Digital image processing (2nd ed.)
Digital image processing (2nd ed.)
A New Sense for Depth of Field
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robot Vision
Digital Picture Processing
Digital Image Restoration
Accurate Recovery of Three-Dimensional Shape from Image Focus
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Variational Approach to Recovering Depth From Defocused Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Local Scale Control for Edge Detection and Blur Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
International Journal of Computer Vision
An MRF Model-Based Approach to Simultaneous Recovery of Depth and Restoration from Defocused Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision - Special issue on computer vision research at NEC Research Institute
Depth from Defocus vs. Stereo: How Different Really Are They?
International Journal of Computer Vision - Special issue on computer vision research at the Technion
A Variational Approach to Shape from Defocus
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
Shape and Radiance Estimation from the Information-Divergence of Blurred Images
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part I
A fast focus measure for video display inspection
Machine Vision and Applications
A Geometric Approach to Shape from Defocus
IEEE Transactions on Pattern Analysis and Machine Intelligence
Depth-of-field-based alpha-matte extraction
APGV '05 Proceedings of the 2nd symposium on Applied perception in graphics and visualization
On defocus, diffusion and depth estimation
Pattern Recognition Letters
Focus Area Extraction by Blind Deconvolution for Defining Regions of Interest
IEEE Transactions on Pattern Analysis and Machine Intelligence
Capturing hair assemblies fiber by fiber
ACM SIGGRAPH Asia 2009 papers
Wide-Angle Intraocular Imaging and Localization
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part I
Wide-angle localization of intraocular devices from focus
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Virtual focus and depth estimation from defocused video sequences
IEEE Transactions on Image Processing
Evolving measurement regions for depth from defocus
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part II
Robot positioning using camera-space manipulation with a linear camera model
IEEE Transactions on Robotics
Computer Science - Research and Development
3D shape from anisotropic diffusion
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Rational filter design for depth from defocus
Pattern Recognition
DAGM'06 Proceedings of the 28th conference on Pattern Recognition
Depth recovery from motion and defocus blur
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part II
Towards Unrestrained Depth Inference with Coherent Occlusion Filling
International Journal of Computer Vision
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Calibration of double stripe 3D laser scanner systems using planarity and orthogonality constraints
Digital Signal Processing
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The concept of depth from focus involves calculating distances to points in an observed scene by modeling the effect that the camera's focal parameters have on images acquired with a small depth of field. This technique is passive and requires only a single camera. The most difficult segment of calculating depth from focus is deconvolving the defocus operator from the scene and modeling it. Most current methods for determining the defocus operator employ inverse filtering. The authors reveal some fundamental problems with inverse filtering: inaccuracies in finding the frequency domain representation, windowing effects, and border effects. A general, matrix-based method using regularization is presented, which eliminates these problems. The new method is confirmed experimentally, with the results showing an RMS error of 1.3%.