Read Books Online and Download eBooks, EPub, PDF, Mobi, Kindle, Text Full Free.
Landmark Based Image Analysis
Download Landmark Based Image Analysis full books in PDF, epub, and Kindle. Read online Landmark Based Image Analysis ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!
Book Synopsis Landmark-Based Image Analysis by : Karl Rohr
Download or read book Landmark-Based Image Analysis written by Karl Rohr and published by Springer Science & Business Media. This book was released on 2013-03-14 with total page 314 pages. Available in PDF, EPUB and Kindle. Book excerpt: Landmarks are preferred image features for a variety of computer vision tasks such as image mensuration, registration, camera calibration, motion analysis, 3D scene reconstruction, and object recognition. Main advantages of using landmarks are robustness w. r. t. lightning conditions and other radiometric vari ations as well as the ability to cope with large displacements in registration or motion analysis tasks. Also, landmark-based approaches are in general com putationally efficient, particularly when using point landmarks. Note, that the term landmark comprises both artificial and natural landmarks. Examples are comers or other characteristic points in video images, ground control points in aerial images, anatomical landmarks in medical images, prominent facial points used for biometric verification, markers at human joints used for motion capture in virtual reality applications, or in- and outdoor landmarks used for autonomous navigation of robots. This book covers the extraction oflandmarks from images as well as the use of these features for elastic image registration. Our emphasis is onmodel-based approaches, i. e. on the use of explicitly represented knowledge in image analy sis. We principally distinguish between geometric models describing the shape of objects (typically their contours) and intensity models, which directly repre sent the image intensities, i. e. ,the appearance of objects. Based on these classes of models we develop algorithms and methods for analyzing multimodality im ages such as traditional 20 video images or 3D medical tomographic images.
Book Synopsis Recent Trends in Image Processing and Pattern Recognition by : K. C. Santosh
Download or read book Recent Trends in Image Processing and Pattern Recognition written by K. C. Santosh and published by Springer. This book was released on 2019-07-15 with total page 543 pages. Available in PDF, EPUB and Kindle. Book excerpt: This three-book set constitutes the refereed proceedings of the Second International Conference on Recent Trends in Image Processing and Pattern Recognition (RTIP2R) 2018, held in Solapur, India, in December 2018. The 173 revised full papers presented were carefully reviewed and selected from 374 submissions. The papers are organized in topical sections in the tree volumes. Part I: computer vision and pattern recognition; machine learning and applications; and image processing. Part II: healthcare and medical imaging; biometrics and applications. Part III: document image analysis; image analysis in agriculture; and data mining, information retrieval and applications.
Book Synopsis Advanced Machine Vision Paradigms for Medical Image Analysis by : Tapan K. Gandhi
Download or read book Advanced Machine Vision Paradigms for Medical Image Analysis written by Tapan K. Gandhi and published by Academic Press. This book was released on 2020-08-11 with total page 308 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computer vision and machine intelligence paradigms are prominent in the domain of medical image applications, including computer assisted diagnosis, image guided radiation therapy, landmark detection, imaging genomics, and brain connectomics. Medical image analysis and understanding are daunting tasks owing to the massive influx of multi-modal medical image data generated during routine clinal practice. Advanced computer vision and machine intelligence approaches have been employed in recent years in the field of image processing and computer vision. However, due to the unstructured nature of medical imaging data and the volume of data produced during routine clinical processes, the applicability of these meta-heuristic algorithms remains to be investigated. Advanced Machine Vision Paradigms for Medical Image Analysis presents an overview of how medical imaging data can be analyzed to provide better diagnosis and treatment of disease. Computer vision techniques can explore texture, shape, contour and prior knowledge along with contextual information, from image sequence and 3D/4D information which helps with better human understanding. Many powerful tools have been developed through image segmentation, machine learning, pattern classification, tracking, and reconstruction to surface much needed quantitative information not easily available through the analysis of trained human specialists. The aim of the book is for medical imaging professionals to acquire and interpret the data, and for computer vision professionals to learn how to provide enhanced medical information by using computer vision techniques. The ultimate objective is to benefit patients without adding to already high healthcare costs. Explores major emerging trends in technology which are supporting the current advancement of medical image analysis with the help of computational intelligence Highlights the advancement of conventional approaches in the field of medical image processing Investigates novel techniques and reviews the state-of-the-art in the areas of machine learning, computer vision, soft computing techniques, as well as their applications in medical image analysis
Book Synopsis Anatomical Landmark Detection Leveraging Implicit and Explicit Information Sharing Techniques by : Alexander Blair Powers
Download or read book Anatomical Landmark Detection Leveraging Implicit and Explicit Information Sharing Techniques written by Alexander Blair Powers and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Anatomical landmark detection is an essential step in various medical imaging processes, including morphological analysis, inter-/intra-subject registration, and, fundamentally, anatomy orientation. Deep reinforcement learning (DRL) has shown promise in replacing heuristic methods and classical image processing approaches to landmark detection. In this work, we propose multiple extensions of a multi-agent deep q-network approach to anatomical landmark detection. We first improve the localization of high confidence primary landmarks by searching in the physical space coordinate system of the image rather than voxel space. Second, when detecting a large number of landmarks, we decompose the detection process into two stages to compensate for the memory limitations induced by detecting a large number of landmarks.
