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Depth of General Scenes from Defocused Images Using Multilayer Feedforward Networks

  • Veysel Aslantas
  • Mehmet Tunckanat
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3949)

Abstract

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

Keywords

Spread Parameter Convolution Operation Focus Image Sensor Plane Optical Transfer Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Veysel Aslantas
    • 1
  • Mehmet Tunckanat
    • 1
  1. 1.Faculty of Engineering, Department of Computer Eng.Erciyes UniversityMelikgazi, KayseriTurkey

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