• Journal of the Korea Computer Industry Society
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• v.8 no.3
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• pp.155-166
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• 2007

In this paper, captured face-image was pre-processing, segmentation, and extracting features from thinning by differential operator and minute-delineation. A straight line in slope-intercept form was transformed at the $r-\theta$ domain using Hough Transform, instead of Housdorff distance are extract feature as length, rotation, displacement of lines from thinning line components by differentiation. This research proposed a new approach compare with Hough Transformation and Housdorff Distance for face recognition so that Hough transform is simple and fast processing of face recognition than processing by Housdorff Distance. Rcognition accuracy rate is that Housdorff method is higher than Hough transformation's method.

• Proceedings of the IEEK Conference
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• summer
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• pp.274-275
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• 2004

The Hough transform has been used as a tool for line detection. The main idea of the Hough transform is to transform each pixel in the image individually into the parameter domain. In this way, the Hough transform converts a difficult global detection problem in the image domain into a more easily solved local peak detection problem in the parameter domain. In this paper, we show that the discrete Hough transform is identical to the discrete Radon transform. Thus, we can use the generalized Radon transform to handle more general parameterized curve types.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.2
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• pp.406-412
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• 2006

In this paper, we propose OHT(optimized nough Transform) algorithm for the lane extraction. Input image is changed into 256 gray revel image. Gray level image is separated into background region and road region by using limited horizontal projection value. In separated road area, we apply OHT algorithm. OHT algorithm is characterized as follows. First, the number of candidate pixels is reduced using the outline orientation of the lane. Second, each range of the left and right lane is defined by limited ${\theta}$ Experimental results show that the proposed method is better than Hough Transform.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.11
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• pp.2055-2061
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• 2006

The detection of geometric primitives from a digital image is one of the basic tasks in computer vision area and the Hough transform is a well-known method for detecting analytical shape represented by a number of free parameters. However the basic property of the Hough transform, the one-to-many mapping from an image space to a Hough space, causes the innate problem, the sensitivity to noise. In this paper, we proposed Edge Strength Hough Transform which uses edge strength to reduce the sensitivity to noise and proved the insensitivity using the ratio of peaks in a Mough space. We also experimented the proposed method on lines and got small number of peaks in a Hough space compared to traditional Hough transform, which supports the noise insensitivity of the proposed method.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.10a
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• pp.500-503
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• 1997

In this paper, a method for recognizing 3-D objects using the 3-D Hough transform and the robust stereo vision is studied. A 3-D object is recognized through two steps; modeling step and matching step. In modeling step, features of the object are extracted by analyzing the IGES file. In matching step, the values of the sensed image are compared with those of the IGES file which is assumed to location and orientation in the 3-D Hough transform domain. Since we use the 3-D Hough transform domain of the input image directly, the sensitivity to the noise and the high computational complexity could be significantly allcv~ated. Also, the cost efficiency is improved using the robust stereo vision for obtaining depth map image which is needed for 3-D Hough transform. In order lo verify the proposed method, real telephone model is recognized. Thc results of the location and orientation of the model are presented.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.421-424
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• 2022

Hough transform is a representative algorithm for detecting straight lines in an edge image. It corresponds the parameters of straight lines that may occur in the edge pixel into a parameter space, and detects valid parameters satisfying a given condition as straight lines. In general Hough transform, the parameters of the line are calculated with the image origin as the reference point. However, in this paper, the Hough transform based on the image center as a reference point is performed and its performance is compared and analyzed with the conventional Hough transform.

• Proceedings of the Optical Society of Korea Conference
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• 1990.02a
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• pp.121-125
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• 1990

Optical Hough transform technique is introduced to obtain the straight line features in parallel from the input scene images. Experimental results are also provided to demonstrate the advantage of such optical parallel processor over the digital one. Peaks in optical Hough space are free from quantization noise and thus easy to detect.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.5
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• pp.575-581
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• 2007

Though the Hough transform is a well-known method for detecting analytical shape represented by a number of free parameters, the basic property of the Hough transform, the one-to-many mapping from an image space to a Hough space, causes the innate problem, the sensitivity to noise. To remedy this problem, Edge Strength Hough Transform (ESHT) was proposed and proved to reduce the noise sensitivity. However the performance of ESHT depends on the size of a Hough space and image and some other parameters which should be decided experimentally. In this paper, we derived formulae to decide 2 parameter values; decreasing parameter and broadening parameter, which play an important role in ESHT. Using the derived formulae, 2 parameter values can be decided only with the pre-determined values, the size of a Hough space and an image, which make it possible to decide them automatically. The experiments with different parameter values also support the result.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.44 no.6
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• pp.11-20
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• 2007

This paper proposes a new efficient method to detect ellipses using a segment merging based Randomized Hough Transform. The key idea of the proposed method is to separate single line segments from an edge image, to estimate ellipses from any pair of the single line segments using Randomized Hough Transform (RHT), and to merge the ellipses. This algorithm is able to accuracy estimate the number of ellipses and largely improves the computational time by reducing iterations.

• Journal of Korea Society of Digital Industry and Information Management
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• v.10 no.3
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• pp.143-149
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• 2014

This paper is on the blind signal separation(BSS) method by the geometric method. To separate the signal sources, we use Hough transform and BSS. Hough transform is a geometric method which let us know the local informations of the signal. We find the orientations of signals by Hough transform and know the number of signal sources. When the number of sensors is more than the number of sources. the BSS algorithm can separate the mixtures well in the time domain. This algorithm has a good performance in converging fast. We had checked up the quality of the algorithm after separating the mixed signals. The results of simulations show that this BSS method has the abnormal waveforms due to unconverging coefficients in the beginning, and stably has the separated waveforms which almost equal to the sources in the most period.