Fast algorithms for color image processing by principal component analysis

Cheng, Stone; Hsia, Shih-Chang

doi:10.1016/s1047-3203(03)00024-5

Cited by 23 publications

(20 citation statements)

References 14 publications

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“…However, more than a decade passed since the idea was successfully incorporated into a practical algorithm. In 2003, without being seriously involved in the theoretical aspects, Cheng and Hsia used the principal component analysis (PCA) for color image processing [23]. Then, in 2004, Nikolaev and Nikolayev started the work again from the theory and showed that the PCA is a proper tool for color image processing [24].…”

Section: Previous Work On Pca-based Color Processingmentioning

confidence: 99%

An efficient PCA-based color transfer method

Abadpour

Kasaei

2007

Journal of Visual Communication and Image Representation

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Color information of natural images can be considered as a highly correlated vector space. Many different color spaces have been proposed in the literature with different motivations toward modeling and analysis of this stochastic field. Recently, color transfer among different images has been under investigation. Color transferring consists of two major categories: colorizing grayscale images and recoloring colored images. The literature contains a few color transfer methods that rely on some standard color spaces. In this paper, taking advantages of the principal component analysis (PCA), we propose a unifying framework for both mentioned problems. The experimental results show the efficiency of the proposed method. The performance comparison of the proposed method is also given.

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Section: Previous Work On Pca-based Color Processingmentioning

confidence: 99%

An efficient PCA-based color transfer method

Abadpour

Kasaei

2007

Journal of Visual Communication and Image Representation

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“…In addition to image enhancement for visual inspection, our method can help existing implementations of grayscale algorithms to take better account of isoluminant image regions when performing pattern recognition. Cheng and Hsia [4] pursued related goals and devised color algorithms for edge detection, image sharpening, and image compression relying on an approximation to principal component analysis to determine color ordering. The global consistency of our color to grayscale mapping imposes an image dependent color ordering relation that could prove useful in extending mathematical morphology to color image processing, as discussed by Hanbury and Serra [5].…”

Section: Discussionmentioning

confidence: 99%

Decolorize: Fast, contrast enhancing, color to grayscale conversion

Grundland

Dodgson

2007

Pattern Recognition

230

182

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We present a new contrast enhancing color to grayscale conversion algorithm which works in real-time. It incorporates novel techniques for image sampling and dimensionality reduction, sampling color differences by Gaussian pairing and analyzing color differences by predominant component analysis. In addition to its speed and simplicity, the algorithm has the advantages of continuous mapping, global consistency, and grayscale preservation, as well as predictable luminance, saturation, and hue ordering properties.

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“…Reinhard et al [1] modeled colors in the image by Guassian models. Cheng and Hsia [3], Kotera [4], Xiao and Ma [5], Abadpour and Kasaei [6] used the principal component analysis (PCA) to model image colors. Pitié et al [7] proposed a nonlinear color transfer technique using N-dimensional probability density function transfer.…”

Section: Related Workmentioning

confidence: 99%

Color Transfer by Region Exploration and Navigation

Phatthanachuanchom

Tanawongsuwan

2017

IEICE Trans. Inf. & Syst.

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SUMMARYColor transfer is a simple process to change a color tone in one image (source) to look like another image (target). In transferring colors between images, there are several issues needed to be considered including partial color transfer, trial-and-error, and multiple target color transfer. Our approach enables users to transfer colors partially and locally by letting users select their regions of interest from image segmentation. Since there are many ways that we can transfer colors from a set of target regions to a set of source regions, we introduce the region exploration and navigation approach where users can choose their preferred color tones to transfer one region at a time and gradually customize towards their desired results. The preferred color tones sometimes can come from more than one image; therefore our method is extended to allow users to select their preferred color tones from multiple images. Our experimental results have shown the flexibility of our approach to generate reasonable segmented regions of interest and to enable users to explore the possible results more conveniently.

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Fast algorithms for color image processing by principal component analysis

Cited by 23 publications

References 14 publications

An efficient PCA-based color transfer method

An efficient PCA-based color transfer method

Decolorize: Fast, contrast enhancing, color to grayscale conversion

Color Transfer by Region Exploration and Navigation

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