An improved Hough transform for line detection

Duan, Dagao; Xie, Mei; Mo, Qian; Han, Zhongming; Wan, Yuqing

doi:10.1109/iccasm.2010.5620827

Cited by 42 publications

(12 citation statements)

References 10 publications

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“…This algorithm is essentially a voting process where each point belonging to the patterns votes for all the possible patterns passing through that point. These votes are accumulated in an accumulator array called bins, and the pattern receiving the maximum votes is recognized as the desired pattern [25].…”

Section: Hough Transform In the Tomographic Plane Of 3-d Point Cloudsmentioning

confidence: 99%

Hough Transform and Clustering for a 3-D Building Reconstruction with Tomographic SAR Point Clouds

Liu

Pang

et al. 2019

Sensors

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Tomographic synthetic aperture radar (TomoSAR) produces 3-D point clouds with unavoidable noise or false targets that seriously deteriorate the quality of 3-D images and the building reconstruction over urban areas. In this paper, a Hough transform was adopted to detect the outline of a building; however, on one hand, the obtained outline of a building with Hough transform is broken, and on the other hand, some of these broken lines belong to the same segment of a building outline, but the parameters of these lines are slightly different. These problems will lead to that segment of a building outline being represented by multiple different parameters in the Hough transform. Therefore, an unsupervised clustering method was employed for clustering these line parameters. The lines gathered in the same cluster were considered to correspond to a same segment of a building outline. In this way, different line parameters corresponding to a segment of a building outline were integrated into one and then the continuous outline of the building in cloud points was obtained. Steps of the proposed data processing method were as follows. First, the Hough transform was made use of to detect the lines on the tomography plane in TomoSAR point clouds. These detected lines lay on the outline of the building, but they were broken due to the density variation of point clouds. Second, the lines detected using the Hough transform were grouped as a date set for training the building outline. Unsupervised clustering was utilized to classify the lines in several clusters. The cluster number was automatically determined via the unsupervised clustering algorithm, which meant the number of straight segments of the building edge was obtained. The lines in each cluster were considered to belong to the same straight segment of the building outline. Then, within each cluster, which represents a part or a segment of the building edge, a repaired straight line was constructed. Third, between each two clusters or each two segments of the building outline, the joint point was estimated by extending the two segments. Therefore, the building outline was obtained as completely as possible. Finally, taking the estimated building outline as the clustering center, supervised learning algorithm was used to classify the building cloud point and the noise (or false targets), then the building cloud point was refined. Then, our refined and unrefined data were fed into the neural network for building the 3-D construction. The comparison results show the correctness and the effectiveness of our improved method.

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Section: Hough Transform In the Tomographic Plane Of 3-d Point Cloudsmentioning

confidence: 99%

Hough Transform and Clustering for a 3-D Building Reconstruction with Tomographic SAR Point Clouds

Liu

Pang

et al. 2019

Sensors

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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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“…An improved Hough algorithm for line detection, which shares the comparative qualities of the modified Hough transform (MHT) and the Windowed random Hough transform (RHT) [ 28 ]. Hough transform (HT) has discovered monstrous viable applications in vision issues such as object detection, movement detection, biometric validation, medical imaging, remote information processing, and robot route [ 29 ]. Applying Hough transform formulation depends on the utilization of the inverse Radon operator, which decides the location and orientation of the lines in the image to the noise information image [ 30 ].…”

Section: Introductionmentioning

confidence: 99%

Developed an Automated Design Sorting System According to Outer Edges of Object

Hamzah

Abbas²

2022

The Scientific World Journal

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The automated sorting systems are used in the industrial sectors to increase the rate of production. This research developed the sorting system by using a vision machine to detect the matching of capturing image with the storage base image. The system will be matching and sorting in real-time with 5 cm/s conveyor belt speed. The vision system is based on three stages to arrive at the sorting decision. The first stage is to covert the capturing image to a binary image, the second stage is applying edge detection of the product, and the third stage is matching this result with the base image. The system was successful to sort any product with complexity in shape and with high efficiency. The system sorting can be detected and sorted any product/machine element at any position or orientation. The system uses real-time analysis in order to provide the required results. The results arrived at the sorting gate at the end conveyer belt of the system if open that means the product matching. Three different products were selected in order to investigate the response and the accuracy of the results. It was found that the maximum of error to detect the product is not exceeding 2% for all cases.

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An improved Hough transform for line detection

Cited by 42 publications

References 10 publications

Hough Transform and Clustering for a 3-D Building Reconstruction with Tomographic SAR Point Clouds

Hough Transform and Clustering for a 3-D Building Reconstruction with Tomographic SAR Point Clouds

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

Developed an Automated Design Sorting System According to Outer Edges of Object

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