Multispectral image visualization through first-order fusion

Socolinsky, Diego A.; Wolff, Lawrence B.

doi:10.1109/tip.2002.801588

Cited by 113 publications

(89 citation statements)

References 10 publications

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“…To this end, they encode the chromaticity contrast rather than the luminance information. Socolinsky and Wolff (2002) were amongst the first to propose an elegant decolourization technique that embedded this principle. During the past years, many new perception-based decolourization approaches have surfaced, all claiming to more or less preserve lightness, meaningful colour contrast and other visual features in the greyscale variant.…”

Section: Decolourization Methodsmentioning

confidence: 99%

Mind Your Grey Tones – Examining the Influence of Decolourization Methods on Interest Point Extraction and Matching for Architectural Image-Based Modelling

Verhoeven

Karel

Štuhec

et al. 2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:This paper investigates the use of different greyscale conversion algorithms to decolourize colour images as input for two Structurefrom-Motion (SfM) software packages. Although SfM software commonly works with a wide variety of frame imagery (old and new, colour and greyscale, airborne and terrestrial, large-and small scale), most programs internally convert the source imagery to singleband, greyscale images. This conversion is often assumed to have little, if any, impact on the final outcome. To verify this assumption, this article compares the output of an academic and a commercial SfM software package using seven different collections of architectural images. Besides the conventional 8-bit true-colour JPEG images with embedded sRGB colour profiles, for each of those datasets, 57 greyscale variants were computed with different colour-to-greyscale algorithms. The success rate of specific colour conversion approaches can therefore be compared with the commonly implemented colour-to-greyscale algorithms (luma Y'601, luma Y'709, or luminance CIE Y), both in terms of the applied feature extractor as well as of the specific image content (as exemplified by the two different feature descriptors and the various image collections, respectively). Although the differences can be small, the results clearly indicate that certain colour-to-greyscale conversion algorithms in an SfMworkflow constantly perform better than others. Overall, one of the best performing decolourization algorithms turns out to be a newly developed one.

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Section: Decolourization Methodsmentioning

confidence: 99%

Mind Your Grey Tones – Examining the Influence of Decolourization Methods on Interest Point Extraction and Matching for Architectural Image-Based Modelling

Verhoeven

Karel

Štuhec

et al. 2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Application of these filters is similar to the FFT with the only difference that the group of the discrete shift operators is replaced by the dihedral group D(4). From the general theory it follows that there are six filter pairs that transform like common edgedetection filters (e (1) x , e (1) y , . .…”

Section: Theoretical Background and Implementationmentioning

confidence: 99%

“…Combining the information from several color channels into one scalar value is one of the basic operations in color processing [1][2][3]. A typical example is the separation of the color information into a scalar-valued intensity and a vectorvalued chromaticity component.…”

Section: Introductionmentioning

confidence: 99%

Fisher Information and the Combination of RGB Channels

Lenz

Zografos

2013

Lecture Notes in Computer Science

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Abstract. We introduce a method to combine the color channels of an image to a scalar valued image. Linear combinations of the RGB channels are constructed using the Fisher-Trace-Information (FTI), defined as the trace of the Fisher information matrix of the Weibull distribution, as a cost function. The FTI characterizes the local geometry of the Weibull manifold independent of the parametrization of the distribution. We show that minimizing the FTI leads to contrast enhanced images, suitable for segmentation processes. The Riemann structure of the manifold of Weibull distributions is used to design optimization methods for finding optimal weight RGB vectors. Using a threshold procedure we find good solutions even for images with limited content variation. Experiments show how the method adapts to images with widely varying visual content. Using these image dependent de-colorizations one can obtain substantially improved segmentation results compared to a mapping with pre-defined coefficients.

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“…This technique reduces the processing cost substantially, compared to previous approaches. The multi-spectral method of Alsam and Drew [17] improves the approach of Socolinsky and Wolff [18] in terms of computation efficiency. Both approaches aim to preserve the maximum local contrast.…”

Section: Related Workmentioning

confidence: 99%

Image and Video Decolorization by Fusion

Ancuti

Hermans

Bekaert

2011

Computer Vision – ACCV 2010

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Abstract. In this paper we present a novel decolorization strategy, based on image fusion principles. We show that by defining proper inputs and weight maps, our fusion-based strategy can yield accurate decolorized images, in which the original discriminability and appearance of the color images are well preserved. Aside from the independent R,G,B channels, we also employ an additional input channel that conserves color contrast, based on the Helmholtz-Kohlrausch effect. We use three different weight maps in order to control saliency, exposure and saturation. In order to prevent potential artifacts that could be introduced by applying the weight maps in a per pixel fashion, our algorithm is designed as a multi-scale approach. The potential of the new operator has been tested on a large dataset of both natural and synthetic images. We demonstrate the effectiveness of our technique, based on an extensive evaluation against the state-of-the-art grayscale methods, and its ability to decolorize videos in a consistent manner.

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Multispectral image visualization through first-order fusion

Cited by 113 publications

References 10 publications

Mind Your Grey Tones – Examining the Influence of Decolourization Methods on Interest Point Extraction and Matching for Architectural Image-Based Modelling

Mind Your Grey Tones – Examining the Influence of Decolourization Methods on Interest Point Extraction and Matching for Architectural Image-Based Modelling

Fisher Information and the Combination of RGB Channels

Image and Video Decolorization by Fusion

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