5 edition of **Multivariate image analysis** found in the catalog.

- 328 Want to read
- 32 Currently reading

Published
**1996** by Wiley in Chichester, New York .

Written in English

- Image processing -- Statistical methods.,
- Multivariate analysis.

**Edition Notes**

Includes bibliographical references (p. [299]-311) and index.

Statement | Paul Geladi, Hans Grahn. |

Contributions | Grahn, Hans. |

Classifications | |
---|---|

LC Classifications | TA1637 .G45 1996 |

The Physical Object | |

Pagination | xiii, 316 p., [16] p. of plates : |

Number of Pages | 316 |

ID Numbers | |

Open Library | OL975902M |

ISBN 10 | 0471930016 |

LC Control Number | 96012988 |

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Multivariate Image Analysis gives the reader a sound understanding of the importance of, and the principles behind, multivariate image analysis. A short introduction to the image and its perception is followed by a discussion of some popular techniques of multivariate image formation, taken from fields such as microscopy, remote sensing and Cited by: Multivariate image analysis (MIA) is a methodology for analyzing multivariate images, where the image coordinates are position (two- or three-dimensions) and variable number.

Multivariate image analysis. Multivariate image analysis (MIA) was born to deal with images that presented more than one measurement per pixel (related to the three RGB channels in color images or to a number of spectroscopic channels in multispectral and hyperspectral images).Cited by: Perhaps "Applied Multivariate Data Analysis", 2nd edition, by Everitt, B.

and Dunn, G. (), published by Arnold. [Roger Johnson] Rencher's Methods of Multivariate Analysis is a great resource. I think a strong undergraduate student could grasp the material.

[Philip Yates]. I'm fond of Rencher's approach. Multivariate image analysis is an analytical technique used in many areas including remote sensing (satellites), medicine (MRIs) and the food and chemical industries.

Though there are many good books on using this technique in remote sensing, this is one of the first that focuses on its use in chemistry, medicine and food analysis. Multivariate analysis (MVA) is based on the principles of multivariate statistics, which involves observation and analysis of more than one statistical outcome variable at a lly, MVA is used to address the situations where multiple measurements are made on each experimental unit and the relations among these measurements and their structures are important.

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Duchesne C, Liu JJ, MacGregor JF () Multivariate image analysis in the process industries: a review. Chemom Intell Lab Syst – CrossRef Google Scholar Esbensen K, Geladi P () Strategy of multivariate image analysis (MIA). Multivariate Statistics Introduction 1 Population Versus Sample 2 Elementary Tools for Understanding Multivariate Data 3 Data Reduction, Description, and Estimation 6 Concepts from Matrix Algebra 7 Multivariate Normal Distribution 21.

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Multivariate Image Analysis has a short introduction to the image and its perception is followed by a discussion of some popular techniques of multivariate image formation, taken from fields such as microscopy, remote sensing and medical principles behind one of the key multivariate techniques, principal components analysis, are thoroughly explained without going too far into the.

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Lectures, presentations on principal components analysis, correspondence analysis, other dimensionality reduction methods, discriminant analysis, cluster analysis, with various applications.