Book Synopsis Computational Vision and Bio-Inspired Computing by : S. Smys
Download or read book Computational Vision and Bio-Inspired Computing written by S. Smys and published by Springer Nature. This book was released on 2022-03-30 with total page 877 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book includes selected papers from the 5th International Conference on Computational Vision and Bio Inspired Computing (ICCVBIC 2021), held in Coimbatore, India, during November 25–26, 2021. This book presents state-of-the-art research innovations in computational vision and bio-inspired techniques. The book reveals the theoretical and practical aspects of bio-inspired computing techniques, like machine learning, sensor-based models, evolutionary optimization and big data modeling and management that make use of effectual computing processes in the bio-inspired systems. It also contributes to the novel research that focuses on developing bio-inspired computing solutions for various domains, such as human–computer interaction, image processing, sensor-based single processing, recommender systems and facial recognition, which play an indispensable part in smart agriculture, smart city, biomedical and business intelligence applications.
Book Synopsis The Image of the City by : Kevin Lynch
Download or read book The Image of the City written by Kevin Lynch and published by MIT Press. This book was released on 1964-06-15 with total page 212 pages. Available in PDF, EPUB and Kindle. Book excerpt: The classic work on the evaluation of city form. What does the city's form actually mean to the people who live there? What can the city planner do to make the city's image more vivid and memorable to the city dweller? To answer these questions, Mr. Lynch, supported by studies of Los Angeles, Boston, and Jersey City, formulates a new criterion—imageability—and shows its potential value as a guide for the building and rebuilding of cities. The wide scope of this study leads to an original and vital method for the evaluation of city form. The architect, the planner, and certainly the city dweller will all want to read this book.
Book Synopsis New Approaches in Medical Image Analysis by : Binh Pham
Download or read book New Approaches in Medical Image Analysis written by Binh Pham and published by . This book was released on 1999 with total page 234 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Image Processing and Analysis by : Richard Baldock
Download or read book Image Processing and Analysis written by Richard Baldock and published by OUP Oxford. This book was released on 1999-12-09 with total page 322 pages. Available in PDF, EPUB and Kindle. Book excerpt: A wide range of books on image processing and analysis provide comprehensive descriptions of mathematics and algorithms for image processing practitioners, or introductory material for engineering students. This volume is different in addressing the topic from the point of view of the "user". Standard algorithms, procedures and rules of thumb are explained in the context of successful application to biological or medical images. Early chapters cover the basic topics of image acquisition, processing, analysis and pattern recognition. Much of the explanation is in the form of protocols, which should equip the user in the biological or earth sciences with the background for informed use of image processing software, and sufficient knowledge to write their own programmes if they feel moved to do so. More advanced techniques in the use of explicit models and analysis of 3D images are covered in later chapters, also with reference to specific applications. The coverage of these is not exhaustive, but may inspire the reader to consider applying image analysis to problems beyond those tackled by commercial packages.
Book Synopsis Evaluation of Image-based Landmark Recognition Techniques by : Yutaka Takeuchi
Download or read book Evaluation of Image-based Landmark Recognition Techniques written by Yutaka Takeuchi and published by . This book was released on 1998 with total page 16 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: "Recognizing landmarks in sequences of images is a challenging problem for a number of reasons. First of all, the appearance of any given landmark varies substantially from one observation to the next. In addition to variations due to different aspects, an illumination change, external clutter, and changing geometry of the imaging devices are other factors affecting the variability of the observed landmarks. Finally, it is typically difficult to make use of accurate 3D information in landmark recognition applications. For those reasons, it is not possible to use many of the object recognition techniques based on strong geometric models. The alternative is to use image-based techniques in which landmarks are represented by collections of images which capture the 'typical' appearance of the object. The information most relevant to recognition is extracted from the collection of raw images and used as the model for recognition. This process is often referred to as 'visual learning.' Models of landmarks are acquired from image sequences and later recognized for vehicle localization in urban environments. In the acquisition phase, a vehicle drives and collects images of an unknown area. The algorithm organizes these images into groups with similar image features. The feature distribution for each group describes a landmark. In the recognition phase, while navigating through the same general area, the vehicle collects new images. The algorithm classifies these images into one of the learned groups, thus recognizing a landmark. Unlike computationally intensive model-based approaches that build models from known objects observed in isolation, our image-based approach automatically learns the most salient landmarks in complex environments. It delivers a robust performance under a wide range of lighting and imaging angle variations."
