3D shape recovery from image focus using kernel regression in eigenspace

Authors:
Muhammad Tariq Mahmood;Tae-Sun Choi
Affiliations:
Signal and Image Processing Lab, School of Information and Mechatronics, Gwangju Institute of Science and Technology, 261 Cheomdan Gwagiro, Buk-Gu, Gwangju 500-712, South Korea;Signal and Image Processing Lab, School of Information and Mechatronics, Gwangju Institute of Science and Technology, 261 Cheomdan Gwagiro, Buk-Gu, Gwangju 500-712, South Korea
Venue:
Image and Vision Computing
Year:
2010

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Abstract

Shape from focus (SFF) is one of the optical passive methods for three dimensional (3D) shape recovery of an object from its two dimensional (2D) images. The focus measure plays important role in SFF algorithms. Mostly, conventional focus measures are based on gradient, so their performance is restricted under noisy conditions. Moreover, SFF methods also suffer from loss of focus information due to discreteness. This paper introduces a new SFF method based on principal component analysis (PCA) and kernel regression. The focus values are computed through PCA by considering a sequence of small 3D neighborhood for each object point. We apply unsupervised regression through Nadaraya and Watson Estimate (NWE) on depth values to get a refined 3D shape of the object. It reduces the effect of noise within a small surface area as well as approximates the accurate 3D shape by exploiting the depth dependencies in the neighborhood. Performance of the proposed scheme is investigated in the presence of different types of noises and textured areas. Experimental results demonstrate effectiveness of the proposed approach.