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Etude Dune Methode De Segmentation Dimages Obtenues En Resonnance Magnetique En Vue Dune Quantification
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Author :International Livestock Research Institute Publisher :ILRI (aka ILCA and ILRAD) ISBN 13 :9789291460038 Total Pages :268 pages Book Rating :4.4/5 (6 download)
Book Synopsis Livestock Policy Analysis by : International Livestock Research Institute
Download or read book Livestock Policy Analysis written by International Livestock Research Institute and published by ILRI (aka ILCA and ILRAD). This book was released on 1995-01-01 with total page 268 pages. Available in PDF, EPUB and Kindle. Book excerpt: Policy concepts; Identification of policy issues; Production systems, supply and demand; Market, price and trade policies; Marketing and distribution systems; Budget and manpower planning; Land tenure police for the livestock sector; Policy analysis report writing and communication; Livestock production and marketing in alphabeta - a case study.
Book Synopsis Low Frequency Scattering by : George Dassios
Download or read book Low Frequency Scattering written by George Dassios and published by Oxford University Press. This book was released on 2000 with total page 322 pages. Available in PDF, EPUB and Kindle. Book excerpt: Scattering theory deals with the interactions of waves with obstacles in their path, and low frequency scattering occurs when the obstacles involved are very small. This book gives an overview of the subject for graduates and researchers, for the first time unifying the theories covering acoustic, electromagnetic and elastic waves.
Book Synopsis Eat Sleep Bagpipes Repeat by : Mirako Press
Download or read book Eat Sleep Bagpipes Repeat written by Mirako Press and published by Createspace Independent Publishing Platform. This book was released on 2018-07-18 with total page 104 pages. Available in PDF, EPUB and Kindle. Book excerpt: This adorable music notebook is perfect for staffs, kids and musicians. The high-quality manuscript book includes 110 pages of 12 staves. Let exercise your composing skills with this well-designed music sketchbook! Enjoy!
Book Synopsis Agricultural English by : Georgeta Raţă
Download or read book Agricultural English written by Georgeta Raţă and published by Cambridge Scholars Publishing. This book was released on 2012-04-25 with total page 385 pages. Available in PDF, EPUB and Kindle. Book excerpt: Agricultural English is a collection of essays on the English of Agriculture. The approach is a linguistic one: the different aspects of the English used in the field of agriculture (agricultural practices, agricultural systems) and in some fields related to agriculture (agricultural zoology, agri-tourism, biology, botany, ecology, entomology, gastronomy, land measurement, plant pathology, zoology) are analysed from a morphological (combination, derivation), syntactical (nominal phrases, verbal phrases), lexical and lexicographical, semantic (homonymy, semantic fields, synonymy, terminology), pragmatic (academic discourse, idiom, metaphor), etymological (etymon, Latin heritage), and contrastive (English–Croatian, English–French, English–German, English–Romanian, Romanian–English) points of view. The book will appeal to agriculturists, animal breeders, professors, researchers, students, and translators from Croatian-, English-, French-, German-, and Romanian-speaking countries, active in their own countries or abroad. The types of academic readership it would appeal to include academic teaching staff, researchers and students in the fields of agriculture and related fields – agricultural zoology, agri-tourism, biology, botany, ecology, entomology, gastronomy, land measurement, plant pathology, and zoology.
Book Synopsis Probabilistic Modeling for Segmentation in Magnetic Resonance Images of the Human Brain by : Michael Wels
Download or read book Probabilistic Modeling for Segmentation in Magnetic Resonance Images of the Human Brain written by Michael Wels and published by Logos Verlag Berlin GmbH. This book was released on 2010 with total page 147 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this book the fully automatic generation of semantic annotations for medical imaging data by means of medical image segmentation and labeling is addressed. In particular, the focus is on the segmentation of the human brain and related structures from magnetic resonance imaging (MRI) data. Three novel probabilistic methods from the field of database-guided knowledge-based medical image segmentation are presented. Each of the methods is applied to one of three MRI segmentation scenarios: 1) 3-D MRI brain tissue classification and intensity non-uniformity correction, 2) pediatric brain cancer segmentation in multi-spectral 3-D MRI, and 3) 3-D MRI anatomical brain structure segmentation. All the newly developed methods make use of domain knowledge encoded by probabilistic boosting-trees (PBT), which is a recent machine learning technique. For all the methods uniform probabilistic formalisms are presented that group the methods into the broader context of probabilistic modeling for the purpose of image segmentation. It is shown by comparison with other methods from the literature that in all the scenarios the newly developed algorithms in most cases give more accurate results and have a lower computational cost. Evaluation on publicly available benchmarking data sets ensures reliable comparability of the results to those of other current and future methods. One of the methods successfully participated in the ongoing online caudate segmentation challenge (www.cause07.org), where it ranks among the top five methods for this particular segmentation scenario.
