A fast focus measure for video display inspection
Machine Vision and Applications
DCT and PCA Based Method for Shape from Focus
ICCSA '08 Proceedings of the international conference on Computational Science and Its Applications, Part II
Depth Estimation by Finding Best Focused Points Using Line Fitting
ICISP '08 Proceedings of the 3rd international conference on Image and Signal Processing
3D Shape from Focus and Depth Map Computation Using Steerable Filters
ICIAR '09 Proceedings of the 6th International Conference on Image Analysis and Recognition
3D shape recovery from image focus using kernel regression in eigenspace
Image and Vision Computing
Noise analysis for depth estimation
BVAI'07 Proceedings of the 2nd international conference on Advances in brain, vision and artificial intelligence
Computational filter-aperture approach for single-view multi-focusing
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
A novel iterative shape from focus algorithm based on combinatorial optimization
Pattern Recognition
Shape from focus using fast discrete curvelet transform
Pattern Recognition
A Fuzzy-Neural approach for estimation of depth map using focus
Applied Soft Computing
Optimal depth estimation by combining focus measures using genetic programming
Information Sciences: an International Journal
Rectification of illumination in images used for shape from focus
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part II
Depth of general scenes from defocused images using multilayer feedforward networks
TAINN'05 Proceedings of the 14th Turkish conference on Artificial Intelligence and Neural Networks
Analysis of focus measure operators for shape-from-focus
Pattern Recognition
3D shape from focus using LULU operators and discrete pulse transform in the presence of noise
Journal of Visual Communication and Image Representation
Reliability measure for shape-from-focus
Image and Vision Computing
Hi-index | 0.01 |
The conventional shape-from-focus (SFF) methods have inaccuracies because of piecewise constant approximation of the focused image surface (FIS). We propose a scheme for SFF based on representation of three-dimensional (3-D) FIS in terms of neural network weights. The neural networks are trained to learn the shape of the FIS that maximizes the focus measure