A New Sense for Depth of Field
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition Letters
Accurate Recovery of Three-Dimensional Shape from Image Focus
IEEE Transactions on Pattern Analysis and Machine Intelligence
Depth from defocus: a spatial domain approach
International Journal of Computer Vision
International Journal of Computer Vision
Rational Filters for Passive Depth from Defocus
International Journal of Computer Vision
Robot Vision
Three dimensional machine vision using image defocus
Three dimensional machine vision using image defocus
Shape from focus using multilayer feedforward neural networks
IEEE Transactions on Image Processing
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One of the important tasks in computer vision is the computation of object depth from acquired images. This paper explains the use of neural networks to calculate the depth of general objects using only two images, one of them being a focused image and the other one a blurred image. Having computed the power spectra of each image, they are divided to obtain a result which is independent from the image content. The result is then used for training Multi-Layer Perceptron (MLP) neural network (NN) trained by the backpropagation algorithm to determine the distance of the object from the camera lens. Experimental results are presented to validate the proposed approach