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Author: Jean-Michel Morel
Publisher: Springer Science & Business Media
ISBN: 1468405675
Category : Mathematics
Languages : en
Pages : 257
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Book Description
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").
Author: Jean-Michel Morel
Publisher: Springer Science & Business Media
ISBN: 1468405675
Category : Mathematics
Languages : en
Pages : 257
Get Book Here
Book Description
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").
Author: Amar Mitiche
Publisher: Springer Science & Business Media
ISBN: 3642153526
Category : Technology & Engineering
Languages : en
Pages : 192
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Book Description
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.
Author: Ayman S. El-Baz
Publisher: Springer
ISBN: 9781441981950
Category : Medical
Languages : en
Pages : 410
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Book Description
With the advances in image guided surgery for cancer treatment, the role of image segmentation and registration has become very critical. The central engine of any image guided surgery product is its ability to quantify the organ or segment the organ whether it is a magnetic resonance imaging (MRI) and computed tomography (CT), X-ray, PET, SPECT, Ultrasound, and Molecular imaging modality. Sophisticated segmentation algorithms can help the physicians delineate better the anatomical structures present in the input images, enhance the accuracy of medical diagnosis and facilitate the best treatment planning system designs. The focus of this book in towards the state of the art techniques in the area of image segmentation and registration.
Author: Ouattara-S
Publisher: Omn.Univ.Europ.
ISBN: 9786131563171
Category : Literary Criticism
Languages : fr
Pages : 256
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Book Description
La segmentation est une étape primordiale en traitement d'images puisqu'elle conditionne la qualité de l'interprétation, puis la prise de décision. Beaucoup de méthodes existantes donnent de bons résultats mais supposent des a priori sur la stratégie de traitement ou la distribution des classes, et des connaissances sur le contenu de l'image. Au regard des progrès technologiques des capteurs et de la capacité des mémoires de stockage, les images multicomposantes sont de plus en plus plébiscitées par rapport aux images monocomposantes (scalaires ou en niveaux de gris) à cause de leur richesse à caractériser une scène donnée.Parmi les stratégies de segmentation à approche région, celles basées sur la classification par analyse d'histogrammes d'images multicomposantes présentent l'avantage de réaliser une segmentation sans connaissance a priori des images. L'objectif fixé dans le cadre de cette de travail est la mise en oeuvre d'une méthode de segmentation d'images multicomposantes par analyse d'histogrammes multidimensionnels à stratégie vectorielle et non paramétrique, en résolvant les problèmes liés à la manipulation des histogrammes nD et à leur aspect diffus.
Author: Jiamin Liu
Publisher:
ISBN: 9781109847802
Category :
Languages : en
Pages : 116
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Book Description
The focus of this thesis is practical model-based image segmentation. The target application in mind is segmentation and separation of the individual bony components at a joint for studying joint motion. This thesis examines this problem in three successive stages: (1) how best to combine model and image based strategies for 2D segmentation; (2) extending these to 3D segmentation; (3) utilizing these to segment (track) the same object in images corresponding to different positions of the joint.
Author: Patrick Lambert
Publisher:
ISBN:
Category :
Languages : fr
Pages : 103
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Book Description
Author: Petr Dokládal
Publisher:
ISBN: 9788021415980
Category :
Languages : en
Pages : 180
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Book Description
Author: GABRIELA CSURKA; RICCARDO VOLPI; BORIS CHIDLOVSKII.
Publisher:
ISBN: 9781638280774
Category : Electronic books
Languages : en
Pages : 0
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Book Description
Semantic image segmentation (SiS) plays a fundamental role towards a general understanding of the image content and context, in a broad variety of computer vision applications, thus providing key information for the global understanding of an image.This monograph summarizes two decades of research in the field of SiS, where a literature review of solutions starting from early historical methods is proposed, followed by an overview of more recent deep learning methods, including the latest trend of using transformers.The publication is complemented by presenting particular cases of the weak supervision and side machine learning techniques that can be used to improve the semantic segmentation, such as curriculum, incremental or self-supervised learning. State-of-the-art SiS models rely on a large amount of annotated samples, which are more expensive to obtain than labels for tasks such as image classification. Since unlabeled data is significantly cheaper to obtain, it is not surprising that Unsupervised Domain Adaptation (UDA) reached a broad success within the semantic segmentation community. Therefore, a second core contribution of this monograph is to summarize five years of a rapidly growing field, Domain Adaptation for Semantic Image Segmentation (DASiS), which embraces the importance of semantic segmentation itself and a critical need of adapting segmentation models to new environments. In addition to providing a comprehensive survey on DASiS techniques, newer trends such as multi-domain learning, domain generalization, domain incremental learning, test-time adaptation and source-free domain adaptation are also presented. The publication concludes by describing datasets and benchmarks most widely used in SiS and DASiS and briefly discusses related tasks such as instance and panoptic image segmentation, as well as applications such as medical image segmentation.This monograph should provide researchers across academia and industry with a comprehensive reference guide, and will help them in fostering new research directions in the field.
Author:
Publisher:
ISBN:
Category : Artificial intelligence
Languages : en
Pages : 406
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Book Description
Author: Bernadette Bouchon-Meunier
Publisher: Physica
ISBN: 3790818895
Category : Computers
Languages : en
Pages : 283
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Book Description
This book presents the main tools for aggregation of information given by several members of a group or expressed in multiple criteria, and for fusion of data provided by several sources. It focuses on the case where the availability knowledge is imperfect, which means that uncertainty and/or imprecision must be taken into account. The book contains both theoretical and applied studies of aggregation and fusion methods in the main frameworks: probability theory, evidence theory, fuzzy set and possibility theory. The latter is more developed because it allows to manage both imprecise and uncertain knowledge. Applications to decision-making, image processing, control and classification are described.
