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13 May 2019 Nonparametric kernel smoothing classification to enhance optical correlation decision performances
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Abstract
Optical correlation is a pattern recognition method which is very famous to recognize an image from a database. It is simple to implement, to use and allows to obtain good performances. However, it suffers from a global decision based on the location, height and shape of the correlation peak within the correlation plane. It entails a considerable reduction of its robustness. Moreover, the correlation is sensitive to the rotation, to the scale, it pulls a deformation on the correlation plane which will decrease the performances of this method. In this paper, to overcome these problems, we propose and validate a new method of nonparametric modelling of the correlation plane. This method is based on a kernel estimation of the regression function used to classify the individuals according to the correlation plane. The idea is to enhance the decision by taking into consideration the shape and the distribution of energy in the correlation plane. This relies on calculations of the Hausdorff distance between the target correlation plane and the correlation planes coming from the database. The results showed the very good performance of our method compared to other in the literature especially in terms of a significant rate of good detection and a very low rate of false alarm.
Conference Presentation
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Matthieu Saumard, Marwa El Bouz, Michaël Aron, and Ayman Alfalou "Nonparametric kernel smoothing classification to enhance optical correlation decision performances", Proc. SPIE 10995, Pattern Recognition and Tracking XXX, 109950C (13 May 2019); https://doi.org/10.1117/12.2518840
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