Claxton, Christopher David, 1980- (2007) Colour depth-from-defocus incorporating experimental point spread function measurements. PhD thesis, University of Warwick.

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Abstract

Depth-From-Defocus (DFD) is a monocular computer vision technique for creating depth maps from two images taken on the same optical axis with different intrinsic camera parameters. A pre-processing stage for optimally converting colour images to monochrome using a linear combination of the colour planes has been shown to improve the accuracy of the depth map. It was found that the first component formed using Principal Component Analysis (PCA) and a technique to maximise the signal-to-noise ratio (SNR) performed better than using an equal weighting of the colour planes with an additive noise model. When the noise is non-isotropic the Mean Square Error (MSE) of the depth map by maximising the SNR was improved by 7.8 times compared to an equal weighting and 1.9 compared to PCA. The fractal dimension (FD) of a monochrome image gives a measure of its roughness and an algorithm was devised to maximise its FD through colour mixing. The formulation using a fractional Brownian motion (mm) model reduced the SNR and thus produced depth maps that were less accurate than using PCA or an equal weighting. An active DFD algorithm to reduce the image overlap problem has been developed, called Localisation through Colour Mixing (LCM), that uses a projected colour pattern. Simulation results showed that LCM produces a MSE 9.4 times lower than equal weighting and 2.2 times lower than PCA. The Point Spread Function (PSF) of a camera system models how a point source of light is imaged. For depth maps to be accurately created using DFD a high-precision PSF must be known. Improvements to a sub-sampled, knife-edge based technique are presented that account for non-uniform illumination of the light box and this reduced the MSE by 25%. The Generalised Gaussian is presented as a model of the PSF and shown to be up to 16 times better than the conventional models of the Gaussian and pillbox.

Item Type:	Thesis or Dissertation (PhD)
Subjects:	T Technology > TA Engineering (General). Civil engineering (General)
Library of Congress Subject Headings (LCSH):	Computer vision
Date:	January 2007
Institution:	University of Warwick
Theses Department:	School of Engineering
Thesis Type:	PhD
Publication Status:	Unpublished
Supervisor(s)/Advisor:	Staunton, Richard C., 1952-
Sponsors:	University of Warwick
Extent:	xxi, 265 leaves
Language:	eng
URI:	http://wrap.warwick.ac.uk/id/eprint/4461

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