0000000001109573

AUTHOR

Steven Le Moan

showing 2 related works from this author

Visualisation d'images spectrales : une méthode basée sur la perception humaine

2011

We propose a new method for the visualization of spectral images. It involves a perception-based spectrum segmentation using an adaptable thresholding of the stretched CIE standard observer color-matching functions. This allows for an underlying removal of irrelevant channels, and, consequently, an alleviation of the computational burden of further processings. Principal Components Analysis is then used in each of the three segments to extract the Red, Green and Blue primaries for final visualization. A comparison framework using two different datasets shows the efficiency of the proposed method.

[INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV][INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV][ INFO.INFO-CV ] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]
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Visualization of spectral images: a comparative study

2011

International audience; The dimensionality reduction of spectral images for visualization has been a quite active area of research recently. Given the variety of existing approaches, it can be very chal- lenging to understand the actual advantages of one over another, especially in the absence of a very specific application. Moreover, there is no consensus on how to evaluate the general efficiency of such a method. In this paper, we propose a comparison framework not only to compare such techniques, but also to measure their intrinsic properties in terms of naturalness and informative content.

[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing[INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing
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