Comparison of Local Feature Extraction Paradigms Applied to Visual SLAM

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Victor R. Lopez-Lopez and Leonardo Trujillo and 
                 Pierrick Legrand and Victor H. Diaz-Ramirez and 
                 Gustavo Olague",
  title =        "Comparison of Local Feature Extraction Paradigms
                 Applied to Visual SLAM",
  journal =      "Computacion y Sistemas",
  year =         "2016",
  volume =       "20",
  number =       "4",
  pages =        "565--587",
  note =         "Thematic Issue: Research Advances and Applications of
                 Evolutionary Computation",
  keywords =     "genetic algorithms, genetic programming, Local
                 features, composite correlation filter, SLAM",
  ISSN =         "1405-5546",
  URL =          "",
  URL =          "",
  DOI =          "doi:10.13053/CyS-20-4-2500",
  size =         "23 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 descriptors. Many detection
                 and description algorithms have been proposed, most of
                 them derived using different assumptions or problem
                 models. This work presents a comparison of different
                 approaches towards the feature extraction problem,
                 namely: (1) standard computer vision techniques, (2)
                 automatic synthesis techniques based on genetic
                 programming (GP), and (3) a new local descriptor based
                 on composite correlation filtering, proposed for the
                 first time in this paper. The considered methods are
                 evaluated on a difficult real-world problem,
                 vision-based simultaneous localization and mapping
                 (SLAM). Using three experimental scenarios, results
                 indicate that the GP-based methods and the correlation
                 filtering techniques outperform widely used computer
                 vision algorithms such as the Harris and Shi-Tomasi
                 detectors and the Speeded Up Robust Features
  notes =        "In english.

                 robot RX-60 with a web-cam",

Genetic Programming entries for Victor Raul Lopez Lopez Leonardo Trujillo Pierrick Legrand Victor Hugo Diaz-Ramirez Gustavo Olague