Book Synopsis Handbook of Biomedical Image Analysis by : David Wilson
Download or read book Handbook of Biomedical Image Analysis written by David Wilson and published by Springer Science & Business Media. This book was released on 2007-04-25 with total page 583 pages. Available in PDF, EPUB and Kindle. Book excerpt: Our goal is to develop automated methods for the segmentation of thr- dimensional biomedical images. Here, we describe the segmentation of c- focal microscopy images of bee brains (20 individuals) by registration to one or several atlas images. Registration is performed by a highly parallel imp- mentation of an entropy-based nonrigid registration algorithm using B-spline transformations. We present and evaluate different methods to solve the cor- spondence problem in atlas based registration. An image can be segmented by registering it to an individual atlas, an average atlas, or multiple atlases. When registering to multiple atlases, combining the individual segmentations into a ?nalsegmentationcanbeachievedbyatlasselection,ormulticlassi?erdecision fusion. Wedescribeallthesemethodsandevaluatethesegmentationaccuracies that they achieve by performing experiments with electronic phantoms as well as by comparing their outputs to a manual gold standard. The present work is focused on the mathematical and computational t- ory behind a technique for deformable image registration termed Hyperelastic Warping, and demonstration of the technique via applications in image regist- tion and strain measurement. The approach combines well-established prin- ples of nonlinear continuum mechanics with forces derived directly from thr- dimensional image data to achieve registration. The general approach does not require the de?nition of landmarks, ?ducials, or surfaces, although it can - commodate these if available. Representative problems demonstrate the robust and ?exible nature of the approach. Three-dimensional registration methods are introduced for registering MRI volumes of the pelvis and prostate. The chapter ?rst reviews the applications, xi xii Preface challenges, and previous methods of image registration in the prostate.
Book Synopsis Proceedings of the IEEE Workshop on Mathematical Methods in Biomedical Image Analysis by :
Download or read book Proceedings of the IEEE Workshop on Mathematical Methods in Biomedical Image Analysis written by and published by . This book was released on 2001 with total page 260 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Proceedings : IEEE Workshop on Mathematical Methods in Biomedical Image Analysis by :
Download or read book Proceedings : IEEE Workshop on Mathematical Methods in Biomedical Image Analysis written by and published by IEEE Computer Society Press. This book was released on 2000 with total page 258 pages. Available in PDF, EPUB and Kindle. Book excerpt: Twenty-nine contributions are organized into segments addressing segmentation; deformable models; registration; flow and motion; and shape. Topics addressed include watersheds on the cortical surface for automated sulcal segmentation; needle placement under X-ray fluoroscopy using perspective invari"
Book Synopsis Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis by : Carole H. Sudre
Download or read book Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis written by Carole H. Sudre and published by Springer Nature. This book was released on 2020-10-05 with total page 233 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the Second International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2020, and the Third International Workshop on Graphs in Biomedical Image Analysis, GRAIL 2020, held in conjunction with MICCAI 2020, in Lima, Peru, in October 2020. The workshops were held virtually due to the COVID-19 pandemic. For UNSURE 2020, 10 papers from 18 submissions were accepted for publication. They focus on developing awareness and encouraging research in the field of uncertainty modelling to enable safe implementation of machine learning tools in the clinical world. GRAIL 2020 accepted 10 papers from the 12 submissions received. The workshop aims to bring together scientists that use and develop graph-based models for the analysis of biomedical images and to encourage the exploration of graph-based models for difficult clinical problems within a variety of biomedical imaging contexts.
