IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Depth from Defocus vs. Stereo: How Different Really Are They?
International Journal of Computer Vision - Special issue on computer vision research at the Technion
Depth Measurement by the Multi-Focus Camera
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Microscopic vision system with all-in-focus and depth images
Machine Vision and Applications
A Geometric Approach to Shape from Defocus
IEEE Transactions on Pattern Analysis and Machine Intelligence
Removing image artifacts due to dirty camera lenses and thin occluders
ACM SIGGRAPH Asia 2009 papers
Evolving measurement regions for depth from defocus
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part II
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A fundamental problem in depth from defocus is the measurement of relative defocus between images. We propose a class of broadband operators that, when used together, provide invariance to scene texture and produce accurate and dense depth maps. Since the operators are broadband, a small number of them are sufficient for depth estimation of scenes with complex textural properties. Experiments are conducted on both synthetic and real scenes to evaluate the performance of the proposed operators. The depth detection gain error is less than 1%, irrespective of texture frequency. Depth accuracy is found to be 0.5/spl sim/1.2% of the distance of the object from the imaging optics.