A simple and robust line detection algorithm based on small eigenvalue analysis

Guru, D. S.; Shekar, B. H.; Nagabhushan, P.

doi:10.1016/j.patrec.2003.08.007

Cited by 115 publications

(74 citation statements)

References 21 publications

(22 reference statements)

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“…circles and lines. There is a large number of algorithms for detecting them in images, from the classical Hough transform (Davies, 2004) up to a range of more elaborate methods (Guru and Shekar, 2004).…”

Section: State Of the Artmentioning

confidence: 99%

(A) VISION FOR 2050 - The Road Towards Image Understanding for a Human-Robot Soccer Match

Frese¹,

Laue²

2008

Proceedings of the Fifth International Conference on Informatics in Control, Automation and Robotics Service

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Abstract:We believe it is possible to create the visual subsystem needed for the RoboCup 2050 challenge -a soccer match between humans and robots -within the next decade. In this position paper, we argue, that the basic techniques are available, but the main challenge will be to achieve the necessary robustness. We propose to address this challenge through the use of probabilistically modeled context, so for instance a visually indistinct circle is accepted as the ball, if it fits well with the ball's motion model and vice versa. Our vision is accompanied by a sequence of (partially already conducted) experiments for its verification. In these experiments, a human soccer player carries a helmet with a camera and an inertial sensor and the vision system has to extract all information from that data, a humanoid robot would need to take the human's place.

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Section: State Of the Artmentioning

confidence: 99%

(A) VISION FOR 2050 - The Road Towards Image Understanding for a Human-Robot Soccer Match

Frese¹,

Laue²

2008

Proceedings of the Fifth International Conference on Informatics in Control, Automation and Robotics Service

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show abstract

“…Finding straight line segments in images is a fundamental task in image processing. In the literature, line segment detection has been classified into four categories [1], namely statistical-based models, gradient-based models, pixel connectivity-edge linkingbased models, and Hough transform (HT)-based models. Significant progress has been made in each of these classes [2][3][4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

“…Guru et al identified straight line segments that are longer than a predefined threshold by exploring the characteristics of the smallest eigenvalue associated with the covariance matrix of a set of connected edge pixels. The computation is simple and effective [1], but its results are not robust under non-uniform and non-linear monotonic changes in intensity. This phenomenon is illustrated in Fig.…”

Section: Introductionmentioning

confidence: 99%

Step‐by‐step pipeline processing approach for line segment detection

Shao

Ding

Luo

et al. 2017

IET Image Processing

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This study proposes a line segment detection that can efficiently and effectively handle non-linear uniform intensity changes. The presented sketching algorithm applies the resistant to affine transformation and monotonic intensity change (RATMIC) descriptor to conduct binary translation in the image pre-processing step, which can remove the unwanted smoothing of the Canny detector in most line detections. The Harris corner detector is applied to catch regions of line segments for the purpose of simulating the composition of sketching and achieving a sense of unity within the picture. Furthermore, the RATMIC descriptor is employed to obtain binary images of the regions of interest (ROIs). Finally, small eigenvalue analysis is implemented to detect straight lines in the ROIs. The experiments conducted on various images with image rotation, scaling, and translation validate the effectiveness of the proposed method. The experimental results also demonstrate that about 30% in the overall coverage of major lines and 20% in the coverage per major line are increased compared with the state-of-the-art line detectors. Moreover, the performance of the proposed method produces a combined advantage of ∼17% in the coverage of line segments over the line segment detector with noisy images.

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“…In this paper, we will focus on a novel vision-based measurement technique and on a system prototype that perfectly fits the industrial domain, as it is able to improve accuracy, robustness and speed in estimating both the direction and the position of a robotic vehicle with respect to a painted line, thus supporting efficient control, as preliminary reported in [21]. In general, the problem of line detection is closely related to the classic problem of line recognition in images [22], [23], although it is made more difficult by time-varying light conditions and by the robot's dynamics. In general, the performances of vision-based line detection systems are limited by robustness and speed issues, which in turn depend on camera frame rate, camera resolution and algorithm complexity.…”

Section: Introductionmentioning

confidence: 99%

A fast and low-cost vision-based line tracking measurement system for robotic vehicles

2015

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Localization and tracking systems are nowadays a quite common solution for automated guided vehicles (AGV) in industrial environments. The bunch of technological solutions developed in this sector is now being revitalized by their applications in service robots, e.g. for safe navigation in crowded public spaces or in autonomous cars. Related to this field are robotic competitions and races. In this particular scenario, the robot often has to track a line painted on the ground. Line tracking techniques typically rely on light dependent resistors (LDR), photo-diodes or photo-transistors detecting the light generated by normal or infrared Light Emitting Diodes (LEDs), arrays of electric inductance sensors or vision systems. Crucial issues for line tracking are: accuracy and reliability in estimating the direction of the moving vehicle with respect to the line and processing speed. Such problems are particularly critical when high-speed mobile robots are considered. To address this issue, in this paper a vision-based technique of moderate computational complexity is described.Â The proposed solution has been implemented on a low-cost embedded platform and relies on a high frame rate light contrast sensor, a tailored RANSAC-based algorithm and a Kalman filter. The reported experimental results prove that the proposed solution is able to track the direction of the vehicle in real-time even when the field of view of the camera is limited and the vehicle moves at high speed.

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A simple and robust line detection algorithm based on small eigenvalue analysis

Cited by 115 publications

References 21 publications

(A) VISION FOR 2050 - The Road Towards Image Understanding for a Human-Robot Soccer Match

(A) VISION FOR 2050 - The Road Towards Image Understanding for a Human-Robot Soccer Match

Step‐by‐step pipeline processing approach for line segment detection

A fast and low-cost vision-based line tracking measurement system for robotic vehicles

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