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Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning
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3D Point Cloud Analysis - Traditional, Deep Learning, and Explainable Machine Learning Methods (Hardcover, 1st ed. 2021)
Loot Price: R3,357
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3D Point Cloud Analysis - Traditional, Deep Learning, and Explainable Machine Learning Methods (Hardcover, 1st ed. 2021)
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This book introduces the point cloud; its applications in industry,
and the most frequently used datasets. It mainly focuses on three
computer vision tasks -- point cloud classification, segmentation,
and registration -- which are fundamental to any point cloud-based
system. An overview of traditional point cloud processing methods
helps readers build background knowledge quickly, while the deep
learning on point clouds methods include comprehensive analysis of
the breakthroughs from the past few years. Brand-new explainable
machine learning methods for point cloud learning, which are
lightweight and easy to train, are then thoroughly introduced.
Quantitative and qualitative performance evaluations are provided.
The comparison and analysis between the three types of methods are
given to help readers have a deeper understanding. With the rich
deep learning literature in 2D vision, a natural inclination for 3D
vision researchers is to develop deep learning methods for point
cloud processing. Deep learning on point clouds has gained
popularity since 2017, and the number of conference papers in this
area continue to increase. Unlike 2D images, point clouds do not
have a specific order, which makes point cloud processing by deep
learning quite challenging. In addition, due to the geometric
nature of point clouds, traditional methods are still widely used
in industry. Therefore, this book aims to make readers familiar
with this area by providing comprehensive overview of the
traditional methods and the state-of-the-art deep learning methods.
A major portion of this book focuses on explainable machine
learning as a different approach to deep learning. The explainable
machine learning methods offer a series of advantages over
traditional methods and deep learning methods. This is a main
highlight and novelty of the book. By tackling three research tasks
-- 3D object recognition, segmentation, and registration using our
methodology -- readers will have a sense of how to solve problems
in a different way and can apply the frameworks to other 3D
computer vision tasks, thus give them inspiration for their own
future research. Numerous experiments, analysis and comparisons on
three 3D computer vision tasks (object recognition, segmentation,
detection and registration) are provided so that readers can learn
how to solve difficult Computer Vision problems.
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