• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.23 no.2
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• pp.165-175
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• 2005

If the interlaced scan type camera moves while an image is filming from the camera, blur is often created from the misalignment of the two images of even and odd lines. This paper proposed an algorithm which removes the misalignment of the even and odd line images cased by the vibration of the interlaced scan type camera. The blurred original image is separated into the even and the odd line images as half size. Based on these two images, two full sized images are generated using interpolation technique. If a big difference between these two interpolated images is generated, the original image is taken while the camera is moving. In this case, a deblurred image is obtained with the alignment of these separated two images through feature point extraction, feature point matching, sub-pixel matching, outlier detection, and image mosaicking processes. This paper demonstrated that the proposed algorithm can create clear images from blurred images caused by various camera motions.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.5
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• pp.934-940
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• 2003

In this paper, hair beauty fashion design feature points detection system is proposed. A hair models and hair face is represented as a graph where the nodes are placed at facial feature points labeled by their Gabor features and the edges are describes their spatial relations. An innovative flexible feature matching is proposed to perform features correspondence between hair models and the input image. This matching hair model works like random diffusion process in the image space by employing the locally competitive and globally corporative mechanism. The system works nicely on the face images under complicated background. pose variations and distorted by accessories. We demonstrate the benefits of our approach by its implementation on the face identification system.

• Smart Media Journal
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• v.6 no.2
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• pp.33-41
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• 2017

Railway point machines act as actuators that provide different routes to trains by driving switchblades from the current position to the opposite one. Since point failure caused by the aging effect can significantly affect railway operations with potentially disastrous consequences, replacement detection of point machine at an appropriate time is critical. In this paper, we propose a replacement condition detection method of point machine in railway condition monitoring systems using electrical current signals, after analyzing and relabeling domestic in-field replacement data by means of OLAP(On-Line Analytical Processing) operations in the multidimensional data cube into "does-not-need-to-be replaced" and "needs-to-be-replaced" data. The system enables extracting suitable feature vectors from the incoming electrical current signals by DWT(Discrete Wavelet Transform) with reduced feature dimensions using PCA(Principal Components Analysis), and employs SVM(Support Vector Machine) for the real-time replacement detection of point machine. Experimental results with in-field replacement data including points anomalies show that the system could detect the replacement conditions of railway point machines with accuracy exceeding 98%.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1999.10a
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• pp.432-439
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• 1999

In this paper, an off-line programming(OLP) system is presented as the three dimensional graphic simulator and one of the human-robot interface systems for industrial robots. The OLP system has been especially developed to testify robot programs visually using three dimensional geometric modeling and graphics technologies in personal computes. A special feature is its capability of collision detection and of comparing performance of control algorithms. This paper places the focus on the structure and major characteristic of OLP system.

• Journal of the Institute of Convergence Signal Processing
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• v.13 no.4
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• pp.173-179
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• 2012

In this paper, we presents a new method for vehicle number plate detection. Our method is basically the method extracting a rectangles from a car image because the shape of a vehicle number plate is a rectangle. For detecting the vehicle number plate, firstly, the contrast of the input image is enhanced. Then, the lines in the image are obtained by using LSD(line segment detector), and rectangles in the image are detected from the line data. These rectangles are the candidates of the car plate, from which the car plate is selected. In this procedure, the method of detecting rectangles is our proposed method, which consists of three stages: (1) extracting corners from the line segments by LSD; (2) extracting diagonal lines from the corner data; and (3) detecting rectangles from diagonal line information. And finally the vehicle number plate is selected from these rectangles by using the feature of the vehicle number plate and the inside information of rectangles. In the experiments with the 100 images captured by our digital camera, we have achieved a detection rate of 94%.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.5
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• pp.103-110
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• 2008

A reliable extraction of welded area is the precedent task before the detection of weld defects in industrial radiography. This paper describes an attempt to detect and extract the welded features of steel tubes from the computed radiography(CR) images. The statistical properties are first analyzed on over 160 sample radiographic images which represent either weld or non-weld area to identify the differences between them. The analysis is then proceeded by pattern classification to determine the clustering parameters. These parameters are the width, the functional match, and continuity. The observed weld image is processed line by line to calculate these parameters for each flexible moving window in line image pixel set. The local statistic line-clustering method is used as the classifier to recognize each window data as weld or non-weld cluster. The sequential procedure is to track the edge lines between two distinct regions by iterative calculation of threshold, and it results in extracting the weld feature. Our methodology is concluded to be effective after experiment with CR weld images.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 1999.05a
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• pp.169-172
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• 1999

Recently, the wavelet transform has been a new and powerful tool for signal processing. It is more suitable specially for the feature extraction and detection of non-stationary signals than traditional methods such as, the Fourier Transform(FT), the Fast Fourier Transform(FFT) and the Least Square Method etc. because of the characteristic of the multi-scale analysis and time-frequency domain localization. The wavelet transform has been developed for the analysis of PD pulse signal to raise in the progress of insulation degradation. In this paper, the wavelet transform was applied to one foundational method for feature extraction. For the obtain experimental data, a computer-aided partial discharge measurement system with a single acoustic sensor was used. If we are applying to the neural network method the accumulated data through the extracted feature, it is expected that we can detect the PD pulse signal in the insulation materials on the on-line.

• IEMEK Journal of Embedded Systems and Applications
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• v.19 no.1
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• pp.65-71
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• 2024

An indirect visual SLAM takes raw image data and exploits geometric information such as key-points and line edges. Due to various environmental changes, SLAM performance may decrease. The main problem is caused by dynamic objects especially in highly crowded environments. In this paper, we propose a robust feature-based visual SLAM, building on ORB-SLAM, via multi-channel dynamic objects estimation. An optical flow and deep learning-based object detection algorithm each estimate different types of dynamic object information. Proposed method incorporates two dynamic object information and creates multi-channel dynamic masks. In this method, information on actually moving dynamic objects and potential dynamic objects can be obtained. Finally, dynamic objects included in the masks are removed in feature extraction part. As a results, proposed method can obtain more precise camera poses. The superiority of our ORB-SLAM was verified to compared with conventional ORB-SLAM by the experiment using KITTI odometry dataset.

• Journal of Mechanical Science and Technology
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• v.20 no.12
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• pp.2013-2024
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• 2006

This paper proposes a condition classification system using wavelet transform, feature evaluation and artificial neural networks to detect faulty products on the production line of reciprocating compressors for refrigerators. The stationary features of vibration signals are extracted from statistical cumulants of the discrete wavelet coefficients and root mean square values of band-pass frequencies. The neural networks are trained by the sample data, including healthy or faulty compressors. Based on training, the proposed system can be used on the automatic mass production line to classify product quality instead of people inspection. The validity of this system is demonstrated by the on-site test at LG Electronics, Inc. for reciprocating compressors. According to different products, this system after some modification may be useful to increase productivity in different types of production lines.

• Laser Solutions
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• v.3 no.2
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• pp.53-61
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• 2000

On the laser weld production line, a slight alteration of the welding condition produces many defects. The defects are monitored in real time, in order to prevent continuous occurrence of defects, reduce the loss of material, and guarantee good quality. The measurement system is produced by using three photo-diodes for detection of the plasma and spatter signal in CO$_2$ laser welding. For high speed CO$_2$ laser welding, laser tailored welded blanks for example, on-line weld quality monitoring system was developed by using fuzzy multi-feature pattern recognition. Weld qualities were classified optimal heat input, a little low heat input, low heat input, and focus misalignment, and final weld quality were classified good and bad.