Probabilistic graphical models for computer vision
type: Item
instance of: scholarly article
title: Probabilistic graphical models for computer vision
zbMATH Open document ID: 1475.68002
DOI: 10.1016/C2014-0-04359-6
author: Qiang Ji
published in: Computer Vision and Pattern Recognition Series
publication date: 2019-06-17T00:00:00Z
review text: The book describes probabilistic graphical models in application to computer vision tasks. The theoretical concepts are accompanied by illustrative figures and algorithms in pseudocode. All the main categories of models are referred to. The applications range from image denoising and segmentation, object detection and tracking to 3D reconstruction and action recognition. It is a book that is valuable for theoreticians and practitioners alike.
reviewed by: Catalin Stoean
Mathematics Subject Classification ID: 68-01
Mathematics Subject Classification ID: 62H22
Mathematics Subject Classification ID: 68T45
Mathematics Subject Classification ID: 68U10
zbMATH DE Number: 7066913
zbMATH Keywords: computer vision
zbMATH Keywords: probabilistic graphical models
zbMATH Keywords: image denoising
zbMATH Keywords: image segmentation
zbMATH Keywords: object detection
zbMATH Keywords: object tracking
zbMATH Keywords: 3D reconstruction
zbMATH Keywords: action recognition
MaRDI profile type: Publication
description: scientific article