Abstract
The use of thermal imaging is a benefit for the military people. Due to their advantages, it has a large number of applications, including the detection of camouflaged people. For better results, the thermal information can be merged with the color information which allows a greater de-tail, resulting in a higher degree of security. The present work implemented as pixel level image fusion methods: Principal Components Analysis, Laplacian Pyramid, and Discrete Wavelet Transform. A qualitative analysis concluded that the method which performs better is the one that uses Wavelets, followed by the Laplacian Pyramid and finally the PCA. A quantitative analysis was made using the metrics: Standard Deviation, Entropy, Spatial Frequency, Mutual Information, Fusion Quality Index and Structural Similarity Index. The values obtained support the conclusions extracted from the qualitative analysis.
Highlights
Nowadays the majority of surveillance systems use detection systems through color, these systems are highly limited by luminosity
It is done a brief explanation of the methods used in this work: Principal Component Analysis (PCA), Laplacian Pyramid and Wavelets, and the metrics used for quantitative analysis
The method using the Wavelets is the one with better results because it is the one that does the fusion of the various image components in line with the established fusion rules to be the best for the intended purpose, which is to detect the camouflaged people
Summary
Nowadays the majority of surveillance systems use detection systems through color, these systems are highly limited by luminosity. The use of infrared cameras allows to capture the thermal image of an object, with a benefit for the military due to its ability to daytime and nighttime use, as well under different weather conditions [1] In this context, these images can be used to detect camouflaged people. While the color images give a visual context to objects, thermal images give information about objects with high temperature The fusion of both images gives a better visual perception of the scene and it allows a better detection of people. Metrics such as Standard Deviation, Entropy, CrossEntropy and Spatial Frequency were considered appropriate when there is no reference image They concluded that the Image fusion using Wavelets with a greater degree of decomposition has better performance
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