Automated Color Image Arrangement Method Based on Histogram Matching

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

doi:10.5057/ijae.14.85

Cited by 10 publications

(6 citation statements)

References 5 publications

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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%

“…[4][5] And also, we have illustrated that HMGD processing works to obtain better feeling (or Kansei) impression than that of original image, by the comparative experimental investigation. 6 However, HMGD processing has just worked only for single brightness-peak histogram image. In this paper, we present the improved HMGD for multiple-brightness peak histogram image that we call "HMGD-MBP", using curvature computation and variance estimation.…”

Section: Introductionmentioning

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

Self Cite

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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%

Section: Introductionmentioning

confidence: 99%

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

Kawakami¹,

Hattori²,

Imai³

et al. 2016

JRNAL

Self Cite

View full text Add to dashboard Cite

show abstract

“…The example analysis results showed that the scheme could obtain a satisfactory color scheme. Kawakami et al [6] studied the automatic color arrangement method based on Gaussian distribution histogram matching. The experimental results showed that the visual perception of the image processed by the color arrangement method significantly improved.…”

Section: Introductionmentioning

confidence: 99%

Investigation into color designs of product packaging through visual evaluations using machine learning methods

Gao

2021

Manufacturing Rev.

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For a commodity, in addition to its quality, its external package is also very essential. This paper briefly introduced the intelligent support vector machine (SVM) algorithm for color design of paper packaging. The features were extracted from photos of packages using scale-invariant feature transform (SIFT), and the intelligent algorithm was trained and tested using photos of paper packaging for ceramic products collected at the ceramic crafts market as a sample set. Two paper package schemes designed in this study were used for further test. The SVM algorithm was compared with the back-propagation (BP) algorithm and the convolutional neural network (CNN) algorithm. The results showed that the three intelligent algorithms could evaluate the color design of paper packages, but the SVM algorithm was more accurate than the BP and CNN algorithms in evaluating the imagery of color design, both for the samples collected in the craft market and for the paper packaging scheme designed in this paper.

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“…And we have illustrated that the HMGD processing could improve the feeling (or Kansei) impression better than the original image. 6 In order to further develop the HMGD processing method for better automated one, we noticed that the variance estimation of Gaussian distribution around the neighborhood of peak in the original image's histogram should be performed. Then previously, we presented a variance (or Gaussian width) estimation method using the symmetry of Gaussian function.…”

Section: Introductionmentioning

confidence: 99%

Regression Analysis Based Variance Estimation of Gaussian Distribution for Histogram Matching

Kawakami¹,

Hattori²,

Imai³

et al. 2018

JRNAL

Self Cite

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This paper describes an improved method of the variance estimation of Gaussian distribution for Histogram Matching based on Gaussian Distribution (HMGD). In our previous paper, focusing on the symmetry of the Gaussian function, we presented another method for the variance (or Gaussian width) estimation of Gaussian distribution in the original histogram. However, since the real shape of mountain like Gaussian function in the original image's histogram does not always show the good symmetry, the variance estimation method that we previously presented did not work so well as we expected. In this paper, we newly propose the improved estimation method using regression analysis, based on curvature computation for the cumulative histogram of original image's one. In the newly proposed method, first, we detect the histogram peak of original image's histogram by using curvature computation; next, we perform the regression analysis for the cumulative histogram, using an approximated function of the curvature that includes the variance parameter. Also in this paper, we show some experimental results by the estimation method.

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Automated Color Image Arrangement Method Based on Histogram Matching

Cited by 10 publications

References 5 publications

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

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

Investigation into color designs of product packaging through visual evaluations using machine learning methods

Regression Analysis Based Variance Estimation of Gaussian Distribution for Histogram Matching

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