• Journal of the Korean Society of Manufacturing Process Engineers
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• v.8 no.2
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• pp.25-31
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• 2009

The image processing methods are widely used in many industrial fields to detect defections in inspection devices. In this study an image processing method was conducted for the detection of abnormal pixels in a OLED(Organic Light Emitting Diode) type panel which is used for small size displays. The display quality of an OLED device is dependent on the pixel formation quality. So, among the so many pixels, to find out the faulty pixels is very important task in manufacturing processing or inspection division. We used a line scanning type BW(Black & White) camera which has very high resolution characteristics to acquire an image of display pixel patterns. And the various faulty cases in pixel abnormal patterns are considered to detect abnormal pixels. From the results of the research, the normal BW pixel image could be restored to its original color pixel.

• Proceedings of the KIEE Conference
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• 1995.11a
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• pp.225-227
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• 1995

An object detection algorithm using a modified IDM(Image Differential Method) is proposed for detecting an object in a level crossing area. The conventional object detection method using LASER light has the deadzone that it cannot detect small objects, while the object detection method using image data in a level crossing area can detect such small objects. But the image data in a level crossing area can be changeable easily because the data is outdoor and sensitive to such surrounding environments as the change of the sun beam, the shadow of cars, and so on. So we resolve these problems by adding the normalization and the process for shadow of the image data in a level crossing area to the basic IDM(Image Differential Method).

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.3
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• pp.199-205
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• 2011

This paper presents an algorithm capable of detecting free space for the autonomous vehicle navigation. The algorithm consists of two main steps: 1) estimation of longitudinal profile of road, 2) detection of free space. The estimation of longitudinal profile of road is detection of v-line in v-disparity image which is corresponded to road slope, using v-disparity image and hough transform, Dijkstra algorithm. To detect free space, we detect u-line in u-disparity image which is a boundary line between free space and obstacle's region, using u-disparity image and dynamic programming. Free space is decided by detected v-line and u-line. The proposed algorithm is proven to be successful through experiments under various traffic scenarios.

• Journal of the Institute of Electronics and Information Engineers
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• v.49 no.9
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• pp.113-127
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• 2012

Face detection methods using image processing have been proposed in many different ways. Generally, the most widely used method for face detection is an Adaboost that is proposed by Viola and Jones. This method uses Haar-like feature for image learning, and the detection performance depends on the learned images. It is well performed to detect face images within a certain distance range, but if the image is far away from the camera, face images become so small that may not detect them with the pre-learned Haar-like feature of the face image. In this paper, we propose the far distance face detection method that combine the Aadaboost of Viola-Jones with a saliency map and user's attention information. Saliency Map is used to select the candidate face images in the input image, face images are finally detected among the candidated regions using the Adaboost with Haar-like feature learned in advance. And the user's eye-tracking information is used to select the interest regions. When a subject is so far away from the camera that it is difficult to detect the face image, we expand the small eye gaze spot region using linear interpolation method and reuse that as input image and can increase the face image detection performance. We confirmed the proposed model has better results than the conventional Adaboost in terms of face image detection performance and computational time.

• Journal of Biosystems Engineering
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• v.39 no.3
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• pp.166-173
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• 2014

Purpose: In this study, we focused on the image processing method to determine the external quality of Fuji apples by identifying surface defects such as scabs and bruises. Method: A CCD camera was used to capture filter images with 24 different wavelengths ranging between 530 nm and 1050 nm. Image subtraction and division operations were performed to distinguish the defect area from the normal areas including calyx, stem, and glaring on the apple surface image. All threshold values of the image were examined to reveal the defect area of pretreated filter images. Results: The developed operation methods were [image (720 nm) - image (900 nm)]/image (700 nm) for bruise detection and [image (740 nm) - image (900 nm)]/image (590 nm) for scab detection, which revealed 81% and 90% recognition ratios, respectively. Conclusions: Our results showed several optimal wavelengths and image processing methods to detect Fuji apple surface defects such as bruises and scabs.

