and medical imaging among others, enabling readers to discover novel methodologies with utility in their own research Discusses quality evaluation metrics for each application domain with an interdisciplinary approach from different research perspectives

Image, Video and 3D Data Registration
Image, Video and 3D Data Registration

Author: Vasileios Argyriou

Publisher: John Wiley & Sons

Published: 2015-07-01

Total Pages: 248

ISBN-13: 1118702441

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Data registration refers to a series of techniques for matching or bringing similar objects or datasets together into alignment. These techniques enjoy widespread use in a diverse variety of applications, such as video coding, tracking, object and face detection and recognition, surveillance and satellite imaging, medical image analysis and structure from motion. Registration methods are as numerous as their manifold uses, from pixel level and block or feature based methods to Fourier domain methods. This book is focused on providing algorithms and image and video techniques for registration and quality performance metrics. The authors provide various assessment metrics for measuring registration quality alongside analyses of registration techniques, introducing and explaining both familiar and state-of-the-art registration methodologies used in a variety of targeted applications. Key features: Provides a state-of-the-art review of image and video registration techniques, allowing readers to develop an understanding of how well the techniques perform by using specific quality assessment criteria Addresses a range of applications from familiar image and video processing domains to satellite and medical imaging among others, enabling readers to discover novel methodologies with utility in their own research Discusses quality evaluation metrics for each application domain with an interdisciplinary approach from different research perspectives

Registration and Recognition in Images and Videos
Registration and Recognition in Images and Videos

Author: Roberto Cipolla

Publisher: Springer

Published: 2013-11-19

Total Pages: 292

ISBN-13: 3642449077

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Computer vision is the science and technology of making machines that see. It is concerned with the theory, design and implementation of algorithms that can automatically process visual data to recognize objects, track and recover their shape and spatial layout. The International Computer Vision Summer School - ICVSS was established in 2007 to provide both an objective and clear overview and an in-depth analysis of the state-of-the-art research in Computer Vision. The courses are delivered by world renowned experts in the field, from both academia and industry and cover both theoretical and practical aspects of real Computer Vision problems. The school is organized every year by University of Cambridge (Computer Vision and Robotics Group) and University of Catania (Image Processing Lab). Different topics are covered each year. This edited volume contains a selection of articles covering some of the talks and tutorials held during the last editions of the school. The chapters provide an in-depth overview of challenging areas with key references to the existing literature.

Video Registration
Video Registration

Author: Mubarak Shah

Publisher: Springer Science & Business Media

Published: 2013-11-11

Total Pages: 270

ISBN-13: 1461504597

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Traditionally, scientific fields have defined boundaries, and scientists work on research problems within those boundaries. However, from time to time those boundaries get shifted or blurred to evolve new fields. For instance, the original goal of computer vision was to understand a single image of a scene, by identifying objects, their structure, and spatial arrangements. This has been referred to as image understanding. Recently, computer vision has gradually been making the transition away from understanding single images to analyz ing image sequences, or video understanding. Video understanding deals with understanding of video sequences, e. g. , recognition of gestures, activities, fa cial expressions, etc. The main shift in the classic paradigm has been from the recognition of static objects in the scene to motion-based recognition of actions and events. Video understanding has overlapping research problems with other fields, therefore blurring the fixed boundaries. Computer graphics, image processing, and video databases have obvious overlap with computer vision. The main goal of computer graphics is to gener ate and animate realistic looking images, and videos. Researchers in computer graphics are increasingly employing techniques from computer vision to gener ate the synthetic imagery. A good example of this is image-based rendering and modeling techniques, in which geometry, appearance, and lighting is de rived from real images using computer vision techniques. Here the shift is from synthesis to analysis followed by synthesis.

3D Video and Its Applications
3D Video and Its Applications

Author: Takashi Matsuyama

Publisher: Springer Science & Business Media

Published: 2012-05-31

Total Pages: 353

ISBN-13: 1447141202

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This book presents a broad review of state-of-the-art 3D video production technologies and applications. The text opens with a concise introduction to the field, before examining the design and calibration methods for multi-view camera systems, including practical implementation technologies. A range of algorithms are then described for producing 3D video from video data. A selection of 3D video applications are also demonstrated. Features: describes real-time synchronized multi-view video capture, and object tracking with a group of active cameras; discusses geometric and photometric camera calibration, and 3D video studio design with active cameras; examines 3D shape and motion reconstruction, texture mapping and image rendering, and lighting environment estimation; demonstrates attractive 3D visualization, visual contents analysis and editing, 3D body action analysis, and data compression; highlights the remaining challenges and the exciting avenues for future research in 3D video technology.

