Some Accuracy and Resolution Aspects of Computer Vision Distance Measurements

McVey, Eugene S.; Lee, Jong W.

doi:10.1109/tpami.1982.4767319

Cited by 43 publications

(16 citation statements)

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“…First, the computational costs are too high. Second, stereo errors are proportional to distance [33]. As a consequence, depth measurements corresponding to signs or panels at long distances are not reliable enough.…”

Section: ) Candidate Extractionmentioning

confidence: 99%

Automatic Traffic Signs and Panels Inspection System Using Computer Vision

González

Garrido

Llorca

et al. 2011

IEEE Trans. Intell. Transport. Syst.

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Abstract-Computer vision techniques applied to systems used on road maintenance, which are related either to traffic signs or to the road itself, are playing a major role in many countries because of the higher investment on public works of this kind. These systems are able to collect a wide range of information automatically and quickly, with the aim of improving road safety. In this context, the correct visibility of traffic signs and panels is vital for the safety of drivers. This paper describes an approach to the VISUAL Inspection of Signs and panEls ("VISUALISE"), which is an automatic inspection system, mounted onboard a vehicle, which performs inspection tasks at conventional driving speeds. VISUALISE allows for an improvement in the awareness of the road signaling state, supporting planning and decision making on the administration's and infrastructure operators' side. A description of the main computer vision techniques and some experimental results obtained from thousands of kilometers are presented. Finally, the conclusions of the system are described.

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Section: ) Candidate Extractionmentioning

confidence: 99%

Automatic Traffic Signs and Panels Inspection System Using Computer Vision

González

Garrido

Llorca

et al. 2011

IEEE Trans. Intell. Transport. Syst.

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show abstract

“…While several authors have observed the bias in 3D reconstruction dating back to at least 30 years ago [11], none of them have presented a closed-from, effective correction for it. In most cases, the covariance of the reconstructed point is either only modeled in the Z (range) direction, or is approximated by some form of covariance propagation.…”

Section: Related Workmentioning

confidence: 99%

“…McVey and Lee [11] are arguably the first to publish a study on the error of stereo vision as a function of camera parameters. Later, Blostein and Huang [1] investigated the errors in stereo reconstruction under the assumption that correct correspondences across the two images had been found.…”

Section: Related Workmentioning

confidence: 99%

Exact bias correction and covariance estimation for stereo vision

Freundlich

Zavlanos

Mordohai

2015

2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

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We present an approach for correcting the bias in 3D reconstruction of points imaged by a calibrated stereo rig. Our analysis is based on the observation that, due to quantization error, a 3D point reconstructed by triangulation essentially represents an entire region in space. The true location of the world point that generated the triangulated point could be anywhere in this region. We argue that the reconstructed point, if it is to represent this region in space without bias, should be located at the centroid of this region, which is not what has been done in the literature. We derive the exact geometry of these regions in space, which we call 3D cells, and we show how they can be viewed as uniform distributions of possible pre-images of the pair of corresponding pixels. By assuming a uniform distribution of points in 3D, as opposed to a uniform distribution of the projections of these 3D points on the images, we arrive at a fast and exact computation of the triangulation bias in each cell. In addition, we derive the exact covariance matrices of the 3D cells. We validate our approach in a variety of simulations ranging from 3D reconstruction to camera localization and relative motion estimation. In all cases, we are able to demonstrate a marked improvement compared to conventional techniques for small disparity values, for which bias is significant and the required corrections are large.

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“…Most researches on error analysis in computer vision have been concerned with the analysis of the stereopsis technique and have considered only the quantization error [13], [31], [34], [36], [45]. We show in [50] how the sensor error model can be designed for the structured light sensing technique, which is the imaging technique used in our experiment.…”

Section: The Modeling Schemementioning

confidence: 99%

On 3D Model Construction by Fusing Heterogeneous Sensor Data

Wang¹

1994

Computer Vision and Image Understanding

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In this paper, we propose a scheme for 3D model construction by fusing heterogeneous sensor data. The proposed scheme is intended for use in an environment where multiple, heterogeneous sensors operate asynchronously. Surface depth, orientation, and curvature measurements obtained from multiple sensors and vantage points are incorporated to construct a computer description of the imaged object. The proposed scheme uses Kalman filter as the sensor data integration tool and hierarchical spline surface as the recording data structure. Kalman filter is used to obtain statistically optimal estimates of the imaged surface structure based on possibly noisy sensor measurements. Hierarchical spline surface is used as the representation scheme because it maintains high-order surface derivative continuity, may be adaptively refined, and is storage efficient. We show in this paper how these mathematical tools can be used in designing a modeling scheme to fuse heterogeneous sensor data.

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Some Accuracy and Resolution Aspects of Computer Vision Distance Measurements

Cited by 43 publications

References 1 publication

Automatic Traffic Signs and Panels Inspection System Using Computer Vision

Automatic Traffic Signs and Panels Inspection System Using Computer Vision

Exact bias correction and covariance estimation for stereo vision

On 3D Model Construction by Fusing Heterogeneous Sensor Data

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