2010
DOI: 10.1155/2010/539836
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Error Evaluation in a Stereovision-Based 3D Reconstruction System

Abstract: The work presented in this paper deals with the performance analysis of the whole 3D reconstruction process of imaged objects, specifically of the set of geometric primitives describing their outline and extracted from a pair of images knowing their associated camera models. The proposed analysis focuses on error estimation for the edge detection process, the starting step for the whole reconstruction procedure. The fitting parameters describing the geometric features composing the workpiece to be evaluated ar… Show more

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Cited by 24 publications
(14 citation statements)
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“…The technique shows measurement errors greater than 2 mm when measured dimension exceeds 50 mm which is higher than the reported in the literature (Belhaoua, 2010). As a result, this technique seems to be unsatisfactory for measuring dimensions over 50 mm.…”
Section: Results Of Pstmentioning
confidence: 59%
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“…The technique shows measurement errors greater than 2 mm when measured dimension exceeds 50 mm which is higher than the reported in the literature (Belhaoua, 2010). As a result, this technique seems to be unsatisfactory for measuring dimensions over 50 mm.…”
Section: Results Of Pstmentioning
confidence: 59%
“…The performance of PST shows poor results for the tolerances that were expected based on (Belhaoua, 2010). Because of this, it is bad for quality control purposes, unless improvements in the factors considered on results of PST are done.…”
Section: Conclusion and Discussionmentioning
confidence: 98%
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“…However, we couldn't evaluate the accuracy of this 3D reconstruction without ground truth. As a matter of fact, the reconstruction process is sensitive to various types of error [9]. The future work is devoted to this point, more specifically to the matching errors.…”
Section: Discussionmentioning
confidence: 99%
“…In a static scene (i.e. stationary objects), vergence overcomes the problem of large disparities and allows the computation of the fixation point (Culverhouse et al 2009;Belhaoua et al 2010) and of the absolute depth map by a triangulation algorithm . If the disparities close to the fixation point are kept small, segmentation (Mishra et al 2009), stereo matching and object recognition (Das and Ahuja 1995;Belhaoua et al 2010) problems are facilitated.…”
Section: Introductionmentioning
confidence: 99%