Statistics for imaging, optics, and photonics
"This important resource bridges the gap between imaging, optics, and photonics, and statistics and data analysis. The text contains a wide range of relevant statistical methods including a review of the fundamentals of statistics and expanding into multivariate techniques. The techniques are explained in the context of real examples from remote sensing, multispectral and hyperspectral imaging, signal processing, color science, and other related disciplines. The book also emphasizes intuitive a…
Read More ↓Title Statements
- Full title :Statistics for imaging, optics, and photonics / Peter Bajorski.
Summary
"This important resource bridges the gap between imaging, optics, and photonics, and statistics and data analysis. The text contains a wide range of relevant statistical methods including a review of the fundamentals of statistics and expanding into multivariate techniques. The techniques are explained in the context of real examples from remote sensing, multispectral and hyperspectral imaging, signal processing, color science, and other related disciplines. The book also emphasizes intuitive and geometric understanding of concepts. The topics that are most relevant to imaging, optics, and photonics applications are covered thoroughly. In addition, supplemental topics are discussed to provide an overview of when and how the methods can be used"--
Read More ↓Edition Statements
- Publication Date:2012.
Publication Statements
- Publisher(s):Wiley
- Place of Publication:Hoboken, N.J
- Language:eng
- Publication Date:2012.
The Physical Object
- Description:1 online resource
Edition Identifiers
- ISBN:9781118121955, 1118121953, 0470509457, 9780470509456, 9781118121924, 1118121929
- oclc:ocn757511624
- LC Classification:QC369
- Dewey Decimal:621.3601/5195
Table of Contents
Front Matter -- Introduction -- Fundamentals of Statistics -- Statistical Inference -- Statistical Models -- Fundamentals of Multivariate Statistics -- Multivariate Statistical Inference -- Principal Component Analysis -- Canonical Correlation Analysis -- Discrimination and Classification ₆ Supervised Learning -- Clustering ₆ Unsupervised Learning -- Appendix A: Probability Distributions -- Appendix B: Data Sets -- Appendix C: Miscellanea -- References -- Index -- Wiley Series in Probability and Statistics.
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