A novel iterative shape from focus algorithm based on combinatorial optimization

Authors:
Seong-O Shim;Tae-Sun Choi
Affiliations:
Gwangju Institute of Science and Technology, 261 Cheomdan Gwagiro, Buk-Gu, Gwangju 500-712, Republic of Korea;Gwangju Institute of Science and Technology, 261 Cheomdan Gwagiro, Buk-Gu, Gwangju 500-712, Republic of Korea
Venue:
Pattern Recognition
Year:
2010

Citing 13
Cited 1

A New Sense for Depth of Field

IEEE Transactions on Pattern Analysis and Machine Intelligence
Accurate Recovery of Three-Dimensional Shape from Image Focus

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Depth from defocus: a spatial domain approach

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Selecting the Optimal Focus Measure for Autofocusing and Depth-From-Focus

IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape from Focus

IEEE Transactions on Pattern Analysis and Machine Intelligence
A survey of very large-scale neighborhood search techniques

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Accommodation in computer vision

Accommodation in computer vision
A Geometric Approach to Shape from Defocus

IEEE Transactions on Pattern Analysis and Machine Intelligence
Consideration of illumination effects and optimization of window size for accurate calculation of depth map for 3D shape recovery

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Shape from Defocus via Diffusion

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A novel algorithm for estimation of depth map using image focus for 3D shape recovery in the presence of noise

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Shape from focus using multilayer feedforward neural networks

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A heuristic approach for finding best focused shape

IEEE Transactions on Circuits and Systems for Video Technology

Analysis of focus measure operators for shape-from-focus

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Abstract

Shape from focus (SFF) is a technique to estimate the depth and 3D shape of an object from a sequence of images obtained at different focus settings. In this paper, the SFF is presented as a combinatorial optimization problem. The proposed algorithm tries to find the combination of pixel frames which produces maximum focus measure computed over pixels lying on those frames. To reduce the high computational complexity, a local search method is proposed. After the estimate of the initial depth map solution of an object, the neighborhood is defined, and an intermediate image volume is generated from the neighborhood. The updated depth map solution is found from the intermediate image volume. This update process of the depth map solution continues until the amount of improvement is negligible. The results of the proposed SFF algorithm have shown significant improvements in both the accuracy of the depth map estimation and the computational complexity, with respect to the existing SFF methods.