Automated color image arrangement method for multiple-peak image

Kawakami, Yusuke; Hattori, Tetsuo; Imai, Yumiko; Horikawa, Yo; Matsushita, Haruna; Rajapakse, R.P.C. Janaka

doi:10.1109/cscesm.2015.7331823

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Histogram Matching Based On Gaussian Distribution (Hmgd)1

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2016

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“…In addition, let y=f(x) be a continuous and monotonic increase function corresponding to cumulative histogram of image brightness level between variables x and y. [7][8][9] And let f(x) be defined by Eq.…”

Section: Histogram Matching Based On Gaussian Distribution (Hmgd)mentioning

confidence: 99%

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Automated Processing of Multiple-Brightness Peak Histogram Image Using Curvature and Variance Estimation

Kawakami¹,

Hattori²,

Imai³

et al. 2016

JRNAL

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Previously, we have illustrated that the Histogram Matching based on Gaussian Distribution (HMGD) is an effective automated image processing method for obtaining a better feeling impression image. However, the simple HMGD works only for the image whose histogram has just one peak. For the image whose histogram has multiplebrightness peak, it does not work as in the case of single peak histogram image. In this paper, we propose the improved method for multiple-brightness peak (HMGD-MBP). This method can not only detect multiple peaks but also estimate the variance of Gaussian distribution at each detected peak in the image histogram, using curvature computation. This paper also presents the effectiveness of the proposed method by showing the experimental results.

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Section: Histogram Matching Based On Gaussian Distribution (Hmgd)mentioning

confidence: 99%

“…Conceptual histogram of HMGD processed image (brightness peak x 1 ). Conceptual histogram of output image 9. Original image and its histogram (b) HMGD processed image and its histogram (c) HMGD-MBP processed image and its histogram Experimental results (I) 10.…”

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confidence: 99%