A novel Hough transform method for line detection by enhancing accumulator array

Ji, Junhong; Guodong, Cheng; Sun, Lining

doi:10.1016/j.patrec.2011.04.011

Cited by 44 publications

(31 citation statements)

References 19 publications

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“…(3) Then, the line parameters are computed. The most commonly used line fitting techniques are the Hough transform (HT) [26] and least squares. HT can be used to extract line features by transforming the image pixel into the parameter space and detecting peaks in that space.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Covariance Estimation Method for LiDAR-Aided Multi-Sensor Integrated Navigation Systems

et al. 2015

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Abstract:The accurate estimation of measurements covariance is a fundamental problem in sensors fusion algorithms and is crucial for the proper operation of filtering algorithms. This paper provides an innovative solution for this problem and realizes the proposed solution on a 2D indoor navigation system for unmanned ground vehicles (UGVs) that fuses measurements from a MEMS-grade gyroscope, speed measurements and a light detection and ranging (LiDAR) sensor. A computationally efficient weighted line extraction method is introduced, where the LiDAR intensity measurements are used, such that the random range errors and systematic errors due to surface reflectivity in LiDAR measurements are considered. The vehicle pose change is obtained from LiDAR line feature matching, and the corresponding pose change covariance is also estimated by a weighted least squares-based technique. The estimated LiDAR-based pose changes are applied as periodic updates to the Inertial Navigation System (INS) in an innovative extended Kalman filter (EKF) design. Besides, the influences of the environment geometry layout and line estimation error are discussed. Real experiments in indoor environment are performed to evaluate the proposed algorithm. The results showed the great consistency between the LiDAR-estimated pose change covariance and the true accuracy. Therefore, this leads to a significant improvement in the vehicle's integrated navigation accuracy. OPEN ACCESSMicromachines 2015, 6 197

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Section: Related Workmentioning

confidence: 99%

Adaptive Covariance Estimation Method for LiDAR-Aided Multi-Sensor Integrated Navigation Systems

et al. 2015

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“…Next step is to find marker position. Hough transformation [16] is used for the detection of the marker pattern features. For simplicity marker containt only one black square, but any recognizable pattern could be applied.…”

Section: Related Workmentioning

confidence: 99%

“…First having acquired marker image we determined its edges using method of [10]. Next Hough method [16] is used to detect square edges characterizing the marker. Hough transform is recognized as a powerful tool to extract parametrized curves.…”

Section: B Tracking Points Selectionmentioning

confidence: 99%

Augmented Reality Using Optical Flow

Koniarski¹

2015

Annals of Computer Science and Information Systems

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Abstract-The paper deals with the application of LucasKanade optical flow algorithm to develop an augmented reality (AR) system. Merging of a live view of the physical real world with context-related computer-rendered images to create a mixed image is a challenging problem. A virtual object has to be located in the correct pose and position in real time and perspective. Besides the occlusion problem need to be taken into consideration.In the paper a computer-vision based method for AR systems based on the fiducial marker matching is proposed. For simplicity black square was used as the marker. This method consists of two main steps. The initial step uses Hough's transformation to detect the marker initial position and to select the marker tracked points. In the second step for each image frame these selected points are being tracked using Lucas-Kanade optical flow method. The positions of the selected points are used for calculating the pose and position of a virtual object. Unlike existing method proposed system using optical flow to increase speed performance. The examples of AR applications using the proposed algorithm are provided and discussed.

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“…The second class of methods is motivated by the butterfly shape [30,31] of a peak region in Hough space [32,33,34]. Butterfly features are used to extract parameters of a line segment.…”

Section: Introductionmentioning

confidence: 99%