A New Sense for Depth of Field
IEEE Transactions on Pattern Analysis and Machine Intelligence
Active, optical range imaging sensors
Machine Vision and Applications
Single Lens Stereo with a Plenoptic Camera
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Active vision for reliable ranging: cooperating focus, stereo, and vergence
International Journal of Computer Vision
Range estimation by optical differentiation
Range estimation by optical differentiation
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Separation of Transparent Layers using Focus
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
Constrained Monocular Obstacle Perception with Just One Frame
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part I
Wavelet transform based gaussian point spread function estimation
ISVC'05 Proceedings of the First international conference on Advances in Visual Computing
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Depth from Focus (DFF) and Depth from Defocus (DFD) methods are shown to be realizations of the geometric triangulation principle. Fundamentally, the depth sensitivities of DFF and DFD are not different than those of stereo (or motion) based systems having the same physical dimensions. Contrary to common belief, DFD does not inherently avoid the matching (correspondence) problem. Basically, DFD and DFF do not avoid the occlusion problem any more than triangulation techniques, but they are more stable in the presence of such disruptions. The fundamental advantage of DFF and DFD methods is the two-dimensionality of the aperture, allowing more robust estimation. These results elucidate the limitations of methods based on depth of field and provide a foundation for fair performance comparison between DFF/DFD and shape from stereo (or motion) algorithms.