Ismail Ben Ayed – författare

High-Order Models in Semantic Image Segmentation reviews recent developments in optimization-based methods for image segmentation, presenting several geometric and mathematical models that underlie a broad class of recent segmentation techniques. Focusing on impactful algorithms in the computer vision community in the last 10 years, the book includes sections on graph-theoretic and continuous relaxation techniques, which can compute globally optimal solutions for many problems. The book provides a practical and accessible introduction to these state-of -the-art segmentation techniques that is ideal for academics, industry researchers, and graduate students in computer vision, machine learning and medical imaging.

Gives an intuitive and conceptual understanding of this mathematically involved subject by using a large number of graphical illustrationsProvides the right amount of knowledge to apply sophisticated techniques for a wide range of new applicationsContains numerous tables that compare different algorithms, facilitating the appropriate choice of algorithm for the intended applicationPresents an array of practical applications in computer vision and medical imagingIncludes code for many of the algorithms that is available on the book's companion website

High-Order Models in Semantic Image Segmentation reviews recent developments in optimization-based methods for image segmentation, presenting several geometric and mathematical models that underlie a broad class of recent segmentation techniques. Focusing on impactful algorithms in the computer vision community in the last 10 years, the book includes sections on graph-theoretic and continuous relaxation techniques, which can compute globally optimal solutions for many problems. The book provides a practical and accessible introduction to these state-of -the-art segmentation techniques that is ideal for academics, industry researchers, and graduate students in computer vision, machine learning and medical imaging.- Gives an intuitive and conceptual understanding of this mathematically involved subject by using a large number of graphical illustrations- Provides the right amount of knowledge to apply sophisticated techniques for a wide range of new applications- Contains numerous tables that compare different algorithms, facilitating the appropriate choice of algorithm for the intended application- Presents an array of practical applications in computer vision and medical imaging- Includes code for many of the algorithms that is available on the book''s companion website

Less-Supervised Segmentation with CNNs: Scenarios, Models and Optimization reviews recent progress in deep learning for image segmentation under scenarios with limited supervision, with a focus on medical imaging. The book presents main approaches and state-of-the-art models and includes a broad array of applications in medical image segmentation, including healthcare, oncology, cardiology and neuroimaging. A key objective is to make this mathematical subject accessible to a broad engineering and computing audience by using a large number of intuitive graphical illustrations. The emphasis is on giving conceptual understanding of the methods to foster easier learning.

This book is highly suitable for researchers and graduate students in computer vision, machine learning and medical imaging.

Presents a good understanding of the different weak-supervision models (i.e., loss functions and priors) and the conceptual connections between them, providing an ability to choose the most appropriate model for a given application scenarioProvides knowledge of several possible optimization strategies for each of the examined losses, giving the ability to choose the most appropriate optimizer for a given problem or application scenarioOutlines the main strengths and weaknesses of state-of-the-art approachesGives the tools to understand and use publicly-available code, as well as customize it for specific objectives

Less-Supervised Segmentation with CNNs: Scenarios, Models and Optimization reviews recent progress in deep learning for image segmentation under scenarios with limited supervision, with a focus on medical imaging. The book presents main approaches and state-of-the-art models and includes a broad array of applications in medical image segmentation, including healthcare, oncology, cardiology and neuroimaging. A key objective is to make this mathematical subject accessible to a broad engineering and computing audience by using a large number of intuitive graphical illustrations. The emphasis is on giving conceptual understanding of the methods to foster easier learning. This book is highly suitable for researchers and graduate students in computer vision, machine learning and medical imaging. - Presents a good understanding of the different weak-supervision models (i.e., loss functions and priors) and the conceptual connections between them, providing an ability to choose the most appropriate model for a given application scenario- Provides knowledge of several possible optimization strategies for each of the examined losses, giving the ability to choose the most appropriate optimizer for a given problem or application scenario- Outlines the main strengths and weaknesses of state-of-the-art approaches- Gives the tools to understand and use publicly-available code, as well as customize it for specific objectives

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.

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.