Yeong-Ho Ha

Color image enhancement based on particle swarm optimization with Gaussian mixture

This paper proposes a Gaussian mixture based image enhancement method which uses particle swarm optimization (PSO) to have an edge over other contemporary methods. The proposed method uses the guassian mixture model to model the lightness histogram of the input image in CIEL*a*b* space. The intersection points of the guassian components in the model are used to partition the lightness histogram. . The enhanced lightness image is generated by transforming the lightness value in each interval to appropriate output interval according to the transformation function that depends on PSO optimized parameters, weight and standard deviation of Gaussian component and cumulative distribution of the input histogram interval. In addition, chroma compensation is applied to the resulting image to reduce washout appearance. Experimental results show that the proposed method produces a better enhanced image compared to the traditional methods. Moreover, the enhanced image is free from several side effects such as washout appearance, information loss and gradation artifacts.

Visibility Enhancement of Mobile Device Through Luminance and Chrominance Compensation Upon Hue

The visibility of images displayed on mobile devices can significantly vary according to the lighting conditions. Generally, increasing the lighting scale produces a more compressed perceived color gamut. In addition, the compression rate of the perceived color gamut varies according to the hue angle. Therefore, this paper presents a method of compensating the luminance and chrominance according to the hue of the image displayed on a mobile device. Two psychophysics experiments are initially performed to analyze the human perception of luminance and chrominance. The perceived luminance and perceived chrominance reduction rates are then calculated for the primary colors. Next, to calculate the perceived luminance and chrominance reduction rates for all hue angles, piecewise linear interpolation is used between each primary color hue angle. Finally, the luminance and chrominance compensation rates are calculated by using the precalculated perceived luminance and chrominance reduction rates. The luminance and chrominance compensation rates according to the hue angle are then applied to an input image. Experimental results show that the proposed method enhances the colors in indoor and outdoor environments.

Image thresholding using standard deviation

Threshold selection using the within-class variance in Otsu’s method is generally moderate, yet inappropriate for expressing class statistical distributions. Otsu uses a variance to represent the dispersion of each class based on the distance square from the mean to any data. However, since the optimal threshold is biased toward the larger variance among two class variances, variances cannot be used to denote the real class statistical distributions. Therefore, to express more accurate class statistical distributions, this paper proposes the within-class standard deviation as a criterion for threshold selection, and the optimal threshold is then determined by minimizing the within-class standard deviation. Experimental results confirm that the proposed method produced a better performance than existing algorithms.

Hybrid direct combination color constancy algorithm using ensemble of classifier

Abstract Color constancy algorithm aims to estimate the color of light source. Many of computer vision applications, such as object detection and scene understanding, benefited from this color constancy algorithm. Since the traditional color constancy algorithm uses either a statistical assumption or a trained regression function, none of those methods is universal illuminant estimator. As a solution for this, researchers proposed combination color constancy algorithm that combines the estimate of several statistical or learning based unitary algorithms. Traditional combination method either uses a static weight to combine the estimate of unitary methods or choose a best unitary algorithm for the input image. The former one fails due to the limitation of static weight to correctly reflect the underlying relationship for a wide range of scenes and the second one has the difficulty to train a multi-class model with limited training data. This paper addresses this limitation of combination methods and proposes a hybrid multi-class dynamic weight model with an ensemble of classifiers. The proposed method classifies images into several groups and uses distinct dynamic weight generation model (DWM) for each group. The DWM generates dynamic weight using an image feature that has a correlation with the capability of the unitary algorithm used for combination. Experiments on Gehler–Shi and National University of Singapore color constancy benchmark data set show that the proposed method outperforms state-of-the-art.

Gamut estimation with efficient sampling based on modified segment maxima

Abstract Gamut mapping is necessary to achieve color consistency between cross-media devices. In gamut mapping, accurate estimation of the gamut in each device is an important task because it directly influences on the quality of color consistency. However, depending on the samples or estimation method, a false gamut can be calculated, resulting in color distortion in the reproduced image. Accordingly, to address this problem, accurate gamut estimation with efficient sampling is proposed. The proposed method selectively determines the samples and plugs the local concavities formed from the segment maxima algorithm. We assumed that the surface of the RGB cube roughly corresponds to the surface of the real gamut. Thus, points on the surface of the RGB cube can be selected as samples. Furthermore, points around the primaries are more intensively selected than from other parts of the surface. The local concavities that generate a false gamut are plugged by using modified gamut boundary descriptors. A local concavity is detected using a CounterClockWise algorithm with three consecutive descriptors. The descriptor in a concavity region is then moved to a line connecting the preceding and subsequent descriptors. In experiments, the proposed method accurately estimates the gamut with a small number of samples when compared with previous methods, and largely reduces the color distortion in the reproduced images.

