Deep Learning in Object Recognition, Detection, and Segmentation (Foundations and Trends in Signal Processing)

Wang, Xiaogang

ISBN 10: 168083116X / ISBN 13: 9781680831160

Published by Now Publishers, 2016

About this Item

PRINT ON DEMAND pp. 188.

Seller Inventory # 18374999119

Bibliographic Details

• Title: Deep Learning in Object Recognition, Detection, and Segmentation (Foundations and Trends in Signal Processing)
• Publisher: Now Publishers
• Publication Date: 2016
• Language: English
• ISBN 10: 168083116X
• ISBN 13: 9781680831160
• Binding: Soft cover
• Condition: New

About this title

Synopsis

As a major breakthrough in artificial intelligence, deep learning has achieved impressive success on solving grand challenges in many fields including speech recognition, natural language processing, computer vision, image and video processing, and multimedia. This monograph provides a historical overview of deep learning and focuses on its applications in object recognition, detection, and segmentation, which are key challenges of computer vision and have numerous applications to images and videos. Specifically the topics covered under object recognition include image classification on ImageNet, face recognition, and video classification. In detection, the monograph covers general object detection on ImageNet, pedestrian detection, face landmark detection (face alignment), and human landmark detection (pose estimation). Finally, within segmentation, it covers the most recent progress on scene labeling, semantic segmentation, face parsing, human parsing, and saliency detection. Concrete examples of these applications explain the key points that make deep learning outperform conventional computer vision systems. Deep Learning in Object Recognition, Detection, and Segmentation provides a comprehensive introductory overview of a topic that is having major impact on many areas of research in signal processing, computer vision, and machine learning. This is a must-read for students and researchers new to these fields.