Automatic Sensor Placement for Accurate Dimensional Inspection

Mason, Scott; Grün, Armin

doi:10.1006/cviu.1995.1034

Cited by 35 publications

(20 citation statements)

References 16 publications

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“…This problem is similar to the widely studied problems of camera network design and the next best view (Mason and Grun 1995;Mason 1997;Pito 1999;Olague and Mohr 2002). The optimization problem in that case is similar to our problem.…”

Section: / ( 1)mentioning

confidence: 63%

Towards Multiple 3D Bone Surface Identification and Reconstruction Using Few 2D X-Ray Images for Intraoperative Applications

Prakoonwit

2014

International Journal of Art, Culture, Design, and Technology

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This article discusses a possible method to use a small number, e.g. 5, of conventional 2D X-ray images to reconstruct multiple 3D bone surfaces intraoperatively. Each bone's edge contours in X-ray images are automatically identified. Sparse 3D landmark points of each bone are automatically reconstructed by pairing the 2D X-ray images. The reconstructed landmark point distribution on a surface is approximately optimal covering main characteristics of the surface. A statistical shape model, dense point distribution model (DPDM), is then used to fit the reconstructed optimal landmarks vertices to reconstruct a full surface of each bone separately. The reconstructed surfaces can then be visualised and manipulated by surgeons or used by surgical robotic systems.

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Section: / ( 1)mentioning

confidence: 63%

Towards Multiple 3D Bone Surface Identification and Reconstruction Using Few 2D X-Ray Images for Intraoperative Applications

Prakoonwit

2014

International Journal of Art, Culture, Design, and Technology

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“…A recent work in the area is that of Roberts and Marshall [29], who present a viewpoint selection scheme for complete surface coverage of 3-D objects. Some important earlier work in the area include those of Cowan and Kovesi [30], Tarbox and Gottschlich [31] and Mason and Grun [32].…”

Section: Object Feature Detectionmentioning

confidence: 99%

Active recognition through next view planning: a survey

Roy

Chaudhury

Banerjee

2004

Pattern Recognition

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Abstract3-D object recognition involves using image-computable features to identify 3-D object. A single view of a 3-D object may not contain sufficient features to recognize it unambiguously. One needs to plan different views around the given object in order to recognize it. Such a task involves an active sensor -one whose parameters (external and/or internal) can be changed in a purposive manner. In this paper, we review two important applications of an active sensor. We first survey important approaches to active 3-D object recognition. Next, we review existing approaches towards another important application of an active sensor namely, that of scene analysis and interpretation.

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“…Consequently, it can beconcluded that a trial and error approach to FOD is unsatisfactory and not tractable. Finally, a work called CONSENS which follows the expert system approach and uses multiple cameras in combination with optical triangulation, was developed by Mason et al MG95 . It outlines a method of overcoming these limitations.…”

Section: Related Workmentioning

confidence: 99%

Optimal camera placement for accurate reconstruction

Olague

Mohr²

2002

Pattern Recognition

137

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Th eme 3 | I n teraction homme-machine, images, donn ees, connaissances Projet MOVI Rapport de recherche n3338 | Janvier 1998 | 34 pagesAbstract: 3D measurements can berecovered from several views by triangulation. This paper deals with the problem of where to place the cameras in order to obtain a minimal error in the 3D measurements, also called camera network design in photogrammetry. We pose the problem in terms of an optimization design, dividing it into two main components: 1 an analytical part dedicated to the analysis of error propagation from which a criterion is derived, 2 a global optimization process to minimizes this criterion. In this way, the approach consists of an uncertainty analysis applied to the reconstruction process from which a covariance matrix is computed. This matrix represents the uncertainty of the detection from which the criterion is derived. Moreover, the optimization has discontinuities mainly due to the unobservability of points, which leads to a combinatorial optimization process. These aspects are solved using a multicellular genetic algorithm. Experimental results are provided to illustrate the e ectiveness and e ciency of the solution.Keywords: Camera network design, uncertainty analysis, global optimization, covariance matrix, genetic algorithms.R esum e : tsvp De ce côt e-l a, l'approche consiste en une analyse d'incertitude appliqu ee au processus de reconstruction d'o u une matrice de covariance sera calcul ee. Cette matrice repr esente l'incertitude de la detection pour lequel le crit ere est d eriv e. Par ailleurs, l'optimisation a des aspects discontinus essentiellement dû a l'inobservabilit e des points. Ce facteur va nous amener a utiliser un processus d'optimisation combinatoire que nous avons r esolu en utilisant un algorithme g en etique multicellulaire. Des resultats exp erimentaux sont inclus pour illustrer l'e cacit e et la rapidit e de la solution.

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Automatic Sensor Placement for Accurate Dimensional Inspection

Cited by 35 publications

References 16 publications

Towards Multiple 3D Bone Surface Identification and Reconstruction Using Few 2D X-Ray Images for Intraoperative Applications

Towards Multiple 3D Bone Surface Identification and Reconstruction Using Few 2D X-Ray Images for Intraoperative Applications

Active recognition through next view planning: a survey

Optimal camera placement for accurate reconstruction

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