• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.10
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• pp.1991-2000
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• 1994

In this paper, an alagorithm with six types of classification is suggested for the recognition system of hand-written Korean characters. After thinning process and truncating process for noise redection. The input images are used generalized by $64\times64$ size. The six type classification is composed of preliminary and secondary classification process by using the learning algoritm of multi-layer perceptron. Subblock Hough transform is used as local feature and sampling Hough transform is used as global feature. Experiment is conducted for 1800 characters which is written 31 times per each type by 10 persons. The 90% recognition rate is resulted by the preliminary classification of detection the final consonant and by the secondary classification of detecting the vowels.

• Journal of Biomedical Engineering Research
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• v.15 no.3
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• pp.325-332
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• 1994

In this paper, a method is proposed to detect the endocardial contour of the left ventricle using the Hough transform with a weighted model and edge information from the 2D echocardiogram. The implementation of this method is as follows: first, an approximate model detection algorithm was implemented in order to detect the approximate endocardium model and the model center, then we constructed a weighted model with the detected model. Next, we found automatically the cavity center of the left ventricle performing the Hough transform which used the weighted model, and then we detected the endocardial contour using weighted model and edge image.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.1
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• pp.22-30
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• 2011

Plane detection is very important information for mission-critical of robot in 3D environment. A representative method of plane detection is Hough-transformation. Hough-transformation is robust to noise and makes the accurate plane detection possible. But it demands excessive memory and takes too much processing time. Iterative randomized Hough-transformation has been proposed to overcome these shortcomings. This method doesn't vote all data. It votes only one value of the randomly selected data into the Hough parameter space. This value calculated the value of the parameter of the shape that we want to extract. In Hough parameters space, it is possible to detect accurate plane through detection of repetitive maximum value. A common problem in these methods is that it requires too much computational cost and large number of memory space to find the distribution of mixed multiple planes in parameter space. In this paper, we detect multiple planes only via data sampling using Self Organizing Map method. It does not use conventional methods that include transforming to Hough parameter space, voting and repetitive plane extraction. And it improves the reliability of plane detection through division area searching and planarity evaluation. The proposed method is more accurate and faster than the conventional methods which is demonstrated the experiments in various conditions.

• Journal of IKEEE
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• v.15 no.3
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• pp.255-260
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• 2011

In this paper, we propose a parallelization technique in lane detection by using Hough transform. Hough transform has a weakness that it has a lot computation quantity, because it has to compute ${\rho}$ value in all candidate ${\Theta}$ to be detected in an image. We propose an architecture of parallel processing for this transform in a multi-core environment. The parallel processing has application to Hough transform as well as noise reduction and edge detection. This proposed architecture has 5.17 times improvement in performance compare to the existing algorithm.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.2
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• pp.369-377
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• 2008

Though the Hough transform(HT) is a well-known method for detecting analytical shape represented by a number of free parameters, the basic property of the HT, the one-to-many mapping from an image spare to a Hough space, causes the innate problem, the sensitivity to noise. This basic problem also deteriorates the quality of detected lines and makes the detected line deviated from the real one or generates some bogus, multiple lines where only one real line exists. The size of Hough space also affects the quality of detected lines. In this paper, we analyzed the line distortions in the traditional Hough transform and showed that the distortions are relieved in the edge strength Hough transform(ESHT), which is a modified HT. However the usage of expanded edge and edge strength in ESHT can cause some new line distortions which do not exist in the HT. These new ones can be solved by a proper setting of decreasing and broadening parameter values and the optimal values can be determined only by some pre-determined values. We also illustrated several examples to show the distortion-decreasing property of ESHT.

• Proceedings of the IEEK Conference
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• 2000.11d
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• pp.5-8
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• 2000

In this paper, a technique to estimate the circular object's center and radius under noisy condition is described. The technique is based on Davies'Hough transform approach to circular object location but more robust to noise and faster to estimate the circle by using an area of cumulated points.

• Journal of ILASS-Korea
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• v.6 no.1
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• pp.35-43
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• 2001

Previous studies on image processing techniques for panicle size measurement usually have focused on a single panicle or weakly overlapped particles. In the present work, the image processing algorithm for particle size measurement has been improved to process heavily-overlapped spherical-particle images. The algorithm consists of two steps; detection of boundaries which separate the images of the overlapped panicles from the background and the panicle identification process. For the first step, Sobel operator (using gray-level gradient) and the thinning process was adopted, and compared with the gray-level thresholding method that has been widely adopted. In the second, Hough transform was used. Hough transform is the detection algorithm of parametric curves such as straight lines or circles which can be described by several parameters. To reduce the measurement error, the process of finding the true center was added. The improved algorithm was tested by processing an image frame which contains heavily overlapped spherical panicles. The results showed that both the performances of detecting the overlapped images and separating the panicle from them were improved.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.37 no.3
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• pp.39-47
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• 2000

In this paper, we propose a new method of finding the true length of the line and long axis of the ellipse at the $\theta$=$\theta$o＋ 90$^{\circ}$ and short axis of the ellipse at the $\theta$ = $\theta$o from the Hough transform (HT) results. Through the simulations, we showed that the true length of the line and ellipse could be obtained with 98 % accuracy by using the distance from the maximum envelope to the minimum envelope. To compare the simulation results with the experimental results, we performed optical experiments by using a HT CGH filter. Through the experiments, we showed that our results were very similar to those of the simulation.

• Journal of IKEEE
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• v.20 no.1
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• pp.82-88
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• 2016

The detection rate of Progressive Probability Hough Transform(PPHT) is decreased when a lot of noise components exist due to an unclear or complex original image although it is quite a good algorithm that detects line segments accurately. In order to solve the problem, we propose an improved line detecting algorithm which is robust to noise components and recovers slightly damaged edges. The proposed algorithm is based on PPHT and traces a line segments by pixel and checks of it is straight. It increases the detection rate by reducing the effect of noise components and by recovering edge patterns within a limited pixel size. The proposed algorithm is applied to a lane detection method and the false positive detection rate is decreased by 30% and the line detection rate is increased by 15%.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.674-677
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• 2017

The Hough transform is the most representative algorithm for a straight line detection based on edge pixels. It shows excellent performance in a simple linear image but requires a considerable amount of computation in a noisy or complex image and has a problem of detecting a pseudo straight line easily. In this paper, we propose a straight line detection algorithm to solve the problem of the conventional Hough transform. The proposed algorithm detects the straight line information of edge pixels by using principal component analysis (PCA) before performing Hough transform and performs the Hough transform of the limited slope area in the valid edge pixels based on the detected straight line information of edge pixels. Simulation results show that the proposed algorithm reduces the amount of computation as well as eliminates pseudo straight lines.