Multivariate image analysis: a review with applications

By J. Prats-Montalbán, A. de Juan, A. Ferrer. 2011

301 citations recorded by the source corpus.

Abstract Nowadays, image analysis is becoming more important because of its ability to perform fast and non-invasive low-cost analysis on products and processes. Image analysis is a wide denomination that encloses classical studies on gray scale or RGB images, analysis of images collected using few spectral channels (sometimes called multispectral images) or, most recently, data treatments to deal with hyperspectral images, where the spectral direction is exploited in its full extension. Pioneering data treatments in image analysis were applied to simple images mainly for defect detection, segmentation and classification by the Computer Science community. From the late 80s, the chemometric community joined this field introducing powerful tools for image analysis, which were already in use for the study of classical spectroscopic data sets and were appropriately modified to fit the particular characteristics of image structures. These chemometric approaches adapt to images of all kinds, from the simplest to the hyperspectral images, and have provided new insights on the spatial and spectroscopic information of this kind of data sets. New fields open by the introduction of chemometrics on image analysis are exploratory image analysis, multivariate statistical process control (monitoring), multivariate image regression or image resolution. This paper reviews the different techniques developed in image analysis and shows the evolution in the information provided by the different methodologies, which has been heavily pushed by the increasing complexity of the image measurements in the spatial and, particularly, in the spectral direction.

Subfields: Analytical Chemistry, Biophysics, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/e3b573a03459ff85b58f2b5f81a515c9ffb3a0e7

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Citations in the corpus

Recent Advances and Applications of Hyperspectral Imaging for Fruit and Vegetable Quality Assessment (2012, methodology): “General multivariate image analysis is reviewed in Prats-Montalbán et al. (2011).”
Multivariate Mixture Model for Myocardial Segmentation Combining Multi-Source Images (2016, methodology): “D deformation. In the literature, there is a research topic referred to as multivariate image analysis, which has been proposed to deal with images that have more than one measurement per pixel [16], [17]. This research has particularly focused on the three-channel (RGB) color images or multispectra”