Image Features Extraction, Selection And Fusion For Computer Vision

This chapter addresses many problems: different types of sensors, systems and methods from the literature are briefly revised, in order to give a recipe for designing intelligent vehicle systems based on computer vision. Many computer vision or related problems are addressed, like segmentation, features extraction and selection, fusion and classification. Existing solutions are investigated and three different data-bases are presented to perform typical experiments. Features extraction is aimed for finding pertinent features to encode information about possible obstacles from the road. Feature selection schemes are further used to compact the feature vector in order to decrease the computational time. Finally, several approaches to fuse visible and infrared images are used to increase the accuracy of the monomodal systems.