Fast interactive segmentation of natural images using the image foresting transform

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Spina:2009:DSP,
  author =       "T. V. Spina and Javier A. Montoya-Zegarra and 
                 A. X. Falcao and P. A. V. Miranda",
  title =        "Fast interactive segmentation of natural images using
                 the image foresting transform",
  booktitle =    "16th International Conference on Digital Signal
                 Processing",
  year =         "2009",
  month =        jul,
  pages =        "1--8",
  keywords =     "genetic algorithms, genetic programming, IFT,
                 connectivity functions, fast interactive segmentation,
                 feature extraction, feature selection, fuzzy
                 classification, image enhancement, image foresting
                 transform, image processing operators, image
                 recognition, natural images, path-value functions,
                 feature extraction, fuzzy set theory, graph theory,
                 image enhancement, image recognition, image
                 segmentation",
  DOI =          "doi:10.1109/ICDSP.2009.5201044",
  abstract =     "This paper presents an unified framework for fast
                 interactive segmentation of natural images using the
                 image foresting transform (IFT) - a tool for the design
                 of image processing operators based on connectivity
                 functions (path-value functions) in graphs derived from
                 the image. It mainly consists of three tasks:
                 recognition, enhancement, and extraction. Recognition
                 is the only interactive task, where representative
                 image properties for enhancement and the object's
                 location for extraction are indicated by drawing a few
                 markers in the image. Enhancement increases the
                 dissimilarities between object and background for more
                 effective object extraction, which completes
                 segmentation. We show through extensive experiments
                 that, by exploiting the synergism between user and
                 computer for recognition and enhancement, respectively,
                 as a separated step from recognition and extraction,
                 respectively, one can reduce user involvement with
                 better accuracy. We also describe new methods for
                 enhancement based on fuzzy classification by IFT and
                 for feature selection and/or combination by genetic
                 programming.",
  notes =        "Also known as \cite{5201044}",
}

Genetic Programming entries for Thiago Vallin Spina Javier A Montoya Zegarra Alexandre X Falcao Paulo A V de Miranda

Citations