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Books > Computing & IT > Applications of computing > Signal processing
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Deep Learning in Object Recognition, Detection, and Segmentation (Paperback)
Loot Price: R2,335
Discovery Miles 23 350
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Deep Learning in Object Recognition, Detection, and Segmentation (Paperback)
Series: Foundations and Trends (R) in Signal Processing
Expected to ship within 10 - 15 working days
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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.
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