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| Computer Aided Visual Inspection of Aircraft Surfaces
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Full
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Source |
International Journal of Image Processing (IJIP) |
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Table of Contents |
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Complete Issue PDF(2.27MB) |
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Volume: 6 Issue: 1 |
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Pages: NULL |
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Publication
Date: February 2012 |
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ISSN
(Online): 1985-2304 |
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Pages |
38 - 53 |
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Published
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21-02-2012 |
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Publisher |
CSC
Journals, Kuala Lumpur,
Malaysia |
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ADDITIONAL
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| Keywords Abstract References Cited by Related Articles Collaborative
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KEYWORDS: Computer Vision, Contourlet Transform, Non Subsampled Conoturlet Transform, Discrete Cosine Transform, Neural Networks, Gabor Filter |
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| Non Destructive Inspections (NDI) plays a vital role in aircraft industry as it determines the structural integrity of aircraft surface and material characterization. The existing NDI methods are time consuming, we propose a new NDI approach using Digital Image Processing that has the potential to substantially decrease the inspection time. Automatic Marking of cracks have been achieved through application of Thresholding, Gabor Filter and Non Subsampled Contourlet transform. For a novel method of NDI, the aircraft imagery is analyzed by three methods i.e Neural Networks, Contourlet Transform (CT) and Discrete Cosine Transform (DCT). With the help of Contourlet Transform the two dimensional (2-D) spectrum is divided into fine slices, using iterated directional filterbanks. Next, directional energy components for each block of the decomposed subband outputs are computed. These energy values are used to distinguish between the crack and scratch images using the Dot Product classifier. In next approach, the aircraft imagery is decomposed into high and low frequency components using DCT and the first order moment is determined to form feature vectors.A correlation based approach is then used for distinction between crack and scratch surfaces. A comparative examination between the two techniques on a database of crack and scratch images revealed that texture analysis using the combined transform based approach gave the best results by giving an accuracy of 96.6% for the identification of crack surfaces and 98.3% for scratch surfaces.
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| Rafia Mumtaz : Colleagues
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| Mustafa Mumtaz : Colleagues
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| Atif Bin Mansoor : Colleagues
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| Hassan Masood : Colleagues
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