• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.6C
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• pp.465-474
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• 2005

In this paper, a directional reconstruction of lost block in image over noisy channel is presented. DCT coefficients or pixel values in the lost blocks are recovered by using the linear interpolation with available neighboring blocks that are adaptively selected by the directional measure that are composed of the DDC (Difference of DC opposite blocks)and SAC(Similarity of AC opposite blocks) between opposite blocks around lost blocks. The proposed directional recovery method is effective for the strong edge and texture regions because we do not make use of the fixed 4-neighboring blocks but exploit the varying neighboring blocks adaptively by the directional information in the local image. In this paper, we describe the novel directional measure(CDS: Combination of DDC and SAC) composed of the DDC and the SAC and select the usable block to recover the lost block with the directional measure. The proposed method shows about 0.6dB PSNR improvement in average compared to the conventional methods.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.6
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• pp.621-628
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• 2015

This paper describes an algorithm for detecting vehicles detection in real time. The proposed algorithm has the technique based on computer vision and image processing. In real, complex environment such as one with road traffic, many algorithms have great difficulty such as low detection rate and increasing computational time due to complex backgrounds and rapid changes. To overcome this problem in this paper, the proposed algorithm consists of the following methods. First, to effectively separate the candidate regions, we use vertical and horizontal edge information, and shadow values from input image sequences. Second, we extracts features by using HOG from the selected candidate regions. Finally, this paper uses the OS fuzzy-ELM based on SLFN to classify the extracted features. The experimental results show that the proposed method perform well for detecting vehicles and improves the accuracy and the computational time of detecting.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.5
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• pp.1055-1063
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• 2012

The noise of image is added by many environmental factors. Therefore, we need to remove these noises using the conventional filtering methods, which are optimized based on the statistical characteristic of noise. In direct restoration method, there is an inverse filter and the wiener filter. Here, the wiener filter is the optimized filter in the view of least square method. First, we are going to study the inverse filter, wiener filter, constraint least square filter. Second, in order to control the quantity, we use the parameters instead of the power spectrum ratio. But, these parameters have the conflicting condition, therefore, we optimized the variables using parametric wiener filter which adjust the application appropriately. In the simulation results, the contrast of the degraded image was enhanced and the noise was removed. Comparative experimentation was demonstrated edge preserving and noise removing property.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.12
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• pp.2547-2554
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• 2009

In this paper, we proposed a face detection algorithm and a hardware implementation method for ROI(Region Of Interest) of AF(Auto Focusing). We used face features in skin regions of YCbCr color space for face detection. The face features are the number of skin pixels in face regions, edge pixels in eye regions, and shadow pixels in lip regions. The each feature was statistically selected by 2,000 sample pictures of face. The proposed algorithm detects two faces that are closer center of the image for considering the effectiveness of hardware resource. The detected faces are displayed by rectangle for ROI of AF, and the rectangles are represented by positions in the image about starting point and ending point of the rectangles. The proposed face detection method was verified by using FPGA boards and mobile phone camera sensor.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.8
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• pp.1925-1936
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• 2010

In this paper, multi-legged robot was designed and produced using stable walking pattern algorithm. The robot had embedded camera and wireless communication function and it is possible to recognize both hand posture and obstacles. The algorithm decided moving paths, and recognized and avoided obstacles through Hough Transform using Edge Detection of inputed image from image sensor. The robot can be controlled by hand posture using Mahalanobis Distance and average value of skin's color pixel, which is previously learned in order to decide the destination. The developed system has shown obstacle detection rate of 96% and hand posture recognition rate of 94%.

• Nuclear Engineering and Technology
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• v.48 no.6
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• pp.1330-1337
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• 2016