Book Synopsis Imagerie de résonance magnétique by : Michel Décorps
Download or read book Imagerie de résonance magnétique written by Michel Décorps and published by EDP Sciences. This book was released on 2012-12-03T00:00:00+01:00 with total page 534 pages. Available in PDF, EPUB and Kindle. Book excerpt: L'imagerie par résonance magnétique s'est développée de manière prodigieuse au cours des quarante dernières années et le champ d'application des méthodes mises en œuvre, mais aussi leur complexité, s'accroissent de manière continue. Ce livre scientifique a pour objectif de décrire de manière rigoureuse les différentes méthodes de production d'une image par résonance magnétique. Les différentes étapes de production d'une image sont présentées : excitation spatialement sélective, codage de l'espace, traitement des données. Les multiples séquences d'impulsions constituant la panoplie des utilisateurs de l'IRM sont décrites de manière détaillée, depuis les séquences de base de type écho de gradient ou écho de spin, jusqu'aux séquences rapides exploitant l'établissement d'un état stationnaire, et aux balayages écho-planar ou spirale. La description couvre des aspects plus complexes, comme les techniques d'excitation spatiale multi-dimensionnelle ou l'imagerie parallèle.
Download or read book Image Segmentation written by Tao Lei and published by John Wiley & Sons. This book was released on 2022-09-26 with total page 340 pages. Available in PDF, EPUB and Kindle. Book excerpt: Image Segmentation Summarizes and improves new theory, methods, and applications of current image segmentation approaches, written by leaders in the field The process of image segmentation divides an image into different regions based on the characteristics of pixels, resulting in a simplified image that can be more efficiently analyzed. Image segmentation has wide applications in numerous fields ranging from industry detection and bio-medicine to intelligent transportation and architecture. Image Segmentation: Principles, Techniques, and Applications is an up-to-date collection of recent techniques and methods devoted to the field of computer vision. Covering fundamental concepts, new theories and approaches, and a variety of practical applications including medical imaging, remote sensing, fuzzy clustering, and watershed transform. In-depth chapters present innovative methods developed by the authors—such as convolutional neural networks, graph convolutional networks, deformable convolution, and model compression—to assist graduate students and researchers apply and improve image segmentation in their work. Describes basic principles of image segmentation and related mathematical methods such as clustering, neural networks, and mathematical morphology. Introduces new methods for achieving rapid and accurate image segmentation based on classic image processing and machine learning theory. Presents techniques for improved convolutional neural networks for scene segmentation, object recognition, and change detection, etc. Highlights the effect of image segmentation in various application scenarios such as traffic image analysis, medical image analysis, remote sensing applications, and material analysis, etc. Image Segmentation: Principles, Techniques, and Applications is an essential resource for undergraduate and graduate courses such as image and video processing, computer vision, and digital signal processing, as well as researchers working in computer vision and image analysis looking to improve their techniques and methods.
Book Synopsis Variational and Level Set Methods in Image Segmentation by : Amar Mitiche
Download or read book Variational and Level Set Methods in Image Segmentation written by Amar Mitiche and published by Springer Science & Business Media. This book was released on 2010-10-22 with total page 192 pages. Available in PDF, EPUB and Kindle. Book excerpt: Image segmentation consists of dividing an image domain into disjoint regions according to a characterization of the image within or in-between the regions. Therefore, segmenting an image is to divide its domain into relevant components. The efficient solution of the key problems in image segmentation promises to enable a rich array of useful applications. The current major application areas include robotics, medical image analysis, remote sensing, scene understanding, and image database retrieval. The subject of this book is image segmentation by variational methods with a focus on formulations which use closed regular plane curves to define the segmentation regions and on a level set implementation of the corresponding active curve evolution algorithms. Each method is developed from an objective functional which embeds constraints on both the image domain partition of the segmentation and the image data within or in-between the partition regions. The necessary conditions to optimize the objective functional are then derived and solved numerically. The book covers, within the active curve and level set formalism, the basic two-region segmentation methods, multiregion extensions, region merging, image modeling, and motion based segmentation. To treat various important classes of images, modeling investigates several parametric distributions such as the Gaussian, Gamma, Weibull, and Wishart. It also investigates non-parametric models. In motion segmentation, both optical flow and the movement of real three-dimensional objects are studied.
