Evolutionary Approach for Automatic Generation of Multi-Objective Morphological Filters for Depth Images in Embedded Navigation Systems

The efforts spent on the development of assistive technologies has led researches to explore many existing techniques as computer vision, image processing, etc. and apply them as embedded solutions to help people with several types of disabilities, including visual impairment. Embedded navigation systems for visually impaired people (VIP) often use RGB-D cameras to retrieve depth information from surroundings and present them as gray images with depth represented by gray level or black pixels if depth couldn't be estimated, which can be fixed by mathematical morphology. Morphological filters must be efficient to solve the problem and fast to avoid impact on performance. This paper presents an approach for automatic generation and optimization of low complexity and low error morphological filters to fix depth image’s unknown distances based on NSGA-II and Cartesian Genetic Programming. Experiments were performed using two different error metrics and results showed that the presented approach managed to generate and optimize feasible morphological filters that fit within embedded navigation systems for VIP.