A New Sense for Depth of Field
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Investigation of Methods for Determining Depth from Focus
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer modeling and simulation techniques for computer vision problems
Computer modeling and simulation techniques for computer vision problems
Robot Vision
A new wavelet-based measure of image focus
Pattern Recognition Letters
On defocus, diffusion and depth estimation
Pattern Recognition Letters
Image and Vision Computing
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
Generalized Laplacian as Focus Measure
ICCS '08 Proceedings of the 8th international conference on Computational Science, Part I
3D Shape from Focus and Depth Map Computation Using Steerable Filters
ICIAR '09 Proceedings of the 6th International Conference on Image Analysis and Recognition
Discontinuity-Adaptive Shape from Focus Using a Non-convex Prior
Proceedings of the 31st DAGM Symposium on Pattern 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
Accurate 3D shape estimation based on combinatorial optimization
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Shape from focus using kernel regression
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
DAGM'06 Proceedings of the 28th conference on Pattern Recognition
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
Reliability measure for shape-from-focus
Image and Vision Computing
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A new shape-from-focus method is described which is based on a new concept named Focused Image Surface (FIS). FIS of an object is defined as the surface formed by the set of points at which the object points are focused by a camera lens. According to paraxial-geometric optics, there is a one-to-one correspondence between the shape of an object and the shape of its FIS. Therefore, the problem of shape recovery can be posed as the problem of determining the shape of the FIS. From the shape of FIS the shape of the object is easily obtained. In this paper the shape of the FIS is determined by searching for a shape which maximizes a focus measure. In contrast to previous literature where the focus measure is computed over the planar image detector of the camera, here the focus measure is computed over the FIS. This results in more accurate shape recovery than the traditional methods. Also, using FIS, a more accurate focused image can be reconstructed from a sequence of images than is possible with traditional methods. The new method has been implemented on an actual camera system, and the results of shape recovery and focused image reconstruction are presented.