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Combining Focus Measures for Three Dimensional Shape Estimation Using Genetic Programming

Combining Focus Measures for Three Dimensional Shape Estimation Using Genetic Programming

Muhammad Tariq Mahmood, Tae-Sun Choi
Copyright: © 2012 |Pages: 20
ISBN13: 9781613503263|ISBN10: 1613503261|EISBN13: 9781613503270
DOI: 10.4018/978-1-61350-326-3.ch011
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MLA

Mahmood, Muhammad Tariq, and Tae-Sun Choi. "Combining Focus Measures for Three Dimensional Shape Estimation Using Genetic Programming." Depth Map and 3D Imaging Applications: Algorithms and Technologies, edited by Aamir Saeed Malik, et al., IGI Global, 2012, pp. 209-228. https://doi.org/10.4018/978-1-61350-326-3.ch011

APA

Mahmood, M. T. & Choi, T. (2012). Combining Focus Measures for Three Dimensional Shape Estimation Using Genetic Programming. In A. Malik, T. Choi, & H. Nisar (Eds.), Depth Map and 3D Imaging Applications: Algorithms and Technologies (pp. 209-228). IGI Global. https://doi.org/10.4018/978-1-61350-326-3.ch011

Chicago

Mahmood, Muhammad Tariq, and Tae-Sun Choi. "Combining Focus Measures for Three Dimensional Shape Estimation Using Genetic Programming." In Depth Map and 3D Imaging Applications: Algorithms and Technologies, edited by Aamir Saeed Malik, Tae Sun Choi, and Humaira Nisar, 209-228. Hershey, PA: IGI Global, 2012. https://doi.org/10.4018/978-1-61350-326-3.ch011

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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.

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