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1 May 2009 Shape from focus using principal component analysis in discrete wavelet transform
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We introduce a new approach for 3-D shape recovery based on discrete wavelet transform (DWT) and principal component analysis (PCA). A small 3-D neighborhood is considered to incorporate the effect of pixels from previous as well as next frames. The intensity values of the pixels in the neighborhood are then arranged into a vector. DWT is applied on each vector to decompose it into approximation and wavelet coefficients. PCA is then applied on modified energies of wavelet components. The first feature in the eigenspace, as it contains maximum variation, is employed to compute the depth. The performance of the proposed approach is tested and is compared with existing methods by using synthetic and real image sequences. The evaluation is gauged on the basis of unimodality and monotonicity of the focus curve. Resolution, accuracy, root mean square error (RMSE), and correlation metrics have been applied to evaluate the performance. Experimental results and comparative analysis demonstrate the effectiveness of the proposed method.
©(2009) Society of Photo-Optical Instrumentation Engineers (SPIE)
Muhammad Tariq Mahmood, Seong-O Shim, and Tae-Sun Choi "Shape from focus using principal component analysis in discrete wavelet transform," Optical Engineering 48(5), 057203 (1 May 2009).
Published: 1 May 2009


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