• Journal of the Institute of Electronics Engineers of Korea SP
• /
• 제46권3호
• /
• pp.143-151
• /
• 2009

In this paper, a new extraction algorithm for artificial structure in 3D cloud points of terrestrial LIDAR is described, considering that various obstacles in terrestrial LIDAR make it difficult to apply conventional algorithms which are designed for air-born LIDAR data. Firstly we use the R, G, B color information from the terrestrial LIDAR data to discriminate among the massive 3D cloud points. Hough transform is then applied to estimate the straight lines that correspond to the target structure. Finally, the structure is extracted by comparing the distance between the estimated line and 3D cloud points. The proposed algorithm is efficient in the sense that it requires the user interaction only when the reference colors are obtained. Computer simulation shows the performance to be quite satisfactory.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 한국정밀공학회 2004년도 추계학술대회 논문집
• /
• pp.908-911
• /
• 2004

In this paper, we describe a robust image processing algorithm to recognize the road lane in real-time. For the real-time processing, a detection area is decided by a lane segment of a previous frame and edges are detected on the basis of the lane width. For the robust driving, the global threshold with the Otsu algorithm is used to get a binary image in a frame. Therefore, reliable edges are obtained from the algorithms suggested in this paper in a short time. Lastly, the lane segment is found by hough transform. We made a RC(Radio Control) car equipped with a vision system and verified these algorithms using the RC Car.

• Journal of Sensor Science and Technology
• /
• 제27권2호
• /
• pp.137-141
• /
• 2018

Detecting the ego-lane of a vehicle (the lane on which the vehicle is currently running) is one of the basic techniques for a smart car. Vision sensing is a widely-used method for the ego-lane detection. Existing studies usually find road lane lines by detecting edge pixels in the image from a vehicle camera, and then connecting the edge pixels using Hough Transform. However, this approach takes rather long processing time, and too many straight lines are often detected resulting in false detections in various road conditions. In this paper, we find the lane lines by scanning only a limited number of horizontal lines within a small image region of interest. The horizontal image line scan replaces the edge detection process of existing methods. Automatic thresholding and spatiotemporal filtering procedures are also proposed in order to make our method reliable. In the experiments using real road images of different conditions, the proposed method resulted in high success rate.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• 제8권1호
• /
• pp.127-135
• /
• 2009

This paper presents a new technique for corner shape based object retrieval from a database. The proposed feature matrix consists of values obtained through a neighborhood operation of detected corners. This results in a significant small size feature matrix compared to the algorithms using color features and thus is computationally very efficient. The corners have been extracted by finding the intersections of the detected lines found using Hough transform. As the affine transformations preserve the co-linearity of points on a line and their intersection properties, the resulting corner features for image retrieval are robust to affine transformations. Furthermore, the corner features are invariant to noise. It is considered that the proposed algorithm will produce good results in combination with other algorithms in a way of incremental verification for similarity.

• Proceedings of the KIEE Conference
• /
• 대한전기학회 2006년도 심포지엄 논문집 정보 및 제어부문
• /
• pp.15-17
• /
• 2006

This paper presents a new global intensity compensation method for extracting moving object in a visual surveillance system by compensating time variant intensity changes of background region. The method that compensates a little changes of intensity due to time variant illumination change and automatic gain control of camera is called global intensity compensation. The proposed method expresses global intensity change with a mapping table to describe complex form of intensity change while the previous method models this global intensity change with a simple function as a straight line. The proposed method builds the mapping table by calculating the cross histogram between two images and then by selecting an initial point for generating the mapping table by using Hough transform applied to the cross histogram image. Then starting from the initial point, the mapping table is generated according to the proposed algorithm based on the assumption that reflects the characteristic of global intensity change. Experimental results show that the proposed method makes the compensation error much smaller than the previous GIC method

• The Journal of Korean Institute of Communications and Information Sciences
• /
• 제18권10호
• /
• pp.1582-1589
• /
• 1993

This paper presents a method of detecting lines and ellipses from the image by using Hough transform(HT) known as a robust line detection method against noise. Lines are first detected by conventional HT. Then points of the lines are eliminated to reduce the calculation cost and to ease detection of ellipses. For ellipse defection, parameters of the ellipse are calculated by Least Square Method using particular points that contain geometric properties of the ellipse, instead of accumlating Hough array. Circles are detected in the same way as ellipse detection.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 대한산업공학회/한국경영과학회 2002년도 춘계공동학술대회
• /
• pp.886-893
• /
• 2002

The standard Hough transform has been dominantly used to detect straight lines in an image. However, massive storage requirement and low precision in estimating line parameters due to the quantization of parameter space are the major drawbacks of the Hough transform technique. In this paper, to overcome the drawbacks, an iterative algorithm based on a self-organizing map is presented. The self-organizing map can be adaptively learned such that image points are clustered by prominent lines. Through the procedure of the algorithm, a set of lines are sequentially detected one at a time. Computational results for synthetically generated images are given. The promise of the algorithm is also demonstrated with its application to two natural images of inserts.

• Journal of Korean Institute of Industrial Engineers
• /
• 제18권2호
• /
• pp.83-97
• /
• 1992

A new approach, to seam tracking for robotic are welding is proposed. In this approach, the weld model is a snapshot image and the acquired image is analyzed and compared to the welding database which contains CAD data, weld positions, weld parameters, etc. This paper presents a fast and robust algorithm for the Hough Transform. This modified Hough Transform(MHT) algorithm uses the least-squares regression analysis method in order to approximate the edge lines more precisely, and leads to a significant reduction in both computation and storage. In comparison with the conventional seam tracking methods, this new approach has the advantages of low cost, continuous welding, and various type welding.

• Journal of Information Display
• /
• 제11권4호
• /
• pp.169-172
• /
• 2010

This paper proposes a multi-touch detection technology that can be applied to large LCDs. To achieve this goal, a set of IR LEDs and sensors was used to construct an IR matrix, and a new algorithm based on Hough transform was applied. This approach reduced the "Ghost" response of the multi-touch detection technology to make it better than other IR touch recognition technologies, and showed robust performance in terms of multi-touch recognition.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 한국정밀공학회 2003년도 춘계학술대회 논문집
• /
• pp.176-180
• /
• 2003

The paper presents an efficient method of extracting line segment in a local map of a robot's surroundings. The local map is composed of 2-D grids that have both the occupancy and orientation probabilities using sonar sensors. To find the shape of an object in a local map from orientation information, the orientations are clustered into several groups according to their values. The line segment is , then, extracted from the clusters based on Hough transform. The proposed technique is illustrated by experiments in an indoor environment.