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Proceedings Paper

Depth determination from defocused images using neural networks
Author(s): Koduri K. Sreenivasan; Mandayam D. Srinath
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Paper Abstract

Determination of the depth of objects in a scene is based on interpretation of the visual cues that tell us how near or far away the objects are. Such cues can be binocular or monocular. Most existing algorithms are based on binocular cues and use a pair of stereo images of the scene to compute a depth map from the disparity between corresponding points in the two images, the geometry of the imaging system, and camera parameters. To solve the correspondence problem, certain simplifying assumptions are usually made. Here we propose a method based on the fact that the brain computes the approximate distance of an object from the viewer from the amount of defocus of its image on the retina. Given two images of a scene taken with different focal settings, we model one of the images as the convolution of a blur function with the other image and use the DFT of the two images to obtain an estimate of the blur at each pixel. A multilayer perceptron using backpropagation learning is used to infer the complex relationship between blur and depth, which also involves the imaging system parameters. Blur functions obtained from a set of images with objects at known depths are used to train the neural network. This approach avoids both the correspondence and camera calibration problems.

Paper Details

Date Published: 21 May 1993
PDF: 12 pages
Proc. SPIE 1902, Nonlinear Image Processing IV, (21 May 1993); doi: 10.1117/12.144770
Show Author Affiliations
Koduri K. Sreenivasan, Southern Methodist Univ. (United States)
Mandayam D. Srinath, Southern Methodist Univ. (United States)


Published in SPIE Proceedings Vol. 1902:
Nonlinear Image Processing IV
Edward R. Dougherty; Jaakko T. Astola; Harold G. Longbotham, Editor(s)

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