• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.1
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• pp.78-84
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• 2015

Depth estimation is often required for robot vision, 3D modeling, and motion control. Previous method is based on the focus measures which are calculated for a series of image by a single camera at different distance between and object. This method, however, has disadvantage of taking a long time for calculating the focus measure since the mask operation is performed for every pixel in the image. In this paper, we estimates the depth by using the focus measure of the boundary pixels located between the objects in order to minimize the depth estimate time. To detect the boundary of an object consisting of a straight line and a circle, we use the Hough transform and estimate the depth by using the focus measure. We performed various experiments for PCB images and obtained more effective depth estimation results than previous ones.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.1
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• pp.129-136
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• 2018

In this paper, we propose a new real-time lane detection method that is efficient for road environment. Existing methods have a problem of low reliability under environmental changes. In order to overcome this problem, we emphasize the lane candidate area by using gray level division. And Extracts a straight line component near the lane by using the Hough transform, and generates an ROI for each straight line based on the extracted coordinates. And integrates the generated ROI images. Then, the lane is determined by dividing the object using the dual queue in the ROI image. The proposed method is able to detect lanes even in the environmental change unlike the conventional method. And It is possible to obtain an advantage that the area corresponding to the background such as sky, mountain, etc. is efficiently removed and high reliability is obtained.

• IEMEK Journal of Embedded Systems and Applications
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• v.5 no.3
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• pp.111-117
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• 2010

The easily accessible handheld devices equipped with camera are widely available as common commodities. According to this trend, utilization of images is popular among common users for various purposes resulting in huge amount of images in local or network based storage systems. In this environment, identification of an image with a solid and effective manner is demanded in behalf of safe distribution and efficient management of images. The generated identifiers can be used as a file name in file systems or an index in image databases utilizing the uniqueness of the identifiers. In this paper, we propose a method that generates image identifiers using linear components in images. Some experiments of generation of identifiers are performed, and the results evaluate that the proposed method has feasible effectiveness.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.9
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• pp.783-789
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• 2001

The purpose of this study is to build and to manage environment models with line segments from the sonar range data on obstacles in unknown and varied environments. The proposed method subsequently employs a two-stage data-transform process in order to extract environmental line segments from the range data on obstacles. In the first stage, the occupancy grid extracted from the range data is accumulated to a two-dimensional local histogram grid. In the second stage, a line histogram extracted from an local histogram gird is based on a Hough transform, and matching is a process of comparing each of the segments in the global line segments map against the line segments to detect similarity in overlap, orientation, and arrangement. Each of these tests is made by comparing one of the parameters in the segment representation. After the tests, new line segments are composed to the global line segments map. The proposed technique is illustrated by experiments in an indoor environment.

• The Journal of Korean Institute of Information Technology
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• v.17 no.9
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• pp.91-97
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• 2019

When a radar tracks the warhead of a ballistic missile, decoys of a ballistic missile put a heavy burden on the radar resource management tracking the targets. To reduce this burden, it is necessary to be able to separate the signal of the warhead from the received dynamic radar cross section (RCS) signal on the radar. In this paper, we propose the method of separating the dynamic RCS of each target from the received signal by the Hough transform which extracts straight lines from the image. The micro motion of the targets was implemented using a 3D CAD model of the warhead and decoys. Then, we calculated the dynamic RCS from the 3D CAD model having micromotion and verified the performance by applying the proposed algorithm. Simulation results show that the proposed method can separate the signals of the warhead and decoys at the signal-to-noise ratio (SNR) of 10dB.

• Proceedings of the KIEE Conference
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• 1999.07g
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• pp.3080-3082
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• 1999

본 논문에서는 실내환경에서 얻어진 격자 지도(grid map)상의 장애물 정보의 표현을 간략화 시키는 방법에 대하여 논하였다. 여기서 기본이 되는 격자 지도의 장애물 정보는 초음파 센서를 이용하여 얻어졌다고 가정하였다. 그리고 생성된 장애물 정보를 직선 성분으로 간단히 표현하여 저장될 정보의 양을 줄이는 변환 방법들인 체인 코드(chain code)와 호프 변환(Hough transform)에 대하여 논하였다. 그리고, 논의된 방법의 유효성을 증명하기 위하여 컴퓨터 시뮬레이션에 의한 결과를 제시하였으며, 단점들에 대한 앞으로의 연구 방향을 제시 하였다.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.1
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• pp.126-136
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• 2014

In the Advanced Driver Assistance Systems (ADAS) of smart vehicle and Intelligent Transportation System (ITS) for to detect the boundary of lane is being studied a lot of Hough Transform. This method detects correctly recognition the lane. But recognition rate can fall due to detecting straight lines outside of the lane. In order to solve this problems, this paper proposed an algorithm to recognize the lane boundaries and the accumulator cells in Hough space. Based on proposed algorithm, we develop application for Android was developed by H/W verification. Users of smart phone devices could use lane detection and lane departure warning systems for driver's safety whenever and wherever. Software verification using the OpenCV showed efficiency recognition correct rate of 93.8% and hardware real-time verification for an application development in the Android phone showed recognition correct rate of 70%.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.11
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• pp.2645-2652
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• 2013

For a variety of vision systems such as car omni-directional surveillance systems and robot vision systems, many cameras have been equipped and used. In order to detect corners of grid pattern in AVM(Around View Monitoring) systems, after the non-linear radial distortion image obtained from wide-angle camera is corrected, corners of grids of the distortion corrected image must be detected. Though there are transformations such as Sub-Pixel and Hough transformation as corner detection methods for AVM systems, it is difficult to achieve automatic detection by Sub-Pixel and accuracy by Hough transformation. Therefore, we showed that the automatic detection proposed in this paper, which detects corners accurately from the distortion corrected image could be applied for AVM systems, by designing and implementing it, and evaluating its performance.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.12
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• pp.2665-2674
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• 2012

This paper propose the method to recognize the lanes and distance between cars in real-time which detects dangerous situations and helps safe driving in the actual road environment. First of all, it extracts the area of interest corresponding to roads and cars from the road image photographed by using the forward-looking camera. Through the hough transform for the area of interest, this study detects linear components and also selects the lane and conducts filtering by calculating probability. And through the shadow threshold analysis of the cars in front within the area of interest, it extracts the objects of cars in front and calculates the distance from cars in front. According to the result of applying the suggested technology to recognize the lane and distance between cars to the road situation for testing, it showed over 95% recognition rate; thus, it has been proved that it can respond to safe driving.

• Journal of IKEEE
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• v.24 no.1
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• pp.147-153
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• 2020

Conventional lane detection algorithms have problems in that the detection rate is lowered in road environments having a large change in curvature and illumination. The probabilistic Hough transform method has low lane detection rate since it exploits edges and restrictive angles. On the other hand, the method using a sliding window can detect a curved lane as the lane is detected by dividing the image into windows. However, the detection rate of this method is affected by road slopes because it uses affine transformation. In order to detect lanes robustly and avoid obstacles, we propose driving assist system using semantic segmentation based on deep learning. The architecture for segmentation is SegNet based on VGG-16. The semantic image segmentation feature can be used to calculate safety space and predict collisions so that we control a vehicle using adaptive-MPC to avoid objects and keep lanes. Simulation results with CARLA show that the proposed algorithm detects lanes robustly and avoids unknown obstacles in front of vehicle.