An improvement of the Standard Hough Transform to detect line segments

Nguyen, Thuy Tuong; Pham, Xuan Dai; Jeon, Jae Wook

doi:10.1109/icit.2008.4608701

Cited by 16 publications

(2 citation statements)

References 10 publications

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“…This method is used for shapes that can be expressed in a parametric space [16,17]. The figures we need to describe are often nonlinear, like circles, ellipses, etc.…”

Section: Feature Extractionmentioning

confidence: 99%

Increasing the efficiency of the Persian Sign Language system based on new preprocessing method

Yavari

Sadati

Mozaffari

2016

2016 1st Conference on Swarm Intelligence and Evolutionary Computation (CSIEC)

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In this paper, a systems is presented to recognize static gesture of alphabets in Persian Sign Language (PSL). The implemented system does not need any gloves or visual marking system, and just uses images captured by camera to recognize PSL alphabets. This system contains three principal phase: preprocessing, feature extraction, and classification. Preprocessing phase includes using several preprocessing methods on the image which reduces the difference among the hand gesture in the same letter group. In the second phase, Hough Transform function is used for feature extraction from images and MLP NN is used for image classification in the third phase. Results of the paper show that in spite of applying several preprocessing methods on images, the time of neural network training is reduced. Furthermore the recognition rate of PLS improves considerably. This system is able to recognize every 37 PSL alphabet by 98.91% accuracy.

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“…This method is used for shapes that can be expressed in a parametric space [16,17]. The figures we need to describe are often nonlinear, like circles, ellipses, etc.…”

Section: Feature Extractionmentioning

confidence: 99%

Increasing the efficiency of the Persian Sign Language system based on new preprocessing method

Yavari

Sadati

Mozaffari

2016

2016 1st Conference on Swarm Intelligence and Evolutionary Computation (CSIEC)

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“…Gu [12] proposed an improved oblique edge diameter detection algorithm to alleviate the problems of slow speed and poor stability of Prewitt operator in edge detection. Hough transform [13] is a feature extraction technology in image processing, which is usually used to detect lines in images [14]. Affine transformation is widely used in image processing.…”

Section: Introductionmentioning

confidence: 99%

Size and defect detection of valve based on computer vision

Xing¹,

Li²,

Tan³

et al. 2023

International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2022)

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Engine valve is the core component of the engine, and its quality determines the performance of the engine. In industrial production quality inspection, it is necessary to detect the size of the valve and whether there are defects on the surface. Usually, the quality of the valve is determined by comparing the image of the valve surface with the standard image. However, the existing surface defect detection technology cannot detect the curved surface device. In order to solve this problem, this paper designs a valve size and defect detection method based on computer vision. The experimental results show that the method can quickly and accurately detect the rod diameter, groove radius and surface defects of the valve. The method is practical, robust and real-time.

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Live three-dimensional reconstruction system with stereo vision display

Yuan

Tian

et al. 2013

Computers & Electrical Engineering

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An improvement of the Standard Hough Transform to detect line segments

Cited by 16 publications

References 10 publications

Increasing the efficiency of the Persian Sign Language system based on new preprocessing method

Increasing the efficiency of the Persian Sign Language system based on new preprocessing method

Size and defect detection of valve based on computer vision

Live three-dimensional reconstruction system with stereo vision display

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