DCT and PCA Based Method for Shape from Focus

Authors:
M. Tariq Mahmood;Wook-Jin Choi;Tae-Sun Choi
Affiliations:
School of Information and Mechatronics, Gwangju Institute of Science and Technology, Oryong-Dong, Buk-Gu, South Korea 500-712;School of Information and Mechatronics, Gwangju Institute of Science and Technology, Oryong-Dong, Buk-Gu, South Korea 500-712;School of Information and Mechatronics, Gwangju Institute of Science and Technology, Oryong-Dong, Buk-Gu, South Korea 500-712
Venue:
ICCSA '08 Proceedings of the international conference on Computational Science and Its Applications, Part II
Year:
2008

Citing 10
Cited 1

Accurate Recovery of Three-Dimensional Shape from Image Focus

IEEE Transactions on Pattern Analysis and Machine Intelligence
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 Bayes-spectral-entropy-based measure of camera focus using a discrete cosine transform

Pattern Recognition Letters
Consideration of illumination effects and optimization of window size for accurate calculation of depth map for 3D shape recovery

Pattern Recognition
Discrete Cosine Transfom

IEEE Transactions on Computers
Application of Three Dimensional Shape from Image Focus in LCD/TFT Displays Manufacturing

IEEE Transactions on Consumer Electronics
Application of Passive Techniques for Three Dimensional Cameras

IEEE Transactions on Consumer Electronics
Shape from focus using multilayer feedforward neural networks

IEEE Transactions on Image Processing
A heuristic approach for finding best focused shape

IEEE Transactions on Circuits and Systems for Video Technology

Intelligent reversible watermarking and authentication: Hiding depth map information for 3D cameras

Information Sciences: an International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

Discrete Cosine Transform (DCT) and Principal Component Analysis (PCA) are widely used in computer vision applications. In this paper, we introduce a new SFF method based on DCT and 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. PCA is applied on the AC part of the DCT of each vector in the sequence to transform data into eigenspace. Considering the first feature, as it contains maximum variation, and discarding all others, is employed to compute the depth. Though DCT and PCA are both computationally expensive transformations, the reduction in data elements and algorithm iterations have made the new approach efficient. Experimental results are presented to demonstrate the effectiveness of new method by using three different image sequences.