A visualization test of the fuel-coolant interaction in the Test for Real cOrium Interaction with water (TROI) test facility was carried out. To experimentally simulate the In-Vessel corium Retention (IVR)- External Reactor Vessel Cooling (ERVC) conditions, prototypic corium was released directly into the coolant water without a free fall in a gas phase before making contact with the coolant. Corium (34.39 kg) consisting of uranium oxide and zirconium oxide with a weight ratio of 8:2 was superheated, and 22.54 kg of the 34.39 kg corium was passed through water contained in a transparent interaction vessel. An image of the corium jet behavior in the coolant was taken by a high-speed camera every millisecond. Thermocouple junctions installed in the vertical direction of the coolant were cut sequentially by the falling corium jet. It was clearly observed that the visualization image of the corium jet taken during the fuel-coolant interaction corresponded with the temperature variations in the direction of the falling melt. The corium penetrated through the coolant, and the jet leading edge velocity was 2.0 m/s. Debris smaller than 1 mm was 15% of the total weight of the debris collected after a fuel-coolant interaction test, and the mass median diameter was 2.9 mm.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.9
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• pp.1786-1792
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• 2007

In this paper, we proposed a recognition system for resident registration cards using ART-1 and PCA algorithm. To extract registration numbers and issue date, Sobel mask and median filter are applied first and noise removal follows. From the noise-removed image, horizontal smearing is used to extract the regions, which are binarized with recursive binarization algorithm. After that vortical smearing is applied to restore corrupted lesions, which are mainly due to the horizontal smearing. from the restored image, areas of individual codes are extracted using 4-directional edge following algorithm and face area is extracted by the morphologic characteristics of a registration card. Extracted codes are recognized using ART-1 algorithm and PCA algorithm is used to verify the face. When the proposed method was applied to 25 real registration card images, 323 characters from 325 registration numbers and 166 characters from 167 issue date numbers, were correctly recognized. The verification test with 25 forged images showed that the proposed verification algorithm is robust to detect forgery.

• Smart Media Journal
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• v.8 no.3
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• pp.95-103
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• 2019

The global manufacturing industry has reached the limit to growth due to a long-term recession, the rise of labor cost and raw material. As a solution to these difficulties, we promote the 4th Industry Revolution based on ICT and sensor technology. Following this trend, this paper proposes the design of a model using manufacturing data in the protection film process for smart manufacturing innovation. In the protective film process, the manufacturing data of temperature, pressure, humidity, and motion and thermal image are acquired by various sensors for the raw material blending, stirring, extrusion, and inspection processes. While the acquired manufacturing data is stored in mass storage, A.I. platform provides time-series image analysis and its visualization.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.10
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• pp.1310-1316
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• 2021

Recently, with the development of artificial intelligence and IoT technology, the importance of video processing such as object tracking, medical imaging, and object recognition is increasing. In particular, the noise reduction technology used in the preprocessing process demands the ability to effectively remove noise and maintain detailed features as the importance of system images increases. In this paper, we provide a modified weight filter based on pixel matching in an AWGN environment. The proposed algorithm uses a pixel matching method to maintain high-frequency components in which the pixel value of the image changes significantly, detects areas with highly relevant patterns in the peripheral area, and matches pixels required for output calculation. Classify the values. The final output is obtained by calculating the weight according to the similarity and spatial distance between the matching pixels with the center pixel in order to consider the edge component in the filtering process.

• Korean Journal of Agricultural Science
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• v.47 no.4
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• pp.1109-1122
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• 2020

The aim of this study was to develop a marbling classification and prediction model using small parts of sirloin images based on a deep learning algorithm, namely, a convolutional neural network (CNN). Samples were purchased from a commercial slaughterhouse in Korea, images for each grade were acquired, and the total images (n = 500) were assigned according to their grade number: 1++, 1+, 1, and both 2 & 3. The image acquisition system consists of a DSLR camera with a polarization filter to remove diffusive reflectance and two light sources (55 W). To correct the distorted original images, a radial correction algorithm was implemented. Color images of sirloins of Hanwoo (mixed with feeder cattle, steer, and calf) were divided and sub-images with image sizes of 161 × 161 were made to train the marbling prediction model. In this study, the convolutional neural network (CNN) has four convolution layers and yields prediction results in accordance with marbling grades (1++, 1+, 1, and 2&3). Every single layer uses a rectified linear unit (ReLU) function as an activation function and max-pooling is used for extracting the edge between fat and muscle and reducing the variance of the data. Prediction accuracy was measured using an accuracy and kappa coefficient from a confusion matrix. We summed the prediction of sub-images and determined the total average prediction accuracy. Training accuracy was 100% and the test accuracy was 86%, indicating comparably good performance using the CNN. This study provides classification potential for predicting the marbling grade using color images and a convolutional neural network algorithm.