Exploiting sparsity for image-based object surface anomaly detection

Woon Huei Chai, Shen Shyang Ho, Chi Keong Goh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

The anomaly detection task plays an important role in quality control in many industrial or manufacturing processes. However, in many such processes, anomaly detection is done visually by human experts who have in-depth knowledge and vast experience on a product in order to perform well in the detection task. In this paper, we present an approach that (i) identifies anomalies in an image based on the sparse residuals (or errors) obtained during image reconstruction using sparse representation and (ii) learns the threshold to classify an image pixel based on its residual value. The intuitions for our proposed sparse approximation driven approach are, namely: (i) anomalies are infrequent and (ii) anomalies are unwanted portions of an image reconstruction. Empirical results on a real-world image dataset for an industrial surface defect detection task are used to demonstrate the feasibility of our proposed approach.

Original languageEnglish (US)
Title of host publication2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1986-1990
Number of pages5
ISBN (Electronic)9781479999880
DOIs
StatePublished - May 18 2016
Event41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Shanghai, China
Duration: Mar 20 2016Mar 25 2016

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2016-May
ISSN (Print)1520-6149

Other

Other41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016
CountryChina
CityShanghai
Period3/20/163/25/16

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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    Chai, W. H., Ho, S. S., & Goh, C. K. (2016). Exploiting sparsity for image-based object surface anomaly detection. In 2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings (pp. 1986-1990). [7472024] (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2016-May). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP.2016.7472024