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Deep Learning in Object Recognition, Detection, and Segmentation (Foundations and Trends(r) in Signal Processing #23) (Paperback)

Deep Learning in Object Recognition, Detection, and Segmentation (Foundations and Trends(r) in Signal Processing #23) Cover Image
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Description


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.


Product Details
ISBN: 9781680831160
ISBN-10: 168083116X
Publisher: Now Publishers
Publication Date: July 14th, 2016
Pages: 186
Language: English
Series: Foundations and Trends(r) in Signal Processing
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Data Source: Ingram
Created At: 7/24/2016 01:48pm
Last Updated At: 4/2/2022 03:44pm