Evaluation of Local Feature Extraction Methods Generated through Genetic Programming on Visual SLAM

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@Misc{oai:HAL:hal-01207508v1,
  author =       "Victor R. Lopez-Lopez and Leonardo Trujillo and 
                 Pierrick Legrand and Victor H. Diaz-Ramirez",
  title =        "Evaluation of Local Feature Extraction Methods
                 Generated through Genetic Programming on Visual
                 {SLAM}",
  howpublished = "HAL CCSD",
  year =         "2014",
  keywords =     "genetic algorithms, genetic programming",
  type =         "info:eu-repo/semantics/conferenceObject",
  URL =          "https://hal.inria.fr/hal-01207508",
  URL =          "https://www.researchgate.net/publication/282864073_Evaluation_of_Local_Feature_Extraction_Methods_Generated_through_Genetic_Programming_on_Visual_SLAM",
  annote =       "Instituto Tecnol{\'o}gico de Tijuana [Tijuana];
                 Universit{\'e} de Bordeaux (UB); Institut de
                 Math{\'e}matiques de Bordeaux (IMB) ; Universit{\'e}
                 Bordeaux Segalen - Bordeaux 2 - Universit{\'e} Sciences
                 et Technologies - Bordeaux 1 - CNRS",
  bibsource =    "OAI-PMH server at api.archives-ouvertes.fr",
  contributor =  "INRIA Bordeaux - Sud-Ouest and INRIA",
  coverage =     "Ixtapa, Mexico",
  description =  "International audience",
  identifier =   "hal-01207508",
  language =     "en",
  oai =          "oai:HAL:hal-01207508v1",
  source =       "Proceedings of the 2014 IEEE International Autumn
                 Meeting on Power, Electronics and Computing (ROPEC
                 2014); Proceedings of the 2014 IEEE International
                 Autumn Meeting on Power, Electronics and Computing
                 (ROPEC 2014), 2014, Ixtapa, Mexico",
  size =         "6 pages",
  abstract =     "The detection and description of locally salient
                 regions is one of the most widely used low-level
                 processes in modern computer vision systems. The
                 general approach relies on the detection of stable and
                 invariant image features, that can be uniquely
                 characterized using compact numerical vectors or
                 descriptors. Many detection and description algorithms
                 have been proposed, most of them derived using
                 different assumptions, problem models or analysis
                 techniques. On the other hand, the current work focuses
                 on detection and description algorithms that were
                 automatically generated using genetic programming (GP),
                 an evolutionary algorithm intended for automatic
                 program induction. In particular, the goal is to
                 determine if these operators are competitive with
                 traditional techniques in a real-world scenario,
                 specifically a vision-based SLAM system. Obtained
                 results indicate that operators that were automatically
                 generated using GP achieve very strong performance,
                 clearly outperforming standard techniques. It seems
                 that the GP-based design process is indeed capable of
                 producing robust and efficient solutions, that can be
                 used as off-the shelf tools for difficult computer
                 vision applications.",
  notes =        "July 2016 NOT in ROPER 2014 proceedings
                 http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?filter%3DAND%28p_IS_Number%3A7036277%29&rowsPerPage=75&pageNumber=1&resultAction=REFINE&resultAction=ROWS_PER_PAGE#

                 http://www.proceedings.com/25242.html",
}

Genetic Programming entries for Victor Raul Lopez Lopez Leonardo Trujillo Pierrick Legrand Victor H Diaz-Ramirez

Citations