Illumination estimation using nonnegative matrix factorization and dominant chromaticity analysis

Human vision can perceive object colors as being the same as colors under a white illuminant. However, images captured by a camera are influenced by the chromaticity of the illumination. Therefore, various illumination estimation algorithms have already been proposed for removing the chromaticity of illuminations in an image to improve the image quality. Most recently, NMFsc (nonnegative matrix factorization with sparseness constraint) was introduced to extract the illumination and reflectance component using nonnegative matrix decomposition and sparseness constraints. However, if an image has a dominant object, the sparse constraint values include the dominant chromaticity of that object, thereby inducing color distortion. Therefore, this paper suggests illuminant estimation using a combination of the conventional NMFsc and a dominant chromaticity analysis. First, the dominant chromaticity region in an image is selected using a chromaticity histogram and the standard deviation. The non-negative matrix decomposition and sparseness constraints are then separately applied to the dominant chromaticity region and non-dominant chromaticity region. Finally, the illumination in an image is estimated by combining the low sparse constraint values that exclude the dominant chromaticity. The performance of the proposed method is evaluated using the angular error for the Ciurea 11,346 image data set, and the experimental results confirm that the proposed method reduces the angular error more than previous methods.

Illumination estimation based on estimation of dominant chromaticity in nonnegative matrix factorization with sparseness constraint

Changing illumination cause the measurements of object colors to be biased toward chromaticity of illuminants. Various color constancy algorithms are already exist to remove the chromaticity of illuminants in an image for improving image quality. Recently, NMFsc(nonnegative matrix factorization with sparseness constraint) was introduced to extract the illuminant and reflectance component in an image. NMFsc extract illuminant component and reflectance component by using nonnegative matrix decomposition and sparseness constraints. However, if an image has a chromaticity distribution dominated by a particular chromaticity, the sparse constraint values include that dominant chromaticity, thereby inducing color distortion. Therefore, the proposed method modified the matrix decomposition in NMFsc by using standard deviation and K-means algorithm in chromaticity space. Next, non-negative matrix decomposition and sparseness constraints are performed on an image. Subsequently, illumination is estimated by combining the low sparse constraint values that excludes the dominant chromaticity. The performance of the proposed method is evaluated by using angular error for Ciurea 11,346 image data set. Experimental results illustrate that the proposed method reduces the angular error over previous methods.

Improved Linearity and Saturation of Current Sensor by Laminating Silicon Steel and Fermalloy

