Thermal and Visible Image Fusion: a Machine Learning Approach


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Abstract


Both in military defense and civilian applications, an increasing interest is being shown in fusing information from infrared and visible sensors in order to get precise situational assessment. This paper presents a novel pixel-based infrared and visible image fusion algorithm. In this study, linear and nonlinear features in image data are dealt independent of each other. Image is first transformed to wavelet transform domain, splitting its linear features from nonlinear ones, and fusing linear and nonlinear features using different set of fusion rules. The technique successfully incorporates a linear classifier to fuse the linear features and an RBF kernel-based Principal Component Analysis to fuse the nonlinear features in data. Evaluation of the proposed technique through an image database of 32 registered image pairs shows that the proposed method is quite effective for online applications.
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Keywords


Image Fusion; Thermal and Visible Images; Support Vector Machines (SVM) and Kernel Principal Component Analysis (K-PCA)

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References


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