Automated visual inspection of ripple defects using wavelet characteristic based multivariate statistical approach

@article{Lin2007AutomatedVI,
  title={Automated visual inspection of ripple defects using wavelet characteristic based multivariate statistical approach},
  author={Hong-Dar Lin},
  journal={Image Vision Comput.},
  year={2007},
  volume={25},
  pages={1785-1801}
}
This paper presents a wavelet characteristic based approach for the automated visual inspection of ripple defects in the surface barrier layer (SBL) chips of ceramic capacitors. Difficulties exist in automatically inspecting ripple defects because of their semi-opaque and unstructured appearances, the gradual changes of their intensity levels, and the low intensity contrast between their surfaces and the rough exterior of a SBL chip. To overcome these difficulties, we first utilize wavelet… CONTINUE READING
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Key Quantitative Results

  • Experimental results show that the proposed approach (Hotelling T) achieves a 93.75% probability of accurately detecting the existence of ripple defects and an approximate 90% probability of correctly segmenting their regions.
  • Experimental results show that the proposed approach (Hotelling T2) achieves a 93.75% probability of accurately detecting the existence of ripple defects and an approximate 90% probability of correctly segmenting their regions.

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