Book Synopsis Variational Methods in Image Segmentation by : Jean-Michel Morel
Download or read book Variational Methods in Image Segmentation written by Jean-Michel Morel and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 257 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book contains both a synthesis and mathematical analysis of a wide set of algorithms and theories whose aim is the automatic segmen tation of digital images as well as the understanding of visual perception. A common formalism for these theories and algorithms is obtained in a variational form. Thank to this formalization, mathematical questions about the soundness of algorithms can be raised and answered. Perception theory has to deal with the complex interaction between regions and "edges" (or boundaries) in an image: in the variational seg mentation energies, "edge" terms compete with "region" terms in a way which is supposed to impose regularity on both regions and boundaries. This fact was an experimental guess in perception phenomenology and computer vision until it was proposed as a mathematical conjecture by Mumford and Shah. The third part of the book presents a unified presentation of the evi dences in favour of the conjecture. It is proved that the competition of one-dimensional and two-dimensional energy terms in a variational for mulation cannot create fractal-like behaviour for the edges. The proof of regularity for the edges of a segmentation constantly involves con cepts from geometric measure theory, which proves to be central in im age processing theory. The second part of the book provides a fast and self-contained presentation of the classical theory of rectifiable sets (the "edges") and unrectifiable sets ("fractals").
Book Synopsis Statistical Models for Segmentation from MR Localizer Images by : Matthias Fenchel
Download or read book Statistical Models for Segmentation from MR Localizer Images written by Matthias Fenchel and published by Cuvillier Verlag. This book was released on 2010-08-12 with total page 130 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Image Co-segmentation by : Avik Hati
Download or read book Image Co-segmentation written by Avik Hati and published by Springer Nature. This book was released on 2023-02-02 with total page 231 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents and analyzes methods to perform image co-segmentation. In this book, the authors describe efficient solutions to this problem ensuring robustness and accuracy, and provide theoretical analysis for the same. Six different methods for image co-segmentation are presented. These methods use concepts from statistical mode detection, subgraph matching, latent class graph, region growing, graph CNN, conditional encoder–decoder network, meta-learning, conditional variational encoder–decoder, and attention mechanisms. The authors have included several block diagrams and illustrative examples for the ease of readers. This book is a highly useful resource to researchers and academicians not only in the specific area of image co-segmentation but also in related areas of image processing, graph neural networks, statistical learning, and few-shot learning.
Book Synopsis A Summary of Image Segmentation Techniques by : Lilly Spirkovska
Download or read book A Summary of Image Segmentation Techniques written by Lilly Spirkovska and published by . This book was released on 1993 with total page 18 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Pulse Coupled Neural Networks for the Segmentation of Magnetic Resonance Brain Images by : Shane Lee Abrahamson
Download or read book Pulse Coupled Neural Networks for the Segmentation of Magnetic Resonance Brain Images written by Shane Lee Abrahamson and published by . This book was released on 1996-12-01 with total page 94 pages. Available in PDF, EPUB and Kindle. Book excerpt: This research develops an automated method for segmenting Magnetic Resonance (MR) brain images based on Pulse Coupled Neural Networks (PCNN). MR brain image segmentation has proven difficult, primarily due to scanning artifacts such as interscan and intrascan intensity inhomogeneities. The method developed and presented here uses a PCNN to both filter and segment MR brain images. The technique begins by preprocessing images with a PCNN filter to reduce scanning artifacts. Images are then contrast enhanced via histogram equalization. Finally, a PCNN is used to segment the images to arrive at the final result. Modifications to the original PCNN model are made that drastically improve performance while greatly reducing memory requirements. These modifications make it possible to extend the method to filter and segment three dimensionally. Volumes represented as series of images are segmented using this new method. This new three dimensional segmentation technique can be used to obtain a better segmentation of a single image or of an entire volume. Results indicate that the PCNN shows promise as an image analysis tool.
