A 3D object classifier for discriminating manufacturing processes

Ip, Cheuk Yiu; Regli, William C.

doi:10.1016/j.cag.2006.08.013

Cited by 29 publications

(9 citation statements)

References 25 publications

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“…Such an approach has been considered because these differential properties are currently used by algorithms for segmentation, recognition and registration of range images [13][14][15]. Fig.…”

Section: Surface Damage Recognition By Curvature Computationmentioning

confidence: 99%

“…Sampling step (cm) 1.5 0.7 Sub-area size (cm) 15 In case of the column, the comparison between reference visual recognition and K-based recognition has been performed on the entire surface, whereas in case of the beam such a recognition has been performed on four zones each consisting of 448 sub-areas and the corresponding results have been extrapolated to the entire surface. Sub-areas where the computation has not provided results are considered as incorrectly recognized.…”

Section: Column Beammentioning

confidence: 99%

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Contactless recognition of concrete surface damage from laser scanning and curvature computation

Teza

Galgaro

Moro

2009

NDT & E International

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Section: Surface Damage Recognition By Curvature Computationmentioning

confidence: 99%

Section: Column Beammentioning

confidence: 99%

Contactless recognition of concrete surface damage from laser scanning and curvature computation

Teza

Galgaro

Moro

2009

NDT & E International

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“…Our work is similar in spirit to the manufacturing classification problem of CAD models in [6], where a methodology for discriminating between two machining processes of mechanical artifacts was presented. However, our aim is to identify an unknown number of "machining processes", each of which will define a certain set of rules for a specific CAD operation.…”

Section: Introductionmentioning

confidence: 94%

Freeform shape clustering for customized design automation

Zouhar

Baloch

Azernikov

et al. 2009

2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops

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Automation can provide significant performance improvements in digital manufacturing systems that customize shapes of implants and prosthetic devices to the anatomy of a patient. The challenge, however, lies in the ability of an automatic solution to adapt to anatomical variations of a given object category. This paper presents a hierarchical framework that generalizes the digital design of anatomical surface models in terms of a small number of prototypes. The latter are derived from the local shape information of constituent parts via shape matching and clustering and then associated with one operation that dictates how a shape undergoes modification. We demonstrate the proposed technique through application to typical hearing aid design operations with promising results.

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“…Only three methods in [36] are compared by [34], thus the results of the other methods are not presented here. In each class, we apply five different levels (1)(2)(3)(4)(5) of strength for the transformation: the higher the number, the stronger the transformation. Repeatability rate is defined in [34] as the percentage of the detected points that are common in two different instances of an object.…”

Section: Interest Points Extractionmentioning

confidence: 99%