• Journal of the Korea Society of Computer and Information
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• v.26 no.3
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• pp.51-57
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• 2021

In this paper, we propose a method to find the DoF(Depth of field) that is blurred in an image by focusing and out-focusing the camera through a efficient convolutional neural network. Our approach uses the RGB channel-based cross-correlation filter to efficiently classify the DoF region from the image and build data for learning in the convolutional neural network. A data pair of the training data is established between the image and the DoF weighted map. Data used for learning uses DoF weight maps extracted by cross-correlation filters, and uses the result of applying the smoothing process to increase the convergence rate in the network learning stage. The DoF weighted image obtained as the test result stably finds the DoF region in the input image. As a result, the proposed method can be used in various places such as NPR(Non-photorealistic rendering) rendering and object detection by using the DoF area as the user's ROI(Region of interest).

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.10
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• pp.1453-1461
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• 2022

The accuracy of peripheral object recognition algorithms using 3D data sensors such as LiDAR in autonomous vehicles has been increasing through many studies, but this requires high performance hardware and complex structures. This object recognition algorithm acts as a large load on the main processor of an autonomous vehicle that requires performing and managing many processors while driving. To reduce this load and simultaneously exploit the advantages of 3D sensor data, we propose 2D data-based recognition using the ROI generated by extracting physical properties from 3D sensor data. In the environment where the brightness value was reduced by 50% in the basic image, it showed 5.3% higher accuracy and 28.57% lower performance time than the existing 2D-based model. Instead of having a 2.46 percent lower accuracy than the 3D-based model in the base image, it has a 6.25 percent reduction in performance time.

• Journal of Advanced Navigation Technology
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• v.17 no.1
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• pp.90-97
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• 2013

Recently, intelligent transportation system based on image processing has been developed. In this paper, we propose a collision detection algorithm based on the analysis of motion vectors and the staying periods of vehicles in intersections. Objects in the region of interest are extracted from the subtraction image between background images based on Gaussian mixture model and input images. Collisions and traffic jams are detected by analysing measured motion vectors of vehicles and their staying periods in intersections. Experiments are performed on video sequences actually recoded at intersections. Correct detection rate and false alarm rate are 85.7% and 7.7%, respectively.

• Journal of Korea Multimedia Society
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• v.19 no.6
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• pp.1003-1013
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• 2016

This paper proposes a framework of superpixel-based vehicle detection method using plane normal vector in disparity space. We utilize two common factors for detecting vehicles: Hypothesis Generation (HG) and Hypothesis Verification (HV). At the stage of HG, we set the regions of interest (ROI) by estimating the lane, and track them to reduce computational cost of the overall processes. The image is then divided into compact superpixels, each of which is viewed as a plane composed of the normal vector in disparity space. After that, the representative normal vector is computed at a superpixel-level, which alleviates the well-known problems of conventional color-based and depth-based approaches. Based on the assumption that the central-bottom of the input image is always on the navigable region, the road and obstacle candidates are simultaneously extracted by the plane normal vectors obtained from K-means algorithm. At the stage of HV, the separated obstacle candidates are verified by employing HOG and SVM as for a feature and classifying function, respectively. To achieve this, we trained SVM classifier by HOG features of KITTI training dataset. The experimental results demonstrate that the proposed vehicle detection system outperforms the conventional HOG-based methods qualitatively and quantitatively.

• The Journal of the Korea Contents Association
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• v.4 no.4
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• pp.79-87
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• 2004

In this paper, we propose a probability map based genetic algorithm to detect lips from portrait image. The existing genetic algorithm used to get an optimal solution is modified in order to get multiple optimal solutions for lips detection. Each individual consists of two chromosomes to represent coordinates x, y in space. Also the algorithm introduce a preserving zone in the population, a modified uniform crossover, a selection without individual duplication. Using probability map of H, 5 components, the proposed algorithm has adaptability in the segmentation of objects with similar colors. In experiments, we analyzed relationships of primary parameters and found that the algorithm can apply to the detection of other ROIs easily

• Journal of the Korea Society of Computer and Information
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• v.11 no.5 s.43
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• pp.329-336
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• 2006

This paper describes a lane change assistance system for the help of safe lane change, which detects vehicles approaching from the rear side by using a computer vision algorithm and notifies the possibility of safe lane change to a driver. In case a driver tries to lane change, the proposed system can detect vehicles and keep track of them. After detecting side lane lines, region of interest for vehicle detection is decided. For detection a vehicle, optical flow technique is applied. The experimental result of the proposed algorithm and system showed that the vehicle detection rate was 91% and the embedded system would have application to a lane change assistance system being commercialized in the near future.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.7
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• pp.621-626
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• 2015

This paper presents a camera calibration method in order to estimate the lane detection and inter-vehicle distance estimation system for an automotive safety driving system. In order to implement the lane detection and vision-based inter-vehicle distance estimation to the embedded navigations or black box systems, it is necessary to consider the computation time and algorithm complexity. The process of camera calibration estimates the horizon, the position of the car's hood and the lane width for extraction of region of interest (ROI) from input image sequences. The precision of the calibration method is very important to the lane detection and inter-vehicle distance estimation. The proposed calibration method consists of three main steps: 1) horizon area determination; 2) estimation of the car's hood area; and 3) estimation of initial lane width. Various experimental results show the effectiveness of the proposed method.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.1
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• pp.9-14
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• 2017

Embedded devices with the development of modern computer technology also began equipped with a variety of functions. In this study, to provide a method of tracking efficient face with a small instrument of resources, such as built-in equipment that uses an image sensor in recent years has been actively carried out. It uses a face detection method using the features of the MB-LBP in order to obtain an accurate face, specify the region (Region of Interest) around the face when the face detection for the face object tracking in the next video did. And in the video can not be detected faces, to track objects using the CAM-Shift key is a conventional object tracking method, which make it possible to retain the information without loss of object information. In this study, through the comparison with the previous studies, it was confirmed the precision and high-speed performance of the object tracking system.

• Journal of Biomedical Engineering Research
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• v.27 no.5
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• pp.291-299
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• 2006

A Computer-Aided Diagnosis system has been examined to reduce the effort of radiologist. In this paper, we propose the algorithm using Support Vector Machine(SVM) classifier to discriminate whether microcalcifications are malignant or benign tumors. The proposed method to detect microcalcifications is composed of two detection steps each of which uses SVM classifier. The coarse detection step finds out pixels considered high contrasts comparing with neighboring pixels. Then, Region of Interest(ROI) is generated based on microcalcification characteristics. The fine detection step determines whether the found ROIs are microcalcifications or not by merging potential regions using obtained ROIs and SVM classifier. The proposed method is specified on Korean mammogram database. The experimental result of the proposed algorithm presents robustness in detecting microcalcifications than the previous method using Artificial Neural Network as classifier even when using small training data.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.2
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• pp.97-101
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• 2022

In this paper, we propose a method of measuring Fine dust in images using K-means and Sobel-mask based edge detection techniques using CCTV. The proposed algorithm collects images using a CCTV camera and designates an image range through a region of interest. When clustering is completed by applying the K-means algorithm, outline is detected through Sobel-mask, edge strength is measured, and the concentration of fine dust is determined based on the measured data. The proposed method extracts the contour of the mountain range using the characteristics of Sobel-mask, which has an advantage in diagonal measurement, and shows the difference in detection according to the concentration of fine dust as an experimental result.