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

Authors:
Aamir Saeed Malik;Humaira Nisar;Tae-Sun Choi
Affiliations:
Department of Electrical & Electronic Engineering, Universiti Teknologi Petronas, Bandar Sei Iskandar, 31750 Tronoh, Perak, Malaysia;Department of Mechatronics, Gwangju Institute of Science and Technology, Republic of Korea;Department of Mechatronics, Gwangju Institute of Science and Technology, Republic of Korea
Venue:
Applied Soft Computing
Year:
2011

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Abstract

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.