• Journal of Korean Institute of Industrial Engineers
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• v.28 no.2
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• pp.162-170
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• 2002

Detecting straight lines in an image is frequently required for various machine vision applications such as restoring CAD drawings from scanned images and object recognition. The standard Hough transform has been dominantly used to that purpose. 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. The algorithm can produce highly precised estimates of line parameters using very small amount of storage memory. 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.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.5
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• pp.1051-1058
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• 2018

This paper propose image processing algorithm to prevent safety accidents near by subway platform screen door(PSD). First, edges of the subway PSD images are detected and the boundary line between PSD and subway platform is detected to decide people's approach to the PSD using Hough transform. To do this, we draw the boundary line between the PSD and platform, to detect the boundary line and to decide the people's approach to the detected line is completely connected or not. Generally, edge is the basic characteristic of image; thus, edge detection is very important in image processing applications and computer vision area. The conventional edge detection methods such as Roberts, Sobel, Prewitt, and Laplacian etc, which are using a fixed value of mask, and morphological gradient from the structuring element of view and Canny edge detector are widely used. In this paper, we propose the detection algorithm about the people's approach to the subway PSD to prevent the safety accidents by using Canny edge detector and Hough transform and the computer simulation shows the results.

• Proceedings of the KIEE Conference
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• 1998.07g
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• pp.2234-2236
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• 1998

In this paper, we propose a new algorithm based on multi-resolution application of the parameter space to the Hough transform technique. The existing Hough transform technique employs mapping of fixed parameter space in order to extract straight lines from image. One of the difficulties of the existing Hough transform technique lies in the detection of multiple adjacent lines for only one line. Increasing the parameter space from the low level resolution to the high level resolution, our algorithm detects straight line in a stable and efficient fashion. Experimental results are included to verity the performance of proposed algorithm.

• Journal of Korea Multimedia Society
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• v.16 no.11
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• pp.1288-1296
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• 2013

The prominent line at the singe image is the important fact for understanding spatial structure or estimating aesthetic scoring. According to this thesis, the abstraction of the background line helps analyzing vanishing point, reconstitution of 3 dimensions, and determining of image sloppiness. It also makes easy to calculate the rule of thirds. This thesis is composed of section hough transform mapping, prioritizing of the prominent line, and selection of the prominent line. These technologies are departmentalized to be applied abstraction of traffic lane, analyzing of building structure, abstraction of vanishing point, and abstraction of straight line documentation. This gives the choice that users are able to compose technology by considering characteristic of objects and luminous environment. This thesis also can be applied to abstract circle. The interval hough transform is able to select the number of prominent line which users want to abstract. It can analyze important prominent line numbers at the image and then abstract the lines, too. Results of prominent lines by experiments would be show at this thesis.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.9
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• pp.2203-2208
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• 2014

The Hough transform (HT), that is a typical algorithm for detecting lines in images, needs considerable computational costs and easily detects pseudo-lines on the real world images, because of the large amount of features generated by their complex background or noise. This paper proposes an improved HT that add a preprocessing to estimate the validity of features to the conventional HT. The feature estimation can remove a lot of inessential features for the line detection using a pattern of $3{\times}3$ block features. Experiments using various images show that the proposed algorithm saves computational costs by removing 14%~58% of features depending on images and besides it is superior to the conventional HT in valid line detection.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.99.1-99
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• 2002

In this paper, a computer-vision based pipe-inspection algorithm is developed. The algorithm uses the modified Hough transformation and a line-scanning approach to identify the edge line and radius of the pipe image, from which the eccentricity and dimension of the pipe-end is calculated. Line and circle detection was performed using Laplacian operator with input image which are acquired from the front and side cameras. In order to minimize the memory usage and the processing time, a clustering method with the modified Hough transformation for line detection. The dimension of inner and outer radius of pipe is calculated by proposed line-scanning method. The method scans several lines along t...

• Journal of the Computational Structural Engineering Institute of Korea
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• v.32 no.4
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• pp.265-272
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• 2019

In this paper, a vision-based deep learning algorithm and image processing method are proposed to detect bolt-loosening in steel connections. To achieve this objective, the following approaches are implemented. First, a bolt-loosening detection method that includes regional convolutional neural network(RCNN)-based deep learning algorithm and Hough line transform(HLT)-based image processing algorithm are designed. The RCNN-based deep learning algorithm is developed to identify and crop bolts in a connection image. The HLT-based image processing algorithm is designed to estimate the bolt angles from the cropped bolt images. Then, the proposed vision-based method is evaluated for verifying bolt-loosening detection in a lab-scale girder connection. The accuracy of the RCNN-based bolt detector and HLT-based bolt angle estimator are examined with respect to various perspective distortions.

• The Journal of Korea Robotics Society
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• v.9 no.3
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• pp.140-146
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• 2014

This paper introduces a dual-arm robot painter system which is capable of sketching a camera-captured image with short line segments. To express various curved edges in the image by combining line segments, we first apply edge detection algorithm to the entire image, split the edged image into small boxed pieces, and then apply Hough Transformation to each piece so that the edges inside the piece can be approximated with short line segments. To draw the picture within a reasonable time, we designed a simple dual-arm robot system and controlled both arms concurrently according to linear interpolation algorithm. From the experiments, we could verify that simple linear motions can describe various images effectively with a unique brush style.

• Proceedings of the IEEK Conference
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• 2000.09a
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• pp.675-678
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• 2000

This paper presents a parametric line equation on the log-polar mapped plane to detect the straight lines in an original image. The log-polar edge image used in Hough transform is constructed by combining the edge images of both fovea and periphery. The foveal edge image detected by a Sobel mask on the Cartesian plane is transformed to the log-polar plane by forward mapping but the edge detection of the peripheral region is obtained by directly applying the newly developed mask to the log-polar plane. This paper also proposes a analytic method then determining a border between the fovea and the periphery regions.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.5
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• pp.42-47
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• 2017

In the distribution of digital image, the median filtering is used for a forgery. This paper proposed the algorithm of a image forensics detection for the classification of median filtering. For the solution of this grave problem, the feature vector is composed of 42-Dim. The detected quantity 32, 64 and 128 of forgery image edges, respectively, which are processed by the Hough transform, then it extracted from the start-end point coordinates of the Hough Lines. Also, the Hough Peaks of the Angle-Distance plane are extracted. Subsequently, both of the feature vectors are composed of the proposed scheme. The defined 42-Dim. feature vector is trained in SVM (Support Vector Machine) classifier for the MF classification of the forged images. The experimental results of the proposed MF detection algorithm is compared between the 10-Dim. MFR and the 686-Dim. SPAM. It confirmed that the MF forensic classification ratio of the evaluated performance is 99% above with the whole test image types: the unaltered, the average filtering ($3{\times}3$), the JPEG (QF=90 and 70)) compression, the Gaussian filtered ($3{\times}3$ and $5{\times}5$) images, respectively.