Optimal depth estimation by combining focus measures using genetic programming

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@Article{Mahmood20111249,
  author =       "Muhammad Tariq Mahmood and Abdul Majid and 
                 Tae-Sun Choi",
  title =        "Optimal depth estimation by combining focus measures
                 using genetic programming",
  journal =      "Information Sciences",
  volume =       "181",
  number =       "7",
  pages =        "1249--1263",
  year =         "2011",
  ISSN =         "0020-0255",
  DOI =          "doi:10.1016/j.ins.2010.11.039",
  URL =          "http://www.sciencedirect.com/science/article/B6V0C-51N22D2-3/2/463bf41cb8ecb1292e814f690c94cf70",
  keywords =     "genetic algorithms, genetic programming, 3D shape
                 recovery, Focus measure, Shape From Focus, Combining
                 focus measures",
  abstract =     "Three-dimensional (3D) shape reconstruction is a
                 fundamental problem in machine vision applications.
                 Shape From Focus (SFF) is one of the passive optical
                 methods for 3D shape recovery that uses degree of focus
                 as a cue to estimate 3D shape. In this approach,
                 usually a single focus measure operator is applied to
                 measure the focus quality of each pixel in the image
                 sequence. However, the applicability of a single focus
                 measure is limited to estimate accurately the depth map
                 for diverse type of real objects. To address this
                 problem, we develop Optimal Composite Depth (OCD)
                 function through genetic programming (GP) for accurate
                 depth estimation. The OCD function is constructed by
                 optimally combining the primary information extracted
                 using one/or more focus measures. The genetically
                 developed composite function is then used to compute
                 the optimal depth map of objects. The performance of
                 the developed nonlinear function is investigated using
                 both the synthetic and the real world image sequences.
                 Experimental results demonstrate that the proposed
                 estimator is more useful in computing accurate depth
                 maps as compared to the existing SFF methods. Moreover,
                 it is found that the heterogeneous function is more
                 effective than homogeneous function.",
}

Genetic Programming entries for Muhammad Tariq Mahmood Abdul Majid Tae Sun Choi

Citations