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Proceedings Paper

Wavelet and PCA-based approach for 3D shape recovery from image focus
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Paper Abstract

This paper introduces a new approach for 3D shape recovery based on Discrete Wavelet Transform (DWT) and Principal Component Analysis (PCA). Contrary to computing focus quality locally by summing all values in a 2D or 3D window obtained after applying a focus measure, a vector consisting of seven neighboring pixels is populated for each pixel in the image volume. Each vector in the sequence is decomposed by using DWT and then PCA is applied on the energies of detailed coefficients to transform the data into eigenspace. The first feature, as it contains maximum variation, is employed to compute the depth. Though DWT and PCA are both computationally expensive transformations, the reduced data elements and algorithm iterations have made the proposed method efficient. The new approach was experimented and its performance was compared with other methods by using synthetic and real image sequences. The evaluation is gauged on the basis of unimodality, monotonicity and resolution of the focus curve. Two other global statistical metrics Root Mean Square Error (RMSE) and correlation have also been applied for synthetic image sequence. Experimental results demonstrate the effectiveness and the robustness of the new method.

Paper Details

Date Published: 15 September 2008
PDF: 9 pages
Proc. SPIE 7073, Applications of Digital Image Processing XXXI, 70731S (15 September 2008); doi: 10.1117/12.798214
Show Author Affiliations
Muhammad Tariq Mahmood, Gwangju Institute of Science and Technology (Korea, Republic of)
Seongo Shim, Gwangju Institute of Science and Technology (Korea, Republic of)
Tae-Sun Choi, Gwangju Institute of Science and Technology (Korea, Republic of)


Published in SPIE Proceedings Vol. 7073:
Applications of Digital Image Processing XXXI
Andrew G. Tescher, Editor(s)

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