Probabilistic Evaluation of 3D Surfaces Using Statistical Shape Models (SSM)

Sensors
Javier PérezJuan-Carlos Perez-Cortes

Abstract

Inspecting a 3D object which shape has elastic manufacturing tolerances in order to find defects is a challenging and time-consuming task. This task usually involves humans, either in the specification stage followed by some automatic measurements, or in other points along the process. Even when a detailed inspection is performed, the measurements are limited to a few dimensions instead of a complete examination of the object. In this work, a probabilistic method to evaluate 3D surfaces is presented. This algorithm relies on a training stage to learn the shape of the object building a statistical shape model. Making use of this model, any inspected object can be evaluated obtaining a probability that the whole object or any of its dimensions are compatible with the model, thus allowing to easily find defective objects. Results in simulated and real environments are presented and compared to two different alternatives.

References

Jun 16, 2009·Medical Image Analysis·Tobias Heimann, Hans-Peter Meinzer
Oct 27, 2010·IEEE Transactions on Pattern Analysis and Machine Intelligence·Andriy Myronenko, Xubo Song
Aug 3, 2016·IEEE Transactions on Pattern Analysis and Machine Intelligence· Jin Xie Yi Fang
Oct 26, 2016·The Journal of Thoracic and Cardiovascular Surgery·Jan L BruseUNKNOWN Modeling of Congenital Hearts Alliance Collaborative Group
Feb 18, 2017·American Journal of Obstetrics and Gynecology·Andrea Dall'AstaChristoph C Lees
Mar 5, 2018·Medical Image Analysis·Jonas PichatMarc Modat
Sep 13, 2018·Sensors·Juan-Carlos Perez-CortesIsmael Salvador
Jan 1, 2018·International Journal of Computer Vision·James BoothStefanos Zafeiriou

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