Book Synopsis Multispectral Image Analysis Using the Object-Oriented Paradigm by : Kumar Navulur
Download or read book Multispectral Image Analysis Using the Object-Oriented Paradigm written by Kumar Navulur and published by CRC Press. This book was released on 2006-12-05 with total page 206 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bringing a fresh new perspective to remote sensing, object-based image analysis is a paradigm shift from the traditional pixel-based approach. Featuring various practical examples to provide understanding of this new modus operandi, Multispectral Image Analysis Using the Object-Oriented Paradigm reviews the current image analysis methods and demonstrates advantages to improve information extraction from imagery. This reference describes traditional image analysis techniques, introduces object-oriented technology, and discusses the benefits of object-based versus pixel-based classification. It examines the creation of object primitives using image segmentation approaches and the use of various techniques for object classification. The author covers image enhancement methods, how to use ancillary data to constrain image segmentation, and concepts of semantic grouping of objects. He concludes by addressing accuracy assessment approaches. The accompanying downloadable resources present sample data that enable the use of different approaches to problem solving. Integrating remote sensing techniques and GIS analysis, Multispectral Image Analysis Using the Object-Oriented Paradigm distills new tools to extract information from remotely sensed data.
Book Synopsis Image Processing by : Maria M. P. Petrou
Download or read book Image Processing written by Maria M. P. Petrou and published by John Wiley & Sons. This book was released on 2021-03-22 with total page 816 pages. Available in PDF, EPUB and Kindle. Book excerpt: The classic text that covers practical image processing methods and theory for image texture analysis, updated second edition The revised second edition of Image Processing: Dealing with Textures updates the classic work on texture analysis theory and methods without abandoning the foundational essentials of this landmark work. Like the first, the new edition offers an analysis of texture in digital images that are essential to a diverse range of applications such as: robotics, defense, medicine and the geo-sciences. Designed to easily locate information on specific problems, the text is structured around a series of helpful questions and answers. Updated to include the most recent developments in the field, many chapters have been completely revised including: Fractals and Multifractals, Image Statistics, Texture Repair, Local Phase Features, Dual Tree Complex Wavelet Transform, Ridgelets and Curvelets and Deep Texture Features. The book takes a two-level mathematical approach: light math is covered in the main level of the book, with harder math identified in separate boxes. This important text: Contains an update of the classic advanced text that reviews practical image processing methods and theory for image texture analysis Puts the focus exclusively on an in-depth exploration of texture Contains a companion website with exercises and algorithms Includes examples that are fully worked to enhance the learning experience Written for students and researchers of image processing, the second edition of Image Processing has been revised and updated to incorporate the foundational information on the topic and information on the latest advances.
Book Synopsis Theory & Applications of Image Analysis by : P. Johansen
Download or read book Theory & Applications of Image Analysis written by P. Johansen and published by World Scientific. This book was released on 1992 with total page 368 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book contains 31 papers carefully selected from among those presented at the 7th Scandinavian Conference on Image Analysis. The authors have extended their papers to give a more in-depth discussion of the theory, or of the experimental validation of the method they have proposed. The topics covered are current and wide-ranging and include both 2D- and 3D-vision, and low to high level vision.
Book Synopsis Face Centered Image Analysis Using Saliency and Deep Learning Based Techniques by : Rui Guo
Download or read book Face Centered Image Analysis Using Saliency and Deep Learning Based Techniques written by Rui Guo and published by . This book was released on 2016 with total page 138 pages. Available in PDF, EPUB and Kindle. Book excerpt: Image analysis starts with the purpose of configuring vision machines that can perceive like human to intelligently infer general principles and sense the surrounding situations from imagery. This dissertation studies the face centered image analysis as the core problem in high level computer vision research and addresses the problem by tackling three challenging subjects: Are there anything interesting in the image? If there is, what is/are that/they? If there is a person presenting, who is he/she? What kind of expression he/she is performing? Can we know his/her age? Answering these problems results in the saliency-based object detection, deep learning structured objects categorization and recognition, human facial landmark detection and multitask biometrics. To implement object detection, a three-level saliency detection based on the self-similarity technique (SMAP) is firstly proposed in the work. The first level of SMAP accommodates statistical methods to generate proto-background patches, followed by the second level that implements local contrast computation based on image self-similarity characteristics. At last, the spatial color distribution constraint is considered to realize the saliency detection. The outcome of the algorithm is a full resolution image with highlighted saliency objects and well-defined edges. In object recognition, the Adaptive Deconvolution Network (ADN) is implemented to categorize the objects extracted from saliency detection. To improve the system performance, L1=2 norm regularized ADN has been proposed and tested in different applications. The results demonstrate the efficiency and significance of the new structure. To fully understand the facial biometrics related activity contained in the image, the low rank matrix decomposition is introduced to help locate the landmark points on the face images. The natural extension of this work is beneficial in human facial expression recognition and facial feature parsing research. To facilitate the understanding of the detected facial image, the automatic facial image analysis becomes essential. We present a novel deeply learnt tree-structured face representation to uniformly model the human face with different semantic meanings. We show that the proposed feature yields unified representation in multi-task facial biometrics and the multi-task learning framework is applicable to many other computer vision tasks.
