• Journal of KIISE
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• v.42 no.7
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• pp.868-877
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• 2015

The barcode area extraction from images has been extensively studied, and existing methods exploit frequency characteristics or depend on the Hough transform (HT). However, the slantedness of the images and noise affects the performance of these approaches. Moreover, it is difficult to deal with the case where an image contains multiple barcodes. We therefore propose a barcode detection algorithm that is robust under such unfavorable conditions. The pre-processing step implements a probabilistic Hough transform to determine the areas that contain barcodes with a high probability, regardless of the slantedness, noise, and the number of instances. Then, a frequency component analysis extracts the barcodes. We successfully implemented the proposed system and performed a series of barcode extraction tests.

• International Journal of Korean Welding Society
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• v.1 no.2
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• pp.23-29
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• 2001

In this study, a preview-sensing visual sensor system is constructed far weld seam tracking in GMA welding. The visual sensor system consists of a CCD camera, a diode laser system with a cylindrical lens, and a band-pass-filter to overcome the degrading of image due to spatters and/or arc light. Among the image processing methods, Hough transform method is compared with the central difference method from a viewpoint of the capability for extracting the accurate feature position. As a result, it was revealed that Hough transform method can more accurately extract the feature positions and it can be applied to real time weld seam tracking. Image processing which includes Hough transform method is carried out to extract straight lines that express laser stripe. After extracting the lines, weld joint position and edge points are determined by intersecting the lines. Even though the image includes a spatter trace on it, it is possible to recognize the position of weld joint. Weld seam tracking was precisely implemented with adopting Hough transform method, and it is possible to track the weld seam in the case of offset angle is in the region of $\pm$ $15^{\circ}$.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.301-304
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• 1999

In this paper, we proposed the new method of extracting the position and the length of the input-line by using only two parameters ($\theta$, $\rho$) from the HT(Hough Transform) data. The computer simulations and the optical experiments by using the HT CGH(Computer Generated Hologram) filter is perfermed. The results are very similar to those of the computer simulation results.

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 1991.10a
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• pp.77-80
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• 1991

This paper proposes a new feature extraction method which is effectively used in character recognition, and validate the effectiveness through various computational methods for similiarity degree. To get feature vectors used in this method, Hough transform is applied to character image, which is used for edge extraction in image processing. By that transformation technique, strokes could be extracted and feature vectors constructed suitably. The characteristic of this method is solving the difficulties in stroke extraction through transform space analysis, which is induced by noise and blurring, and representing high recognition rate 99.3% within 10 candidates in relative low dimension.

• Proceedings of the Korean Information Science Society Conference
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• 2001.04b
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• pp.568-570
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• 2001

fiducial mark는 PCB 정밀조립/검사 장비에서 위치보정을 위해 필수적으로 사용되는데, 실제 생산현장에서의 fiducial mark의 품질은 완전하지 않고, 손상된 상태인 경우가 많이 발생한다. 기존에 주로 사용되는 blob centroid 방법에 의한 mark의 위치추출은 mark의 상태가 손상되었을 경우, 큰 오차를 수반하게 된다. 본 논문에서는 Generalized Hough Transform을 이용한 fiducial mark의 중심위치 기법을 개발하여, 적용한 결과 손상된 mark에 대해서도 매우 강건한 결과를 보여주었다.

• Proceedings of the Korea Information Processing Society Conference
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• 2009.11a
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• pp.333-334
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• 2009

In this paper, I propose the novel method based on Labeled Hough transform and GLCM(Grey-Level Co-occurrence Matrix) for license plate detection. A lot of conventional methods have been proposed to detect the license plate, but those are useless in order to detect the license plate well in case of dark or unstable images. Histogram equalization is preprocessed to each image before applying this method. As a result, the license plate is detected accurately

• Proceedings of the IEEK Conference
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• 2000.09a
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• pp.931-934
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• 2000

입력영상의 Hough 변환(Hough Transform; HT) 영상으로부터 입력영상인 원호의 중심각의 크기, 반지름의 길이 및 양 끝점의 위치 정보를 구하는 방법을 제안하였다. HT 영역에서 포락선의 정보로부터 원호영상의 외접사각형의 가로 및 세로 길이와 회전 각도를 구한 다음 이로부터 원호영상 정보를 계산하였다.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.13 no.3
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• pp.118-123
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• 1999

Hough transform is well employed to retect or recognize the lines in image processing or in computer vision. Curve of the logarithm of ranges against the bearing does not change its shape according to data trints. This fact suggests that calculation cost can be remarkably reduced. An effective line detection algorithm is represented.sented.

• ETRI Journal
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• v.35 no.1
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• pp.162-165
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• 2013

Recently, a new method called the local polynomial periodogram-Hough transform (LHT) was proposed for linear frequency modulated (LFM) signal detection. In this letter, a closed-form expression of the output signal-to-noise ratio is derived for the LHT, showing that the method exhibits a threshold effect for LFM signal detection. Comparisons with the pseudo-Wigner-Hough transform (PWHT) show that the threshold of the LHT is lower (better) than that of the PWHT.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1377-1381
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• 2003

This paper describes real time object tracking of 3D objects in 2D image sequences. The moving objects are segmented from the image sequence using morphological operations. The moving objects are segmented by the method of differential image followed by the process of morphological dilation. The moving objects are recognized and tracked using statistical moments. The direction of moving objects are determined by the Hough transform. The straight lines in the moving objects are found with the help of Hough transform. The direction of the moving object is calculated from the orientation of the straight lines in the direction of the principal axes of the moving objects. The direction of the moving object and the displacement of the object in the image sequence is used to calculate the velocity of the moving objects. The simulation results of the proposed method are promising on the test images.