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A. Bovik (2009)
Basic Gray Level Image Processing
H. Geus, J. Boer, U. Brinkman (1998)
Two-dimensional gas chromatography
A. Bovik (2005)
2.1 – Basic Gray-Level Image Processing
R. Hummel (1975)
Image Enhancement by Histogram transformationComputer Graphics and Image Processing, 6
J. Kautsky, D. Jupp, N. Nichols (1981)
Image enhancement by smoothed histogram modification
(2000)
Bertsch , “ Two - dimensional gas chromatography . Concepts , instrumentation , and applications — Part 2 : Comprehensive two - dimensional gas chromatography
W. Bertsch (2000)
Two‐Dimensional Gas Chromatography. Concepts, Instrumentation, and Applications – Part 2: Comprehensive Two‐Dimensional Gas ChromatographyHrc-journal of High Resolution Chromatography, 23
30 arc-second digital elevation model for Australia, " in The Global Land One- Kilometer Base Elevation (GLOBE) Digital Elevation Model
Bovik , “ Basic gray - level image processing
E. Ledford, Chris Billesbach (2000)
Jet‐Cooled Thermal Modulator for Comprehensive Multidimensional Gas ChromatographyHrc-journal of High Resolution Chromatography, 23
R. Hummel (1975)
Histogram modification techniquesComputer Graphics and Image Processing, 4
A. Visvanathan, S. Reichenbach, Qingping Tao (2006)
Gradient-based value mapping for colorization of two-dimensional fields, 6246
S. Reichenbach, Mingtian Ni, Visweswara Kottapalli, A. Visvanathan (2004)
Information technologies for comprehensive two-dimensional gas chromatographyChemometrics and Intelligent Laboratory Systems, 71
S. Reichenbach, Mingtian Ni, Dongmin Zhang, E. Ledford (2003)
Image background removal in comprehensive two-dimensional gas chromatography.Journal of chromatography. A, 985 1-2
We develop a method for automatic colorization of images (or two-dimensional fields) in order to visualize pixel values and their local differences. In many applications, local differences in pixel values are as important as their values. For example, in topography, both elevation and slope often must be considered. Gradient-based value mapping (GBVM) is a technique for colorizing pixels based on value (e.g., intensity or elevation) and gradient (e.g., local differences or slope). The method maps pixel values to a color scale (either gray-scale or pseudocolor) in a manner that emphasizes gradients in the image while maintaining ordinal relationships of values. GBVM is especially useful for high-precision data, in which the number of possible values is large. Colorization with GBVM is demonstrated with data from comprehensive two-dimensional gas chromatography (GCxGC), using both gray-scale and pseudocolor to visualize both small and large peaks, and with data from the Global Land One-Kilometer Base Elevation (GLOBE) Project, using gray-scale to visualize features that are not visible in images produced with popular value-mapping algorithms.
Journal of Electronic Imaging – SPIE
Published: Jul 1, 2007
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