A Hough transform based line recognition method utilizing both parameter space and image space

Song, Jiqiang; Lyu, Michael R.

doi:10.1016/j.patcog.2004.09.003

Cited by 64 publications

(34 citation statements)

References 17 publications

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“…The first group of methods is based on image space verification from the information of the HT peaks. Song and Lyu [17] propose an HT-based segment detection method which utilizes both the parameter space and the image space. Using a boundary recorder for each parameter cell, the authors develop an image-based line verification algorithm.…”

Section: Introductionmentioning

confidence: 99%

A variant of the Hough Transform for the combined detection of corners, segments, and polylines

Bachiller

Manso

Bustos

2017

J Image Video Proc.

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The Hough Transform (HT) is an effective and popular technique for detecting image features such as lines and curves. From its standard form, numerous variants have emerged with the objective, in many cases, of extending the kind of image features that could be detected. Particularly, corner and line segment detection using HT has been separately addressed by several approaches. To deal with the combined detection of both image features (corners and segments), this paper presents a new variant of the Hough Transform. The proposed method provides an accurate detection of segment endpoints, even if they do not correspond to intersection points between line segments. Segments are detected from their endpoints, producing not only a set of isolated segments but also a collection of polylines. This provides a direct representation of the polygonal contours of the image despite imperfections in the input data such as missing or noisy feature points. It is also shown how this proposal can be extended to detect predefined polygonal shapes. The paper describes in detail every stage of the proposed method and includes experimental results obtained from real images showing the benefits of the proposal in comparison with other approaches.

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Section: Introductionmentioning

confidence: 99%

A variant of the Hough Transform for the combined detection of corners, segments, and polylines

Bachiller

Manso

Bustos

2017

J Image Video Proc.

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“…Smartphone Camera HT is recognized as a popular and efficient technique for detecting straight lines in images, even in the presence of noise [28][29][30][31][32]. The Canny edge detection algorithm based on the Gaussian low-pass filter [33,34] is first adopted to identify all of the edges contained in the grayscale image, resulting in a binary image.…”

Section: Cable Force Estimation Algorithm Usingmentioning

confidence: 99%

Experimental Verification for Cable Force Estimation Using Handheld Shooting of Smartphones

Zhao¹,

Ri²,

Wang³

2017

Journal of Sensors

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Currently, due to the rapid development and popularization of smartphones, the usage of ubiquitous smartphones has attracted growing interest in the field of structural health monitoring (SHM). The portable and rapid cable force measurement for cablesupported structures, such as a cable-stayed bridge and a suspension bridge, has an important and practical significance in the evaluation of initial damage and the recovery of transportation networks. The extraction of dynamic characteristics (natural frequencies) of cable is considered as an essential issue in the cable force estimation. Therefore, in this study, a vision-based approach is proposed for identifying the natural frequencies of cable using handheld shooting of smartphone camera. The boundary of cable is selected as a target to be tracked in the region of interest (ROI) of video image sequence captured by smartphone camera, and the dynamic characteristics of cable are identified according to its dynamic displacement responses in frequency domain. The moving average is adopted to eliminate the noise associated with the shaking of smartphone camera during measurement. A laboratory scale cable model test and a pedestrian cable-stayed bridge test are carried out to evaluate the proposed approach. The results demonstrate the feasibility of using smartphone camera for cable force estimation.

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“…Firstly, the extracted 3D ground line segments are projected to the image space using the interior orientation (IO) parameters and the initial EO parameters to determine the coarse location of the buildings to which the corresponding 2D line segments belong. The linear segments of the buildings are then automatically extracted using the Hough-transform algorithm [48,49], and the conjugate 2D line segments are then manually selected. There will be multiple (not less than two) conjugate 2D line segments in the image space for a 3D line segment in the ground space, and all the available 2D line segments are extracted.…”

Section: Extraction Of Conjugate 2d Line Segments and Tie Points Frommentioning

confidence: 99%

A Linear Feature-Based Approach for the Registration of Unmanned Aerial Vehicle Remotely-Sensed Images and Airborne LiDAR Data

et al. 2016

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Abstract:Compared with traditional manned airborne photogrammetry, unmanned aerial vehicle remote sensing (UAVRS) has the advantages of lower cost and higher flexibility in data acquisition. It has, therefore, found various applications in fields such as three-dimensional (3D) mapping, emergency management, and so on. However, due to the instability of the UAVRS platforms and the low accuracy of the onboard exterior orientation (EO) observations, the use of direct georeferencing image data leads to large location errors. Light detection and ranging (LiDAR) data, which is highly accurate 3D information, is treated as a complementary data source to the optical images. This paper presents a semi-automatic approach for the registration of UAVRS images and airborne LiDAR data based on linear control features. The presented approach consists of three main components, as follows. (1) Buildings are first separated from the point cloud by the integrated use of height and size filtering and RANdom SAmple Consensus (RANSAC) plane fitting, and the 3D line segments of the building ridges and boundaries are semi-automatically extracted through plane intersection and boundary regularization with manual selections; (2) the 3D line segments are projected to the image space using the initial EO parameters to obtain the approximate locations, and all the corresponding 2D line segments are semi-automatically extracted from the UAVRS images. Meanwhile, the tie points of the UAVRS images are generated using a Förstner operator and least-squares image matching; and (3) by use of the equations derived from the coplanarity constraints of the linear control features and the colinear constraints of the tie points, block bundle adjustment is carried out to update the EO parameters of the UAVRS images in the coordinate framework of the LiDAR data, achieving the co-registration of the two datasets. Experiments were performed to demonstrate the validity and effectiveness of the presented method, and a comparison with the traditional registration method based on LiDAR intensity images showed that the presented method is more accurate, and a sub-pixel accuracy level can be achieved.

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A Hough transform based line recognition method utilizing both parameter space and image space

Cited by 64 publications

References 17 publications

A variant of the Hough Transform for the combined detection of corners, segments, and polylines

A variant of the Hough Transform for the combined detection of corners, segments, and polylines

Experimental Verification for Cable Force Estimation Using Handheld Shooting of Smartphones

A Linear Feature-Based Approach for the Registration of Unmanned Aerial Vehicle Remotely-Sensed Images and Airborne LiDAR Data

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