Depth estimation of metallic objects using multiwavelets scale-space representation
Bhatti, A. and Nahavandi, S. 2008, Depth estimation of metallic objects using multiwavelets scale-space representation, Current development in theory and applications of wavelets, vol. 2, no. 2, pp. 175-207.
(Some files may be inaccessible until you login with your Deakin Research Online credentials)
The problem of dimensional defects in aluminum die-castings is widespread throughout the foundry industry and their detection is of paramount importance in maintaining product quality. Due to the unpredictable factory environment and metallic with highly reflective nature, it is extremely hard to estimate true dimensionality of these metallic parts, autonomously. Some existing vision systems are capable of estimating depth to high accuracy, however are very much hardware dependent, involving the use of light and laser pattern projectors, integrated into vision systems or laser scanners. However, due to the reflective nature of these metallic parts and variable factory environments, the aforementioned vision systems tend to exhibit unpromising performance. Moreover, hardware dependency makes these systems cumbersome and costly. In this work, we propose a novel robust 3D reconstruction algorithm capable of reconstructing dimensionally accurate 3D depth models of the aluminum die-castings. The developed system is very simple and cost effective as it consists of only a pair of stereo cameras and a defused fluorescent light. The proposed vision system is capable of estimating surface depths within the accuracy of 0.5mm. In addition, the system is invariant to illuminative variations as well as orientation and location of the objects on the input image space, making the developed system highly robust. Due to its hardware simplicity and robustness, it can be implemented in different factory environments without a significant change in the setup. The proposed system is a major part of quality inspection system for the automotive manufacturing industry.
Full text only available to subscribers : http://pphmj.com/abstract/3594.htm
Field of Research
080109 Pattern Recognition and Data Mining
Socio Economic Objective
890299 Computer Software and Services not elsewhere classified
Unless expressly stated otherwise, the copyright for items in Deakin Research Online is owned by the author, with all rights reserved.
Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO.
If you believe that your rights have been infringed by this repository, please contact email@example.com.