FPGA-based robust ellipse estimation for circular road sign detection

Martelli, Samuele; Marzotto, R.; Colombari, A.; Murino, Vittorio

doi:10.1109/cvprw.2010.5543761

Cited by 10 publications

(6 citation statements)

References 13 publications

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“…They proposed a multiprocessor system based on the Microblaze processors to provide parallelism. RANSAC implementation on FPGA was also applied in ellipse estimation for eye tracking [6] and road sign detection [14].…”

Section: Related Workmentioning

confidence: 99%

FPGA implementation of RANSAC algorithm for real-time image geometry estimation

Tang

Shaikh-Husin

Sheikh

2013

2013 IEEE Student Conference on Research and Developement

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Random Sample Consensus (RANSAC) is commonly used in many estimation tasks especially in computer vision applications due to its simplicity. This paper presents a hardware/software co-design implementation of RANSAC algorithm for real-time affine geometry estimation on a field programmable gate array (FPGA) platform. Double buffering technique is used to store and process data in pipeline. Experimental result shows that the proposed system managed to speed up the software process by about 11.4 times for 100 data points. The proposed architecture was also tested on Altera DE2-115 with 100 MHz NiosII Processor running to handle a video stream of 30 frames per second.

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Section: Related Workmentioning

confidence: 99%

FPGA implementation of RANSAC algorithm for real-time image geometry estimation

Tang

Shaikh-Husin

Sheikh

2013

2013 IEEE Student Conference on Research and Developement

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“…A 3D elliptical shape is projected as an ellipse under projective transformation. This property made detection of elliptical features easier and incited many researches in image-based object detection (Kanatani and Ohta, 2004;Martelli et al, 2010;Shuhua and Yong, 2010). Under projective projection a circle is deformed to an ellipse whenever the pose is not fronto-parallel.…”

Section: Introductionmentioning

confidence: 99%

Multi-view 3D circular target reconstruction with uncertainty analysis

Soheilian¹,

Brédif²

2014

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The paper presents an algorithm for reconstruction of 3D circle from its apparition in n images. It supposes that camera poses are known up to an uncertainty. They will be considered as observations and will be refined during the reconstruction process. First, circle apparitions will be estimated in every individual image from a set of 2D points using a constrained optimization. Uncertainty of 2D points are propagated in 2D ellipse estimation and leads to covariance matrix of ellipse parameters. In 3D reconstruction process ellipse and camera pose parameters are considered as observations with known covariances. A minimal parametrization of 3D circle enables to model the projection of circle in image without any constraint. The reconstruction is performed by minimizing the length of observation residuals vector in a non linear Gauss-Helmert model. The output consists in parameters of the corresponding circle in 3D and their covariances. The results are presented on simulated data.

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“…To begin with, the class of ellipses is invariant to projective transformations, thus an elliptical shape remains so when it is captured from any viewpoint by a pinhole camera [4]. This property makes easy to recognize objects that contain ellipses [11,8] or partially elliptical features [18]. When the parameters of one or more coplanar 3D ellipses that originated the projection are known, the class of homographies that make it orthonormal to the image plane can be retrieved.…”

Section: Introductionmentioning

confidence: 99%