Multi-objective Cartesian Genetic Programming optimization of morphological filters in navigation systems for Visually Impaired People

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@Article{DOURADO:2020:ASC,

• author = "Antonio Miguel Batista Dourado and Emerson Carlos Pedrino",
• title = "Multi-objective Cartesian Genetic Programming optimization of morphological filters in navigation systems for Visually Impaired People",
• journal = "Applied Soft Computing",
• volume = "89",
• pages = "106130",
• year = "2020",
• ISSN = "1568-4946",
• DOI = "doi:10.1016/j.asoc.2020.106130",
• URL = "http://www.sciencedirect.com/science/article/pii/S1568494620300703",
• keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, Multi-objective optimization, NSGA-II, Mathematical morphology",
• abstract = "Navigation systems for Visually Impaired People (VIP) have improved in the last decade, incorporating many features to ensure navigation safety. Such systems often use grayscale depth images to segment obstacles and paths according to distances. However, this approach has the common problem of unknown distances. While this can be solved with good quality morphological filters, these might be too complex and power demanding. Considering navigation systems for VIP rely on limited energy sources that have to run multiple tasks, fixing unknown distance areas without major impacts on power consumption is a definite concern. Multi-objective optimization algorithms might improve filters' energy efficiency and output quality, which can be accomplished by means of different quality vs. complexity trade-offs. This study presents NSGA2CGP, a multi-objective optimization method that employs the NSGA-II algorithm on top of Cartesian Genetic Programming to optimize morphological filters for incomplete depth images used by navigation systems for VIP. Its goal is to minimize output errors and structuring element complexity, presenting several feasible alternatives combining different levels of filter quality and complexity-both of which affect power consumption. NSGA2CGP-optimized filters were deployed into an actual embedded platform, so as to experimentally measure power consumption and execution time. We also propose two new fitness functions based on existing approaches from literature. Results showed improvements in visual quality, performance, speed and power consumption, thanks to our proposed error function, proving NSGA2CGP as a solid method for developing and evolving efficient morphological filters",

}

Genetic Programming entries for Antonio Miguel Batista Dourado Emerson Carlos Pedrino

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