A fast Hough transform for segment detection

Guil, Nicolás; Villalba, J.; Zapata, Emilio L.

doi:10.1109/83.469935

Cited by 114 publications

(49 citation statements)

References 11 publications

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“…Because the road edge may intersect with left and right borders of the screen, the road area will be a hexagon or pentagon. It is necessary for us to do a logic judgment to refresh the coordinates [4]. In Fig.…”

Section: B Hough Transformmentioning

confidence: 99%

Traffic Flow Statistic Based on Computer Vision

Qiushi¹,

Cheung²,

Zhang³

et al. 2013

IJCCE

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Abstract-With the development of economy, transportation is becoming more and more important, so a new method to control traffic is needed. Intelligent traffic system emerges as the time require. In this intelligent system, it is the basic that acquire traffic information instant. We used lots of way to get that information, such as induction coil, ultrasonic devices, microwave devices etc,. These tradition ways performed not as except. The cars on the road may block each other in different lane, so the result from the tradition devices may incorrect. Improving the tradition ways, we choose to use computer vision to statistic traffic flow. We use Hough Transform and Gaussian Mixture Model to detect road and cars. The whole system can be achieved on OMAP4460. We test the whole system on a real road, we captured all the cars and according to the test the road is detected accurately. The system performance is as we except.

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Section: B Hough Transformmentioning

confidence: 99%

Traffic Flow Statistic Based on Computer Vision

Qiushi¹,

Cheung²,

Zhang³

et al. 2013

IJCCE

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“…Our approach is based on the Hough transform [18]. The Hough transform is a technique, which can be used to extract features from a set of points.…”

Section: Planar Segmentationmentioning

confidence: 99%

Towards geometric 3D mapping of outdoor environments using mobile robots

Wolf

Howard

Sukhatme

2005

2005 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-This paper presents an approach to generating compact 3D maps of urban environments using mobile robots and laser range finders. Our algorithm extracts planar information from 3D point cloud maps. The planar representation is very efficient for representing building structures in urban environments when a high level of detail is not required. We also present preliminary results on 3D geometric mapping with incomplete data. Based on previously known models and incomplete data, our system is able to estimate parts of buildings which have never been seen before. As validation we present experimental results using a Segway RMP vehicle in two environments, both approximately the size of a city block.

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“…More recently, this research topic has also been studied by the robotics community [10]. The approach used in experiments is based on the Hough transform [8] [22]. The classical application for the Hough transform has been detecting geometric features like lines and circles in sets of 2D points.…”

Section: Plane Extractionmentioning

confidence: 99%