Developments in Medical Image Processing and Computational Vision
Developments in Medical Image Processing and Computational Vision

Author: João Manuel R. S. Tavares

Publisher: Springer

Published: 2015-04-07

Total Pages: 400

ISBN-13: 3319134078

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This book presents novel and advanced topics in Medical Image Processing and Computational Vision in order to solidify knowledge in the related fields and define their key stakeholders. It contains extended versions of selected papers presented in VipIMAGE 2013 – IV International ECCOMAS Thematic Conference on Computational Vision and Medical Image, which took place in Funchal, Madeira, Portugal, 14-16 October 2013. The twenty-two chapters were written by invited experts of international recognition and address important issues in medical image processing and computational vision, including: 3D vision, 3D visualization, colour quantisation, continuum mechanics, data fusion, data mining, face recognition, GPU parallelisation, image acquisition and reconstruction, image and video analysis, image clustering, image registration, image restoring, image segmentation, machine learning, modelling and simulation, object detection, object recognition, object tracking, optical flow, pattern recognition, pose estimation, and texture analysis. Different applications are addressed and described throughout the book, comprising: biomechanical studies, bio-structure modelling and simulation, bone characterization, cell tracking, computer-aided diagnosis, dental imaging, face recognition, hand gestures detection and recognition, human motion analysis, human-computer interaction, image and video understanding, image processing, image segmentation, object and scene reconstruction, object recognition and tracking, remote robot control, and surgery planning. This volume is of use to researchers, students, practitioners and manufacturers from several multidisciplinary fields, such as artificial intelligence, bioengineering, biology, biomechanics, computational mechanics, computational vision, computer graphics, computer science, computer vision, human motion, imagiology, machine learning, machine vision, mathematics, medical image, medicine, pattern recognition, and physics.

A Generalized Multi-sensor 3D Image Registration and Data Fusion Method Using a Multi-resolution Approach
A Generalized Multi-sensor 3D Image Registration and Data Fusion Method Using a Multi-resolution Approach

Author: Carlos Bejar Colonia

Publisher:

Published: 2007

Total Pages: 154

ISBN-13: 9781109978827

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Recently surveillance and Automatic Target Recognition (ATR) applications have been increasing as the cost of computing power, needed to process the massive amount of information, keeps falling. Designing and implementing state-of-the-art electro-optical imaging systems to provide advanced surveillance capabilities involves integration of several technologies (i.e. precise optics, cameras, and image-computer vision algorithms for data fusion) into a programmable system. Multi-sensor fusion and integration refers to the combination of data collected from multiple sensors to provide more reliable and accurate information. Registration is the fundamental and complex process of aligning the collected data before the fusion. Several techniques for image registration have been proposed in the literature, but with limited success. In particular, one of the major limitations of existing methods is their lack of accuracy and efficiency. In addition many of these methods suffer from being applications specific. To the best of our knowledge there is no known accurate method in the literature that (a) can work under any scene circumstances/conditions and that (b) can be generalized and extended from a 2Dimensional to a 3-Dimensional space. In this research an efficient and accurate automated image registration with applications to Multi-sensor 3D LADAR imaging is presented. As we show here, the proposed approach is two-fold. First, comparison and matching of scene image/volume small patches of two overlapping 2-D or 3-D data is performed. We show here how the size of the patches is optimally derived. Second, 2D and 3-D Wavelet transforms are applied to these resulting small similar scene patches to extract a number of matching feature points. We show that the advantages of the proposed technique includes its computational efficiency, in comparison to existing methods, and its accuracy in detecting the necessary matching points, which both constitute the most fundamental/crucial but also challenging components of any data fusion/registration system. Finally, demonstration of the theories, analyses, proof of correctness behind the proposed techniques, implementation, and experimental results are presented to show the power and potential of the proposed generalized method that is extendable from 2D to 3D.

Multi-modal Image Registration with Unsupervised Deep Learning
Multi-modal Image Registration with Unsupervised Deep Learning

Author: Courtney K. Guo

Publisher:

Published: 2019

Total Pages: 40

ISBN-13:

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In this thesis, we tackle learning-based multi-modal image registration. Multi-modal registration, in which two images of dierent modalities need to be aligned to each other, is a difficult yet essential task for medical imaging analysis. Classical methods have been developed for single-modal and multi-modal registration, but are slow because they solve an optimization problem for each pair of images. Recently, deep learning methods for registration have been proposed, and have been shown to shorten registration time by learning a global function to perform registration, which can then be applied quickly on a pair of test images. These methods perform well for single-modal registration but have not yet been extended to the harder task of multi-modal registration. We bridge this gap by implementing classical multi-modal metrics in a differentiable and efficient manner to enable deep image registration for multi-modal data. We nd that our method for multi-modal registration performs significantly better than baselines, in terms of both accuracy and runtime.