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

来源：

APPLIED SOFT COMPUTING | 2011年 / 11卷 / 02期

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

页码：1837 / 1850

页数：14

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