A fast sparse reconstruction approach for high resolution image-based object surface anomaly detection

Woon Huei Chai, Shen Shyang Ho, Chi Keong Goh, Liang Tien Chia, Hiok Chai Quek

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

    2 Scopus citations

    Abstract

    We propose an approach to resolve two issues in a recent proposed sparse reconstruction based, anomaly detection approach as a part of automated visual inspection (AVI). The original approach needs large computation and memory for high resolution problem. To solve it, we proposed a two-step sparse reconstruction, 1) the first sparse representation of input image is estimated in a sparse reconstruction with low resolution downsampled images and 2) the high resolution residual values is generated in another sparse reconstruction with the sparse representation. The first step provides the flexibility of freely adjusting the computation and the demand of memory storage with small trade-off of detection accuracy. Moreover, an illumination adaptive threshold with morphological operators is used in the anomaly classification. Empirical results show that the proposed approach can effectively replace the original approach with better results.

    Original languageEnglish (US)
    Title of host publicationProceedings of the 15th IAPR International Conference on Machine Vision Applications, MVA 2017
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages13-16
    Number of pages4
    ISBN (Electronic)9784901122160
    DOIs
    StatePublished - Jul 19 2017
    Event15th IAPR International Conference on Machine Vision Applications, MVA 2017 - Nagoya, Japan
    Duration: May 8 2017May 12 2017

    Publication series

    NameProceedings of the 15th IAPR International Conference on Machine Vision Applications, MVA 2017

    Other

    Other15th IAPR International Conference on Machine Vision Applications, MVA 2017
    Country/TerritoryJapan
    CityNagoya
    Period5/8/175/12/17

    All Science Journal Classification (ASJC) codes

    • Computer Science Applications
    • Computer Vision and Pattern Recognition

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