A Novel Genetic Programming Algorithm for Designing Morphological Image Analysis Method

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

@InProceedings{conf/swarm/WangT11,
  author =       "Jun Wang2 and Ying Tan",
  title =        "A Novel Genetic Programming Algorithm for Designing
                 Morphological Image Analysis Method",
  booktitle =    "Proceedings of the Second International Conference on
                 Advances in Swarm Intelligence (ICSI 2011) Part {I}",
  editor =       "Ying Tan and Yuhui Shi and Yi Chai and Guoyin Wang",
  year =         "2011",
  volume =       "6728",
  series =       "Lecture Notes in Computer Science",
  pages =        "549--558",
  address =      "Chongqing, China",
  month =        jun # " 12-15",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-642-21514-8",
  DOI =          "doi:10.1007/978-3-642-21515-5_65",
  size =         "10 pages",
  abstract =     "In this paper, we propose an applicable genetic
                 programming approach to solve the problems of binary
                 image analysis and gray scale image enhancement. Given
                 a section of original image and the corresponding goal
                 image, the proposed algorithm evolves for generations
                 and produces a mathematic morphological operation
                 sequence, and the result performed by which is close to
                 the goal. When the operation sequence is applied to the
                 whole image, the objective of image analysis is
                 achieved. In this sequence, only basic morphological
                 operations- erosion and dilation, and logical
                 operations are used. The well-defined chromosome
                 structure leads brings about more complex morphological
                 operations can be composed in a short sequence. Because
                 of a reasonable evolution strategy, the evolution
                 effectiveness of this algorithm is guaranteed. Tested
                 by the binary image features analysis, this algorithm
                 runs faster and is more accurate and intelligible than
                 previous works. In addition, when this algorithm is
                 applied to infrared finger vein grey scale images to
                 enhance the region of interest, more accurate features
                 are extracted and the accuracy of discrimination is
                 promoted.",
  affiliation =  "Key Laboratory of Machine Perception (Ministry of
                 Education), Peking University, P.R. China",
  bibdate =      "2011-06-06",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/conf/swarm/icsi2011-1.html#WangT11",
}

Genetic Programming entries for Jun Wang2 Ying Tan

Citations