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Proceedings Paper

Grayscale enhancement techniques of x-ray images of carry-on luggage
Author(s): Besma Abidi; Mark Mitckes; Mongi A. Abidi; Jimin Liang
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Paper Abstract

Very few image processing applications dealt with x-ray luggage scenes in the past. In this paper, a series of common image enhancement techniques are first applied to x-ray data and results shown and compared. A novel simple enhancement method for data de-cluttering, called image hashing, is then described. Initially, this method was applied using manually selected thresholds, where progressively de-cluttered slices were generated and displayed for screeners. Further automation of the hashing algorithm (multi-thresholding) for the selection of a single optimum slice for screener interpretation was then implemented. Most of the existing approaches for automatic multi-thresholding, data clustering, and cluster validity measures require prior knowledge of the number of thresholds or clusters, which is unknown in the case of luggage scenes, given the variety and unpredictability of the scene’s content. A novel metric based on the Radon transform was developed. This algorithm finds the optimum number and values of thresholds to be used in any multi-thresholding or unsupervised clustering algorithm. A comparison between the newly developed metric and other known metrics for image clustering is performed. Clustering results from various methods demonstrate the advantages of the new approach.

Paper Details

Date Published: 1 May 2003
PDF: 13 pages
Proc. SPIE 5132, Sixth International Conference on Quality Control by Artificial Vision, (1 May 2003); doi: 10.1117/12.515228
Show Author Affiliations
Besma Abidi, Univ. of Tennessee (United States)
Mark Mitckes, Univ. of Tennessee (United States)
Mongi A. Abidi, Univ. of Tennessee (United States)
Jimin Liang, Univ. of Tennessee (United States)


Published in SPIE Proceedings Vol. 5132:
Sixth International Conference on Quality Control by Artificial Vision
Kenneth W. Tobin; Fabrice Meriaudeau, Editor(s)

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