• 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.

• Journal of Convergence for Information Technology
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• v.7 no.6
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• pp.143-151
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• 2017

The generalized Hough transform(GHough) can be used effectively for detecting and extracting an arbitrary-shaped 2-D model in an input image. However, the main drawbacks of the GHough are both heavy computation and an excessive storage requirement. Thus, most of the researches so far have focused on reducing both the time and space requirement of the GHough. But it is still not clear how well their improved algorithms will perform under various noise in an input image. Thus, this paper proposes a new framework that can measure the performance of the GHough quantitatively. For this purpose, we view the GHough as a detector in signal detection theory and the ROC curve will be used to specify the performance of the GHough. Finally, we show that we can evaluate the GHough under various noise conditions in an input image.

• 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.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.12
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• pp.1868-1873
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• 2021

This paper proposes the Hough transform(HT) using an extended lookup table(LUT) to reduce the computational burden of the HT that is a typical straight line detection algorithm, and analyzes its performance. The conventional HT also uses a LUT to the calculation of the parameter 𝜌 of all straight lines passing through an edge pixel of interest(ePel) in order to reduce the computational burden. However, the proposed HT adopts an extended LUT that can be applied to straight lines across the ePel as well as its peripheral edge pixels to induce more computational reduction. This paper proves the validity of the proposed algorithm mathematically and also verifies it through simulation. The simulation results show that the proposed HT reduces the multiplication computation from 49.6% up to 16.1%, depending on the image and the applied extended LUT, compared to the conventional HT.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.1984-1987
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• 2003

본 논문에서는 모델에 기반한 2차원 영상인식 알고리즘 중에 하나인 일반화된 허프변환(Generalized Hough Transform)에 대하여 색상정보까지 포함할 수 있도록 기존의 알고리즘을 확장하는 방법을 제시하였고, 이에 의한 실험결과를 간단히 고찰하였다. 기존의 일반화된 허프변환은 대상물의 윤곽선 정보에 기반을 두었기 때문에, 윤곽선 정보가 일치하면 대상물의 색상이나 명암분포가 달라도 동일한 대상물로 인식할 가능성이 있다. 따라서, 일반화된 허프변환을 확장하여 대상물의 모델링과 인식과정에 색상정보(chromatic information)를 포함한다면 2D 영상인식시 컬러정보를 활용할 수 있는 장점이 있다. 여기에서는 실제로 모델링 과정과 인식과정에서 색상정보를 반영하기 위한 간략한 방법과, 이에 따른 실험결과를 제시하였다. 간단한 2D 위치변환이 존재하는 실험에서 윤곽선의 모양이 거의 일치하더라도 색상이 다른 대상물이 존재할 경우에 이를 올바로 구분할 수 있었다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.10a
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• pp.383-386
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• 2007

허프 변환(Hough transform)은 영상에서 몇 개의 파라미터로 표현되는 기하학적 요소 추출을 위해 널리 사용되고 있는 방법 중 하나이다. 하지만 허프 변환은 영상의 한 픽셀이 허프 공간(Hough space)의 한 방정식에 대응되는 일대다 특성으로 인해 잡음에 민감한 특성을 갖는다. 이러한 잡음 민감성은 검출되는 직선의 개수뿐만이 아니라 검출된 직선의 품질에도 영향을 미칠 수 있다. 즉, 실제 직선에서 벗어난 직선이 검출되거나 하나의 실제 직선에 대해 여러 개의 직선이 검출되는 등의 직선 왜곡이 발생할 수 있다. 이러한 직선 왜곡은 잡음 이외에도 허프 공간의 설정, 특히 각 해상도의 설정에 영향을 받는다. 이 논문에서는 기존의 허프 변환에서 발생하는 이러한 직선 왜곡을 분석하고, 잡음 민감성을 줄이기 위해 제안된 경계선 강도 허프 변환(Edge Strength Hough Transform, ESHT)에서 이러한 왜곡이 적게 발생함을 보인다. 또한 ESHT에서만 발생할 수 있는 왜곡을 분석하고 해결방안을 제시한다. 제시한 방법에 의해 직선의 왜곡이 감소하는 것은 실험 결과를 통해 확인할 수 있다.

• 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.

• 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.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1992.04b
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• pp.91-101
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• 1992

본 연구에서는 용접선 추출의 방법으로 현재 많이 사용되고 있는 용접선을 따라 연속적으로 이미지를 얻어 처리하는 사전관찰(preview)기법을 개선하여 용접모재를 한번에 촬영(snapshot)하여 화상처리를 거친 후 용접정보가 들어 있는 CAD database와 비교, 매칭시켜 필요한 용접정보를 획득하는 새로운 방법을 제시한다. 또한 정확한 꼭지점을 추출하기 위해서는 정확한 직선식이 필요한데 이의 계산에는 허프변환(Hough Transform)이 이용되고 있지만 계산시간이 많이 소요되며 부정확하다. 계산시간의 감소 및 정확도의 향상을 위해 기존의 허프변환(Hough Transform)을 개선한 수정된 허프변환(Modified HoughTranform)을 개발하였다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.06a
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• pp.728-731
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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 play an important role in ESHT and should be decided experimentally. In this paper, we derived a formula to decide decreasing parameter. Using the derived formulae, the decreasing parameter value 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.