• Journal of Mechanical Science and Technology
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• v.17 no.11
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• pp.1704-1713
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• 2003

Point contact film thickness in elastohydrodynamic lubrication (EHL) is analyzed by image processing method for the images from an optical interferometer with monochromatic incident light. Interference between the reflected lights both on half mirror Cr coating of glass disk and on super finished ball makes circular fringes depending on the contact conditions such as sliding velocity, applied load, viscosity-pressure characteristics and viscosity of lubricant under ambient pressure. In this situation the film thickness is regarded as the difference of optical paths between those reflected lights, which make dark and bright fringes with monochromatic incident light. The film thickness is computed by numbering the dark and bright fringe orders and the intensity (gray scale image) in each fringe regime is mapped to the corresponding film thickness. In this work, we developed a measuring technique for EHL film thickness by dividing the image patterns into two typical types under the condition of monochromatic incident light. During the image processing, the captured image is converted into digitally formatted data over the contact area without any loss of the image information of interferogram and it is also interpreted with consistency regardless of the observer's experimental experience. It is expected that the developed image processing method will provide a valuable basis to develop the image processing technique for color fringes, which is generally used for the measurement of relatively thin films in higher resolution.

• Journal of information and communication convergence engineering
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• v.5 no.1
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• pp.50-53
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• 2007

This paper presents face features detection and a new physiological neuro-fuzzy learning method by using two-dimensional variances based on variation of gray level and by learning for a statistical distribution of the detected face features. This paper reports a method to learn by not using partial face image but using global face image. Face detection process of this method is performed by describing differences of variance change between edge region and stationary region by gray-scale variation of global face having featured regions including nose, mouse, and couple of eyes. To process the learning stage, we use the input layer obtained by statistical distribution of the featured regions for performing the new physiological neuro-fuzzy algorithm.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.8
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• pp.108-114
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• 2003

This research describes a image-processing technique for the scratch detecting algorithm for ITO coated glass. We use the modified logical thresholding method (called adaptive logical thresholding method) for binarization of gray-scale glass image. This method is useful to the algorithm for detecting the scratch of ITO coated glass automatically without need of any prior information of manual fine-tuning of parameters.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.1
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• pp.454-475
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• 2018

This paper presents a novel Android malware classification model planned to classify and categorize Android malicious code at Drebin dataset. The amount of malicious mobile application targeting Android based smartphones has increased rapidly. In this paper, Restricted Boltzmann Machine and Deep Belief Network are used to classify malware into families of Android application. A texture-fingerprint based approach is proposed to extract or detect the feature of malware content. A malware has a unique "image texture" in feature spatial relations. The method uses information on texture image extracted from malicious or benign code, which are mapped to uncompressed gray-scale according to the texture image-based approach. By studying and extracting the implicit features of the API call from a large number of training samples, we get the original dynamic activity features sets. In order to improve the accuracy of classification algorithm on the features selection, on the basis of which, it combines the implicit features of the texture image and API call in malicious code, to train Restricted Boltzmann Machine and Back Propagation. In an evaluation with different malware and benign samples, the experimental results suggest that the usability of this method---using Deep Belief Network to classify Android malware by their texture images and API calls, it detects more than 94% of the malware with few false alarms. Which is higher than shallow machine learning algorithm clearly.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.38 no.1
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• pp.1-10
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• 2001

In order to generate the gray scale image by the binary state imaging devices such as a digital printer, the gray scale image needs to be converted into the binary image by the halftoning techniques. This paper presents a new error diffusion technique to achieve the homogeneous dot distributions on the binary images. In this paper,'the minimum pixel distance'from the current pixel under binarization to the nearest minor pixel is defined first. Also, the gray levels of the input image are converted into a new variable based on the principal distance for the error diffusion. In the proposed method, the difference in the principal distances is utilized for the error propagation, whereas the gray level difference due to the binarization is diffused to the neighboring pixels in the existing error diffusion techniques. The quantization is accomplished by comparing the updated principal distance with the minimum pixel distance. In order to calculate the minimum pixel distance, MPOA(Minor Pixel Offset Array) is employed to reduce the computational loads and memory resources.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.5
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• pp.118-126
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• 1996

In object analysis by image processing, finding lines plays a universal role. And these lines can be easily found by detecting ridges and valleys in digital gray scale images. In this paper, a new method of detecting ridges and valleys by using local min/max operations was presented. This method detects ridges and valleys of desired width by using erosion and dilation properties of local min/max operations, and requires no information of ridge or valley direction. Therefore the method is efficient and computationally simple in comparision with the conventional analytical method.

• 한국정보디스플레이학회:학술대회논문집
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• 2002.08a
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• pp.115-117
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• 2002

In order to realize a stable rubbing process for the liquid crystal panel, the authors investigated the quantitative evaluation method of rubbing process uniformity. The proposed method focuses on the relationship between the image quality of the LCD panel for gray scale images and the rubbing uniformity. The proposed method indicates rubbing uniformity using quantitative parameters of spots and hairlines on the LCD panel.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.52 no.3
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• pp.113-120
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• 2003

Plasma display panels normally utilize the binary coded light emission scheme for gray scale expression. Subsequently, this expression method makes dynamic false contours. We propose the "E3DSM(enhanced 3-dimension scattering method)" that improved existing 3-d scattering method and the "HAM(histogram analysis method)" that is decided the driving schemes and subfield selections with histograms of images. Simulation results show the improving image quality.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.5
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• pp.557-564
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• 2017

The purpose of the thermal imaging observation device mounted on the K's tank in the Republic of Korea military is to convert infrared rays into visual information to provide information about the environment under conditions of restricted visibility. Among the various performance indicators of thermal observation devices, such as the view, magnification, resolution, MTF, NETD, and Minimum Resolvable Temperature Difference (MRTD), the MRTD is the most important, because it can indicate both the spatial frequency and temperature resolvable. However, the standard method of measuring the MRTD in NATO contains many subjective factors. As the measurement result can vary depending on subjective factors such as the human eye, metal condition and measurement conditions, the MRTD obtained is not stable. In this study, these qualitative MRTD measurement systems are converted into quantitative indicators based on a gray scale using imaging processing. By converting the average of the gray scale differences of the black and white images into the MRTD, the mean values can be used to determine whether the performance requirements required by the defense specification are met. The (mean) value can also be used to discriminate between detection, recognition and identification and the detectable distance of the thermal equipment can be analyzed under various environmental conditions, such as altostratus, heavy rain and fog.

• Journal of Digital Convergence
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• v.11 no.8
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• pp.191-197
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• 2013

In this paper, we implemented a paper currency recognition system using IR(infrared) sensor. The system has 32 channel IR sensor to measure the reflection and penetration quantity of light. The IR image of paper currency of 10-bit gray scale is used to differentiate the real and counterfeit paper currency with image information from 0 to 4095. The characteristics of IR image are recognized by brightness and darkness and the positions of bright and dark portions are different between real and counterfeit paper currency. The price of IR sensors were relatively high, however, it is good price in these days due to mass production to apply to counterfeit detection area. We used a software table having the IR characteristics of real paper currency to compare with the IR images of the input paper currency. The performance of the implemented system shows 1-2% error rates for Euro real paper currency and 0% error rates for various counterfeit paper currencies of several countries.