Yusuke Kawakami

An Automated Color Image Arrangement Method Based on Histogram Matching

We propose a novel color image arrangement method using an elastic transform based on histogram matching on some kinds of axes. The axes include Lightness axis and PC axes obtained from Principal Component Analysis (PCA) in the RGB three-dimensional vector space that is an attribute space of color image. In this paper, we mainly present the principle of its automated color arrangement method especially based on HMGD (Histogram Matching based on Gaussian Distribution). And we describe that the automated method applies the HMGD to input color image only if the image has single-peaked ness in its histogram on the focused axis. In order to detect the single-peaked ness of histogram, the method uses a curvature computation for the cumulative histogram. We show that the method brings about a good sensibility effect (or Kansei effect) in the case of applying the HMGD onto Lightness axis.

Experimental Investigation of Feature Quantity in Sound Signal and Feeling Impression Using PCA

This paper describes experimental investigation of the relationship between feature quantity of sound signal and feeling impression using PCA (Principal Component Analysis). As the feature quantity, we use Fluctuation value and sum of squared errors (Residual) which is calculated by regression analysis of sound signal, in the same way as our previous paper. In order to investigate the feeling impression and effect from sound signal, we use a questionnaire survey method, that is, we ask some examinees to evaluate their feeling impression about sound (music) that we provide. As a result, we have found that the feeling response of examinees can be classified into three groups by a clustering analysis. And we have verified the feeling impression effects depending on each group of examinees and four kinds of frequency zone of sound signal from the results of PCA. In this paper, we also discuss the analysis results on the Kansei (or feeling) effect.

Automated color image arrangement method for multiple-peak image

This paper proposes the Histogram Matching based on the Gaussian Distribution (HMGD) method to improve feeling (or Kansei) impression for multiple-peak images. In the previous papers, we have described the HMGD processing. And we also proposed how calculate whether the processing results good or not, using curvature computation. As the results, we have understood that the HMGD processing works well for single-peak images. In this paper, we describe the principle and processing method of HMGD processing to correspond multiple-peak images. And we also illustrate experimental results.

Kansei Impression and Automated Color Image Arrangement Methods (incl. erratum)

This paper proposes a new color image arrangement method using an elastic transform on some kinds of axes. In this paper, we present the principle of our method using HMGD (Histogram Matching based on Gaussian Distribution). And we describe that the automated method applies the HMGD to input color image only when the image has single-peakedness in its histogram on the focused axis. And we describe about HPA-HMGD (Histogram Peakedness Adaptive HMGD) as improvement HMGD. We also show that the method gives a good Kansei effect in the case of applying the HMGD onto Brightness axis. Moreover, we compare processing results of the HPA- HMGD and HMGD, we show that HPA-HMGD is better.

Regression Analysis Based Variance Estimation of Gaussian Distribution for Histogram Matching

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.

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

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.

Quantitative Evaluation of Flash-based Educational Visualizing Simulator

A Flash-based simulator of CPU scheduling has been developed and utilized for educational visualization in the class of university lecture. We have designed and implemented it with Flash-based scripting language in order to execute it as a stand-alone application as well as in various browsing environment such as Microsoft IE, Chrome and/or FireFox. Based on questionnaire for our simulator, its quantitative evaluation has been carried out by means of statistical analysis. The report describes Flash-based simulator and the results of the quantitative evaluation.