Combining Focus Measures for Three Dimensional Shape Estimation Using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "Muhammad Tariq Mahmood and Tae-Sun Choi",
  title =        "Combining Focus Measures for Three Dimensional Shape
                 Estimation Using Genetic Programming",
  booktitle =    "Depth Map and {3D} Imaging Applications: Algorithms
                 and Technologies",
  publisher =    "IGI Global",
  year =         "2012",
  editor =       "Aamir Saeed Malik and Tae Sun Choi and Humaira Nisar",
  chapter =      "11",
  pages =        "209--228",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "9781613503263",
  DOI =          "doi:10.4018/978-1-61350-326-3.ch011",
  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, which 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 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 introduce the development of optimal
                 composite depth (OCD) function through genetic
                 programming (GP) for accurate depth estimation. The OCD
                 function is developed through optimally combining the
                 primary information extracted using one (homogeneous
                 features) or more focus measures (heterogeneous
                 features). The genetically developed composite function
                 is then used to compute the optimal depth map of
                 objects. The performance of this function is
                 investigated using both synthetic and real world image
                 sequences. Experimental results demonstrate that the
                 proposed estimator is more accurate than existing SFF
                 methods. Further, it is found that heterogeneous
                 function is more effective than homogeneous function.",

Genetic Programming entries for Muhammad Tariq Mahmood Tae Sun Choi