Machine Learning In Computer Vision (computational Imaging And Vision)
by Thomas S. Huang /
2005 / English / PDF
6.5 MB Download
The goal of this book is to address the use of several important
machine learning techniques into computer vision applications. An
innovative combination of computer vision and machine learning
techniques has the promise of advancing the field of computer
vision, which contributes to better understanding of complex
real-world applications. The effective usage of machine learning
technology in real-world computer vision problems requires
understanding the domain of application, abstraction of a
learning problem from a given computer vision task, and the
selection of appropriate representations for the learnable
(input) and learned (internal) entities of the system.
The goal of this book is to address the use of several important
machine learning techniques into computer vision applications. An
innovative combination of computer vision and machine learning
techniques has the promise of advancing the field of computer
vision, which contributes to better understanding of complex
real-world applications. The effective usage of machine learning
technology in real-world computer vision problems requires
understanding the domain of application, abstraction of a
learning problem from a given computer vision task, and the
selection of appropriate representations for the learnable
(input) and learned (internal) entities of the system.
In this book, we address all these important aspects from a new
perspective: that the key element in the current computer
revolution is the use of machine learning to capture the
variations in visual appearance, rather than having the designer
of the model accomplish this. As a bonus, models learned from
large datasets are likely to be more robust and more realistic
than the brittle all-design models.
In this book, we address all these important aspects from a new
perspective: that the key element in the current computer
revolution is the use of machine learning to capture the
variations in visual appearance, rather than having the designer
of the model accomplish this. As a bonus, models learned from
large datasets are likely to be more robust and more realistic
than the brittle all-design models.