國立虎尾科技大學 |

Language: English

Back

Deep learning in object recognition,...

Wang, Xiaogang.

Deep learning in object recognition, detection, and segmentation /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Deep learning in object recognition, detection, and segmentation // Xiaogang Wang.
Author:	Wang, Xiaogang.
Published:	Hanover, MA :Now Publishers Inc, : 2016.,
Description:	xi, 173 p. :ill. ; : 24 cm.;
Subject:	Machine learning. -
ISBN:	9781680831160 (Pbk) :

Deep learning in object recognition, detection, and segmentation /
Wang, Xiaogang.

Deep learning in object recognition, detection, and segmentation /Xiaogang Wang. - Hanover, MA :Now Publishers Inc, 2016. - xi, 173 p. :ill. ;24 cm. - Foundations and trends in signal processing ;volume 8, issue 4 (2014). - Foundations and trends in signal processing ;volume 8, issue 4 (2014).

Includes bibliographical references.

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.

ISBN: 9781680831160 (Pbk) :NT3628Subjects--Topical Terms:

561253
Machine learning.

LC Class. No.: TK5102.9 / .W364 2016

Dewey Class. No.: 621.382/2

Deep learning in object recognition, detection, and segmentation /
LDR:02119cam a2200193 a 4500 001 891214
008 180920s2016 maua b 000 0 eng d
020 $a 9781680831160 (Pbk) : $c NT3628
020 $a 168083116X (Pbk)
035 $a (OCoLC)ocn950689723
035 $a 002063107
040 $a YDXCP $b eng $c YDXCP $d BTCTA $d BDX $d OCLCQ $c NFU
050 4 $a TK5102.9 $b .W364 2016
082 0 4 $a 621.382/2 $2 23
100 1 $a Wang, Xiaogang. $3 1149461
245 1 0 $a Deep learning in object recognition, detection, and segmentation / $c Xiaogang Wang.
260 $a Hanover, MA : $b Now Publishers Inc, $c 2016.
300 $a xi, 173 p. : $b ill. ; $c 24 cm.
490 1 $a Foundations and trends in signal processing ; $v volume 8, issue 4 (2014)
504 $a Includes bibliographical references.
520 $a 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.
650 0 $a Machine learning. $3 561253
650 0 $a Signal processing $x Digital techniques. $3 556357
830 0 $a Foundations and trends in signal processing ; $v volume 8, issue 4 (2014) $3 1149462