• Journal of the Korean Society of Marine Environment & Safety
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• v.26 no.6
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• pp.601-605
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• 2020

In large ships with complex structures, it is difficult to locate workers. In particular, it is not easy to detect when a worker falls down, making it difficult to respond quickly. Thus, research is being conducted to detect fallen workers using a camera or by attaching a device to the body. Existing image-based fall detection systems have been designed to detect a person's body parts; hence, it is difficult to detect them in various ships and postures. In this study, the entire fall area was extracted and deep learning was used to detect the fallen shipworker based on the image. The data necessary for learning were obtained by recording falling states at the shipyard. The amount of learning data was augmented by flipping, resizing, and rotating the image. Performance evaluation was conducted with precision, reproducibility, accuracy, and a low error rate. The larger the amount of data, the better the precision. In the future, reinforcing various data is expected to improve the effectiveness of camera-based fall detection models, and thus improve safety.

• Journal of the Korean Institute of Telematics and Electronics T
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• v.35T no.2
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• pp.28-34
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• 1998

In this paper, I'd like to make a suggestion for boundary line detect algorithm which is used 3-D image processing system in order to track moving object. Through this study, more than anything else, difference image method was adopted to detect moving object in input image. To detect moving object, I made use of detect windows constructed by 4's predictive areas and object area for the purpose of reducing processing time and its size was determined by the size of moving object and prediction parameter directed center position. And also, tracking camera was movable toward the direction of X, Y by DC motor. As a conclusion of the study proposed algorithm, I found out the following results that tracking error was less than 6% of total moving object size and maximum tracking time 2 seconds by toy-car simulation.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.130-133
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• 2005

This paper presents the efficient method to detect the defective detectors of the SWIR band of SPOT 4. The key of this method are to flatten the baseline of the data using high pass band filter instead of differentiation. This method is made up six steps. First step is to apply image enhancement techniques to enhance the lines imaged by defective detector and improve the quality of an image. Second step is processed by summing the enhanced image in line direction. These summed data have the peaks that represent the defective detectors and the curved baseline characterized by the reflectivity of Earth surface. In order to exactly detect these peaks, third step is to flatten the curved baseline using high pass filtering in the frequency domain. In fourth step, the data with flat baseline is normalized to have zero mean and unity standard deviation. In fifth step, the defective detectors are detected using $99.9\%$ confidence interval. Finally, after removing the detected ones in summed data, the steps from third to five are iterated. Three SPOT 4 images, which have different reflectivity of Earth surface and different sensor, were used to validate this method. The overall accuracy of detection for three images was $97.9\%$. This result shows that this method can detect efficiently the lines made by defective detectors.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.5
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• pp.226-232
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• 2001

The ability to efficiently and robustly recover accurate 3D terrain models from sets of stereoscopic images is important to many civilian and military applications. To develop an effective and practical terrain modeling system, we propose a new multi-image stereo method which detect unreliable elevations in DEM(Digital Elevation Map), and fuse several DEMs from multiple sources into an accurate and reliable results. This paper focuses on two key factors for generating robust 3D terrain models: the ability to detect unreliable elevation estimates and the ability to fuse the reliable elevations into a single optimal terrain model. We apply the self-consistency methodology to reconstruct accurate DEM from multi-image and show the method is more effective than the conventional stereo image 3D reconstruction method. Using photo-realistic simulator, four synthetic image are generated from ground truth DEM and orthoimage to evaluate the accuracy of the proposed method quantitatively.

• Journal of information and communication convergence engineering
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• v.9 no.3
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• pp.331-335
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• 2011

Digital images are often corrupted by impulse noise, and it is very important to remove random-valued impulse noise. Cleaning such noise is far more difficult than cleaning salt and pepper impulse noise. In this paper, we proposed an efficient way to remove random-valued impulse noise from digital images. This novel method comprises two stages. The first stage is to detect the random-valued impulse noise in the image and the pixels are roughly divided into two classes, which are "noise-free pixel" and "noise pixel". Then, the second stage is to eliminate the random-valued impulse noise from the image. In this stage, only the "noise pixels" are processed. The "noise-free pixels" are copied directly to the output image. Simulation results indicated that our method provides a significant improvement over many other existing algorithms.