Presentation
A fast sparse reconstruction approach for high resolution image-based object surface anomaly detection
Fifteenth IAPR International Conference on Machine Vision Applications
(2017)
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.
Disciplines
Publication Date
May, 2017
Location
Nagoya, Japan
DOI
10.23919/MVA.2017.7986761
Comments
Conference proceeding is in IEEE Xplore database.
This paper is on pages 13-16.
Citation Information
Woon Huei Chai, Shen-Shyang Ho, Chi-Keong Goh, Liang-Tien Chia, et al.. "A fast sparse reconstruction approach for high resolution image-based object surface anomaly detection" Fifteenth IAPR International Conference on Machine Vision Applications (2017) Available at: http://works.bepress.com/shen-shyang-ho/6/