Deep Learning in Object Detection and Recognition

Read [Springer Book] * Deep Learning in Object Detection and Recognition Online * PDF eBook or Kindle ePUB free. Deep Learning in Object Detection and Recognition It provides a systematic and methodical overview of the latest developments in deep learning theory and its applications to computer vision, illustrating them using key topics, including object detection, face analysis, 3D object recognition, and image retrieval.The book offers a rich blend of theory and practice. It is suitable for students, researchers and practitioners interested in deep learning, computer vision and beyond and can also be used as a reference book. This book discusses recent

Deep Learning in Object Detection and Recognition

Author :
Rating : 4.86 (542 Votes)
Asin : 9811051518
Format Type : paperback
Number of Pages : 582 Pages
Publish Date : 2015-08-28
Language : English

DESCRIPTION:

It provides a systematic and methodical overview of the latest developments in deep learning theory and its applications to computer vision, illustrating them using key topics, including object detection, face analysis, 3D object recognition, and image retrieval.The book offers a rich blend of theory and practice. It is suitable for students, researchers and practitioners interested in deep learning, computer vision and beyond and can also be used as a reference book. This book discusses recent advances in object detection and recognition using deep learning methods, which have achieved great success in the field of computer vision and image processing. The comprehensive comparison of various deep-learning applications helps readers with a basic understanding of machine learning and calculus grasp the theories and inspires applications in othe

. It provides a systematic and methodical overview of the latest developments in deep learning theory and its applications to computer vision, illustrating them using key topics, including object detection, face analysis, 3D object recognition, and image retrieval.The book offers a rich blend of theory and practice. The comprehensive comparison of various deep-learning applications helps readers with a basic understanding of machine learning and calculus grasp the theories and inspires applications in other computer vision tasks. It is suitable for students, researchers and practitioners interested in deep learning, computer vision and beyond and can also be used as a re

in EE from the Poly-technique Montréal in 2001, and worked as a defense scientist at DRDC-Ottawa (1999-2001), and in R&D with Mitel Networks (2001-04). degree in Electronics and Information from Northwestern Polytechnical University in 2001. He is also the founding director of the Visual Pattern Analysis Laboratory of Tianjin University. He has authored more than 100 scientific paper

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