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 Advances in Image Segmentation by : Pei-Gee Ho
Download or read book Advances in Image Segmentation written by Pei-Gee Ho and published by BoD – Books on Demand. This book was released on 2012-10-24 with total page 130 pages. Available in PDF, EPUB and Kindle. Book excerpt: The field of digital image segmentation is continually evolving. Most recently, the advanced segmentation methods such as Template Matching, Spatial and Temporal ARMA Processes, Mean Shift Iterative Algorithm, Constrained Compound Markov Random Field (CCMRF) model and Statistical Pattern Recognition (SPR) methods form the core of a modernization effort that resulted in the current text. This new edition of "Advanced Image Segmentation" is but a reflection of the significant progress that has been made in the field of image segmentation in just the past few years. The book presented chapters that highlight frontier works in image information processing.
Book Synopsis Image Segmentation and Compression Using Hidden Markov Models by : Jia Li
Download or read book Image Segmentation and Compression Using Hidden Markov Models written by Jia Li and published by Springer. This book was released on with total page 141 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the current age of information technology, the issues of distributing and utilizing images efficiently and effectively are of substantial concern. Solutions to many of the problems arising from these issues are provided by techniques of image processing, among which segmentation and compression are topics of this book. Image segmentation is a process for dividing an image into its constituent parts. For block-based segmentation using statistical classification, an image is divided into blocks and a feature vector is formed for each block by grouping statistics of its pixel intensities. Conventional block-based segmentation algorithms classify each block separately, assuming independence of feature vectors. Image Segmentation and Compression Using Hidden Markov Models presents a new algorithm that models the statistical dependence among image blocks by two dimensional hidden Markov models (HMMs). Formulas for estimating the model according to the maximum likelihood criterion are derived from the EM algorithm. To segment an image, optimal classes are searched jointly for all the blocks by the maximum a posteriori (MAP) rule. The 2-D HMM is extended to multiresolution so that more context information is exploited in classification and fast progressive segmentation schemes can be formed naturally. The second issue addressed in the book is the design of joint compression and classification systems using the 2-D HMM and vector quantization. A classifier designed with the side goal of good compression often outperforms one aimed solely at classification because overfitting to training data is suppressed by vector quantization. Image Segmentation and Compression Using Hidden Markov Models is an essential reference source for researchers and engineers working in statistical signal processing or image processing, especially those who are interested in hidden Markov models. It is also of value to those working on statistical modeling.
Book Synopsis Genetic Learning for Adaptive Image Segmentation by : Bir Bhanu
Download or read book Genetic Learning for Adaptive Image Segmentation written by Bir Bhanu and published by Springer Science & Business Media. This book was released on 1994-09-30 with total page 310 pages. Available in PDF, EPUB and Kindle. Book excerpt: Image segmentation is generally the first task in any automated image understanding application, such as autonomous vehicle navigation, object recognition, photointerpretation, etc. All subsequent tasks, such as feature extraction, object detection, and object recognition, rely heavily on the quality of segmentation. One of the fundamental weaknesses of current image segmentation algorithms is their inability to adapt the segmentation process as real-world changes are reflected in the image. Only after numerous modifications to an algorithm's control parameters can any current image segmentation technique be used to handle the diversity of images encountered in real-world applications. Genetic Learning for Adaptive Image Segmentation presents the first closed-loop image segmentation system that incorporates genetic and other algorithms to adapt the segmentation process to changes in image characteristics caused by variable environmental conditions, such as time of day, time of year, weather, etc. Image segmentation performance is evaluated using multiple measures of segmentation quality. These quality measures include global characteristics of the entire image as well as local features of individual object regions in the image. This adaptive image segmentation system provides continuous adaptation to normal environmental variations, exhibits learning capabilities, and provides robust performance when interacting with a dynamic environment. This research is directed towards adapting the performance of a well known existing segmentation algorithm (Phoenix) across a wide variety of environmental conditions which cause changes in the image characteristics. The book presents a large number of experimental results and compares performance with standard techniques used in computer vision for both consistency and quality of segmentation results. These results demonstrate, (a) the ability to adapt the segmentation performance in both indoor and outdoor color imagery, and (b) that learning from experience can be used to improve the segmentation performance over time.
