Abstract
Image enhancement is very important for increasing the sensitivity of screening luggage performance at airports. On the basis of 11 statistical measures of image viewability we propose a novel approach to optimizing the choice of image enhancement tools. We propose a neural network predictor that can be used for predicting, on a given test image, the best image enhancement algorithm for it. The network is trained using a number of image examples. The input to the neural network is a set of viewability measures and its output is the choice of enhancement algorithm for that image. On a number of test images we show that such a predictive system is highly capable in forecasting the correct choice of enhancement algorithms (as judged by human experts). We compare our predictive system against a baseline approach that uses a fixed enhancement algorithm for all batch test images, and find the proposed model to be substantially superior.
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Singh, M., Singh, S. (2005). Image Enhancement Optimization for Hand-Luggage Screening at Airports. In: Singh, S., Singh, M., Apte, C., Perner, P. (eds) Pattern Recognition and Image Analysis. ICAPR 2005. Lecture Notes in Computer Science, vol 3687. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11552499_1
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DOI: https://doi.org/10.1007/11552499_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28833-6
Online ISBN: 978-3-540-31999-3
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