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

作者:

Highlights:

摘要

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.

论文关键词:3D shape,Focus measure,PCA,Shape from focus,Kernel regression

论文评审过程:Received 20 July 2008, Revised 1 October 2009, Accepted 7 October 2009, Available online 13 October 2009.

论文官网地址:https://doi.org/10.1016/j.imavis.2009.10.005