A Fuzzy-Neural approach for estimation of depth map using focus

被引:12
|
作者
Malik, Aamir Saeed [1 ]
Nisar, Humaira [2 ]
Choi, Tae-Sun [2 ]
机构
[1] Univ Teknol Petronas, Dept Elect & Elect Engn, Tronoh 31750, Perak, Malaysia
[2] Gwangju Inst Sci & Technol, Dept Mechatron, Kwangju, South Korea
关键词
Depth map estimation; 3D shape recovery; Fuzzy logic; Neural Network; Back propagation; SHAPE; RECOVERY;
D O I
10.1016/j.asoc.2010.05.030
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Depth map is used for recovery of three-dimensional structure of the object which is required in many high level vision applications. In this paper, we present a new algorithm for the estimation of depth map for three-dimensional shape recovery. This algorithm is based on Fuzzy-Neural approach using shape from focus (SFF). A Fuzzy Inference System (FIS) is designed for the calculation of the depth map and an initial set of membership functions and fuzzy rules are proposed. Then Neural Network is used to train the FIS. The training is done using back propagation algorithm in combination with the least squares method. Hence, a new set of input membership functions are generated while discarding the initial ones. Lastly, the trained FIS is used to obtain final depth map. The results are compared with five other methods including the traditional SFF method and the Focused Image Surface SFF method (FISM). Six different types of objects are used for testing the proposed algorithm. (C) 2010 Elsevier B.V. All rights reserved.
引用
收藏
页码:1837 / 1850
页数:14
相关论文
共 50 条
  • [1] Refinement of fuzzy production rules by using a fuzzy-neural approach
    Huang, Dong-mei
    Ha, Ming-hu
    Li, Ya-min
    Tsang, Eric C. C.
    ADVANCES IN MACHINE LEARNING AND CYBERNETICS, 2006, 3930 : 517 - 527
  • [2] Depth map estimation using a robust focus measure
    Malik, Aamir Saeed
    Shim, Seong-O
    Choi, Tae-Sun
    2007 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, VOLS 1-7, 2007, : 3360 - 3363
  • [3] Urban traffic flow prediction using a fuzzy-neural approach
    Yin, HB
    Wong, SC
    Xu, JM
    Wong, CK
    TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2002, 10 (02) : 85 - 98
  • [4] A fuzzy-neural networks approach for multisensor fusion
    Yang, J
    ICEMI 2005: CONFERENCE PROCEEDINGS OF THE SEVENTH INTERNATIONAL CONFERENCE ON ELECTRONIC MEASUREMENT & INSTRUMENTS, VOL 4, 2005, : 668 - 671
  • [5] Modelling and motion control of an autonomous underwater robot using a fuzzy and fuzzy-neural approach
    Akkizidis, IS
    Roberts, GN
    MECHATRONICS '98, 1998, : 223 - 228
  • [6] A first approach to a taxonomy of fuzzy-neural systems
    Magdalena, L
    CONNECTIONIST-SYMBOLIC INTEGRATION: FROM UNIFIED TO HYBRID APPROACHES, 1997, : 69 - 88
  • [7] Oil reservoir properties estimation by fuzzy-neural networks
    Ho, Trong Long
    Ehara, Sachio
    Memoirs of the Faculty of Engineering, Kyushu University, 2007, 67 (03): : 117 - 141
  • [8] A NEW APPROACH TO FUZZY-NEURAL SYSTEM MODELING
    LIN, YH
    CUNNINGHAM, GA
    IEEE TRANSACTIONS ON FUZZY SYSTEMS, 1995, 3 (02) : 190 - 198
  • [9] Traffic flow prediction of signalized intersections using fuzzy-neural approach
    Yin, Hongbin
    Xu, Jianmin
    Huang, Shijin
    Zhongguo Gonglu Xuebao/China Journal of Highway and Transport, 2000, 13 (03): : 78 - 81
  • [10] A merged fuzzy-neural network and its application in fuzzy-neural control
    Li, I-Hsum
    Wang, Wei-Yen
    Su, Shun-Fen
    Chen, Ming-Chang
    2006 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS, VOLS 1-6, PROCEEDINGS, 2006, : 4529 - 4534