Abstract The current sensor is used in industrial devices and power utilities. Core materials of these current sensors are divided into mainlytwo groups as silicon steel and fermalloy. Silicon steel has a wide saturation bandwidth but low sensitivity during low-current, whereaspermalloy has a short saturation bandwidth but high sensitivity during low-current. In this paper, laminated silicon steel and permalloyby equal ratio is proposed to improve the linearity and saturation of current sensor. It is proved that the proposed core material has largerbandwidth than fermalloy as well as higher sensitivity than silicon steel. When comparing simulation results by FLUX 3D, the proposedmethod has also better performance than the previous core materials. Keywords: Current sensor, Fermalloy, Silicon steel 1. 서론 현재 전류센서 시장은 산업용 설비 장비의 컨트롤이나 전원부의 과전류 차단시스템에 안전성 확보를 목적으로 한 기기 및시스템용 전류센서를 사용하는 것이 대부분이지만, 향후의 시장은 어느 한 분야에만 사용되는 응용부품이 아니라 모든 산업의전력을 측정하는 분야에 활용할 수 있는 응용기기부품이다.전류센서를 주로 사용하고 있는 분야를 본다면, 산업 설비 분야, 전력 시설 설비 분야용, 차재 전류센서분야로 나눌 수 있다.산업 설비 분야에서 전류센서 적용 분야는 용접기, 전원공급장치, 무정전전원장치(UPS), 공작기계, 로봇, 전철 등에서 주로 사용되고 있다. 전력 설비 분야에는 에너지 생산을 목적 한 장비및 시스템용이나, 대체 에너지 개발로 인한 세계적인 태양광, 풍력 발전 시스템의 전원 분배로 생산되어진 전력을 최대한 절약하면서 사용하고 관리할 수력량계에 있는 전 IT가 접목 되면서수치화된 전력량계의 형태로 발전 되어 가고 있다[1-3].특히, 생산되어진 에너지를 예전에는 만드는 것에만 중점을많이 두었지만 지금은 생산에서 효율, 관리까지 중요성은 높아지고 있으며 응용기기 도입은 사용하고 있는 전력뿐만 아니라사용하고 있지 않는 전력의 중요성을 가지고도 수치화 하여 차단하는 시스템 및 제품을 개발하여 전력의 중요성을 알리고, 개인이 편리하게 관리하여 사용할 수 있도록 응용한 제품들이 출시되어 사용되고 있다[4]. 또한, 차재용 전류센서의 경우는 자동차에 탑재 되었을 경우, 용도에 따라 전류센서의 응용분야가 나뉘게 된다. 차재용 전류센서의 방식은 크게 아날로그 방식과 집적화된 IC를 사용한 방식으로 나뉜다. 아날로그 방식의 회로설계 구현은 어렵지 않으나, 제품 양산면에서 공정이나 단가가 올라가는 단점으로 집적화된 IC를 사용하고 있다[5].일반적인 전류센서는 전류와 전압을 측정하는 응용부품으로피측정 도선에 흐르는 전류를 관통하거나 PCB에 장착하여 흐르는 전류에 비례한 전압이 출력값으로 나타난다. 이러한 전류센서의 전류측정 방법은 크게 전자유도형과 전류자기효과형으로 나눌 수 있다.전자유도형은 전자계의 유도 현상을 이용한 것으로 교류 전류 측정에는 유리하나, 비정형파형 및 직류 전류 파형의 측정에는 별도의 주변 회로를 추가해야 하고 주파수 대비 출력 신호의 비선형성(non-linearity)과 과전류 시에 나타나는 파괴 현상이나타난다[6,7].반면에, 전류자기효과형 전류 센서는 홀 효과(Hall effect) 구동원리 이론을 이용한 변환소자와 자성재료를 조합하는 전자회로가 일체화된 전류 센서로 과전류가 인가되었을 시 비파괴 특

Enhancement of Visibility Using App Image Categorization in Mobile Device

Mobile devices are generally using app images which are artificially designed. Accordingly, this paper presents adjusting device brightness based on app image categorization for enhancing the visibility under various light condition. First, the proposed method performed two prior subjective tests under various lighting conditions for selecting features of app images concerning visibility and for selecting satisfactory range of device brightness for each app image. Then, the relationship between selected features of app image and satisfactory range of device brightness is analyzed. Next, app images are categorized by using two features of average brightness of app image and distribution ratio of advanced colors that are related to satisfaction range of device brightness. Then, optimal device brightness for each category is selected by having the maximum frequency of satisfaction device brightness. Experimental results show that the categorized app images with optimal device brightness have high satisfaction ratio under various light conditions.

Moire Reduction in Digital Still Camera by Using Inflection Point in Frequency Domain

Digital still camera generally uses optical low-pass filter(OLPF) to enhance its image quality because it removes high spatial frequencies causing aliasing. However, the use of OLPF causes some loss of detail. On the other hand, when image are captured by using no OLPF, the moir is generally existed in high spatial frequency region of an image. Therefore, in this paper, moir reduction method in case of using no OLPF is suggested. To detect the moir, spatial frequency response(SFR) of camera was firstly analyzed by using ISO 12233 resolution chart. Then, moir region is detected by using the patterns that are related to the SFR of camera. next, this region is analysed in the frequency domain. Then, the moir is reduced by removing its frequency component, which represents inflection point between high frequency and DC components. Through the experimental results, it is shown that the proposed method can achieve moir reduction with preserving the detail.