Fr. 238.00

Digital Image Processing, Analysis and Computer Vision Using Nonlinear Partial Differential Equations

Inglese · Copertina rigida

Pubblicazione il 09.06.2025

Descrizione

Ulteriori informazioni

This book provides an overview of the applications of partial differential equations (PDEs) to image processing, analysis, and computer vision domains, focusing mainly on the most important contributions of the author in these closely related fields. It addresses almost all the PDE-based image processing and analysis areas, and the connections between partial differential equations, computer vision, and artificial intelligence: PDE-based image filtering, inpainting, compression, segmentation, content-based recognition, indexing and retrieval, and video object detection and tracking, energy-based (variational) and nonlinear diffusion-based models of second and fourth order, nonlinear PDE-based scale-spaces in combination to convolutional neural networks and high-level descriptors to perform edge and feature extraction.
 

Sommario

Introduction.- Nonlinear PDE-based Digital Image Restoration Techniques.- Nonlinear PDE-based Models for Inpainting and Compression.- Nonlinear Diffusion-based Multi-scale Image Analysis Methods.- Variational and PDE-based Static and Video Image Segmentation Approaches.- Nonlinear PDE-based Video Object Detection and Tracking.- Conclusions.

Riassunto

This book provides an overview of the applications of partial differential equations (PDEs) to image processing, analysis, and computer vision domains, focusing mainly on the most important contributions of the author in these closely related fields. It addresses almost all the PDE-based image processing and analysis areas, and the connections between partial differential equations, computer vision, and artificial intelligence: PDE-based image filtering, inpainting, compression, segmentation, content-based recognition, indexing and retrieval, and video object detection and tracking, energy-based (variational) and nonlinear diffusion-based models of second and fourth order, nonlinear PDE-based scale-spaces in combination to convolutional neural networks and high-level descriptors to perform edge and feature extraction.
 

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