A coarse-grained spectral signature generator

Lam, K.P.; Austin, J.C.; Day, C.R.

Abstract

This paper investigates the method for object fingerprinting in the context of element specific x-ray imaging. In particular, the use of spectral descriptors that are illumination invariant and viewpoint independent for pattern identification was examined in some detail. To improve generating the relevant "signature", the spectral descriptor constructed is enhanced with a differentiator which has built-in noise filtration capability and good localisation properties, thus facilitating the extraction of element specific features at a coarse-grained level. In addition to the demonstrable efficacy in identifying significant image intensity transitions that are associated with the underlying physical process of interest, the method has the distinct advantage of being conceptually simple and computationally efficient. These latter properties allow the descriptor to be further utilised by an intelligent system capable of performing a fine-grained analysis of the extracted pattern signatures. The performance of the spectral descriptor has been studied in terms of the quality of the signature vectors that it generated, quantitatively based on the established framework of Spectral Information Measure (SIM). Early results suggested that such a multiscale approach of image sequence analysis offers a considerable potential for real-time applications.

Citation

Lam, K., Austin, J., & Day, C. (2007, May). A coarse-grained spectral signature generator. Presented at Eighth International Conference on Quality Control by Artificial Vision, Le Creusot, France

• Presentation Conference Type: Conference Paper (published)
• Conference Name: Eighth International Conference on Quality Control by Artificial Vision
• Start Date: May 23, 2007
• End Date: May 25, 2007
• Online Publication Date: May 29, 2007
• Publication Date: May 29, 2007
• Deposit Date: Jun 5, 2024
• Publisher: SPIE
• Volume: 6356
• ISBN: 0819464511; 9780819464514
• DOI: https://doi.org/10.1117/12.736723
• Public URL: https://keele-repository.worktribe.com/output/845005