Interest point detection through multiobjective genetic programming

Published in Journal Articles

Abstract

The detection of stable and informative image points is one of the most important low-level problems in modern computer vision. This paper proposes a multiobjective genetic programming (MO-GP) approach for the automatic synthesis of operators that detect interest points. The proposal is unique for interest point detection because it poses a MO formulation of the point detection problem. The search objectives for the MO-GP search consider three properties that are widely expressed as desirable for an interest point detector, these are: (1) stability; (2) point dispersion; and (3) high information content. The results suggest that the point detection task is a MO problem, and that different operators can provide different trade-offs among the objectives. In fact, MO-GP is able to find several sets of Pareto optimal operators, whose performance is validated on standardized procedures including an extensive test with 500 images; as a result, we could say that all solutions found by the system dominate previously man-made detectors in the Pareto sense. In conclusion, the MO formulation of the interest point detection problem provides the appropriate framework for the automatic design of image operators that achieve interesting trade-offs between relevant performance criteria that are meaningful for a variety of vision tasks.

  1. Gustavo Olague and Leonardo Trujillo. Interest point detection through multiobjective genetic programming. Appl. Soft Comput. 12(8):2566-2582, 2012. BibTeX

    @article{DBLP:journals/asc/OlagueT12,
    	author = "Gustavo Olague and Leonardo Trujillo",
    	title = "Interest point detection through multiobjective genetic programming",
    	journal = "Appl. Soft Comput.",
    	volume = 12,
    	number = 8,
    	year = 2012,
    	pages = "2566-2582",
    	ee = "http://dx.doi.org/10.1016/j.asoc.2012.03.058",
    	bibsource = "DBLP, http://dblp.uni-trier.de"
    }
    

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Multiple Objective Genetic Algorithms for Path-planning Optimization in Autonomous Mobile Robots

Published in Journal Articles

Abstract

This paper describes the use of a genetic algorithm (GA) for the problem of offline point-to-point autonomous mobile robot path planning. The problem consists of generating “valid” paths or trajectories, for an Holonomic Robot to use to move from a starting position to a destination across a flat map of a terrain, represented by a two-dimensional grid, with obstacles and dangerous ground that the Robot must evade. This means that the GA optimizes possible paths based on two criteria: length and difficulty. First, we decided to use a conventional GA to evaluate its ability to solve this problem (using only one criteria for optimization). Due to the fact that we also wanted to optimize paths under two criteria or objectives, then we extended the conventional GA to implement the ideas of Pareto optimality, making it a multi-objective genetic algorithm (MOGA). We describe useful performance measures and simulation results of the conventional GA and of the MOGA that show that both types of GAs are effective tools for solving the point-to-point path-planning problem.

  1. Oscar Castillo, Leonardo Trujillo and Patricia Melin. Multiple Objective Genetic Algorithms for Path-planning Optimization in Autonomous Mobile Robots. Soft Comput. 11(3):269–279, 2006. URL, DOI BibTeX

    @article{Castillo:2006:MOG:1178398.1178406,
    	author = "Castillo, Oscar and Trujillo, Leonardo and Melin, Patricia",
    	title = "Multiple Objective Genetic Algorithms for Path-planning Optimization in Autonomous Mobile Robots",
    	journal = "Soft Comput.",
    	issue_date = "October 2006",
    	volume = 11,
    	number = 3,
    	month = "",
    	year = 2006,
    	issn = "1432-7643",
    	pages = "269--279",
    	numpages = 11,
    	url = "http://dx.doi.org/10.1007/s00500-006-0068-4",
    	doi = "10.1007/s00500-006-0068-4",
    	acmid = 1178406,
    	publisher = "Springer-Verlag",
    	address = "Berlin, Heidelberg",
    	keywords = "Autonomous robots, Genetic algorithms, Multiple objective optimization, Path planning"
    }
    

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