Kernel and Feature Selection for Visible and Infrared based Obstacle Recognition

In this article we propose a fusion model at data-level based on a linear combination of kernels. These kernels functions will be evaluated on disjoint entries, on the signature acquired from visible respective infrared spectrum. Therefore, we have to choose the proper numeric signature for the visible and for the infrared images. In order to retain just the best suited features, different feature extraction and feature selection algorithms have been investigated. In this way, important information can be achieved in a small number of coefficients, implying thus a significant reduction of the computation time. Our purpose is to develop the obstacle recognition module and to examine if a visible-infrared fusion is efficient for this task.