A New Sense for Depth of Field
IEEE Transactions on Pattern Analysis and Machine Intelligence
Accurate Recovery of Three-Dimensional Shape from Image Focus
IEEE Transactions on Pattern Analysis and Machine Intelligence
Selecting the Optimal Focus Measure for Autofocusing and Depth-From-Focus
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robot Vision
Size Fair and Homologous Tree Crossovers for Tree Genetic Programming
Genetic Programming and Evolvable Machines
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Bayes-spectral-entropy-based measure of camera focus using a discrete cosine transform
Pattern Recognition Letters
Combination of support vector machines using genetic programming
International Journal of Hybrid Intelligent Systems
Combination and optimization of classifiers in gender classification using genetic programming
International Journal of Knowledge-based and Intelligent Engineering Systems
Measure of image sharpness using eigenvalues
Information Sciences: an International Journal
Computer vision methods for optical microscopes
Image and Vision Computing
Routine high-return human-competitive automated problem-solving by means of genetic programming
Information Sciences: an International Journal
Dynamic population variation in genetic programming
Information Sciences: an International Journal
Comparison of polymers: a new application of shape from focus
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
IEEE Transactions on Image Processing
Ensemble strategies with adaptive evolutionary programming
Information Sciences: an International Journal
A relevance feedback method based on genetic programming for classification of remote sensing images
Information Sciences: an International Journal
Application of Three Dimensional Shape from Image Focus in LCD/TFT Displays Manufacturing
IEEE Transactions on Consumer Electronics
Application of Passive Techniques for Three Dimensional Cameras
IEEE Transactions on Consumer Electronics
Shape from focus using multilayer feedforward neural networks
IEEE Transactions on Image Processing
Universal Impulse Noise Filter Based on Genetic Programming
IEEE Transactions on Image Processing
A heuristic approach for finding best focused shape
IEEE Transactions on Circuits and Systems for Video Technology
Multi-stage genetic programming: A new strategy to nonlinear system modeling
Information Sciences: an International Journal
Evolving estimators of the pointwise Hölder exponent with Genetic Programming
Information Sciences: an International Journal
Information Sciences: an International Journal
Intelligent reversible watermarking and authentication: Hiding depth map information for 3D cameras
Information Sciences: an International Journal
Genetic programming based blind image deconvolution for surveillancesystems
Engineering Applications of Artificial Intelligence
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Three-dimensional (3D) shape reconstruction is a fundamental problem in machine vision applications. Shape From Focus (SFF) is one of the passive optical methods for 3D shape recovery that uses degree of focus as a cue to estimate 3D shape. In this approach, usually a single focus measure operator is applied to measure the focus quality of each pixel in the image sequence. However, the applicability of a single focus measure is limited to estimate accurately the depth map for diverse type of real objects. To address this problem, we develop Optimal Composite Depth (OCD) function through genetic programming (GP) for accurate depth estimation. The OCD function is constructed by optimally combining the primary information extracted using one/or more focus measures. The genetically developed composite function is then used to compute the optimal depth map of objects. The performance of the developed nonlinear function is investigated using both the synthetic and the real world image sequences. Experimental results demonstrate that the proposed estimator is more useful in computing accurate depth maps as compared to the existing SFF methods. Moreover, it is found that the heterogeneous function is more effective than homogeneous function.