Li, Ang (2014) 3D computer vision : passive depth from defocus and manifold learning-based human activity recognition. PhD thesis, University of Warwick.

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Abstract

The rational operator-based approach to depth from defocus (DfD) using pill-box point spread function (PSF) enables texture-invariant 3-dimensional (3D) surface reconstructions. However, pill-box PSF produces errors when the amount of lens diffraction and aberrations varies. This thesis proposes two DfD methods, one using the Gaussian PSF that addresses the situation when diffraction and aberrations are dominant, and the second based on the generalised Gaussian PSF that deals with any levels of the problem. The accuracy of DfD can be severely reduced by elliptical lens distortion. This thesis also presents two correction methods, correction by distortion cancellation and correction by least squares fit. Each method is followed by a smoothing algorithm to address the low-texture problem of DfD.

Most existing human activity recognition systems pay little attention to an effective way to obtain training silhouettes. This thesis presents an algorithm to obtain silhouettes from any view using 3D data produced by Vicon Nexus. Existing background subtraction algorithms produce moving shadow that has a significant impact on silhouette-based recognition system. Shadow removal methods based on colour and texture fail when the surrounding background has similar colour or texture. This thesis proposes an algorithm based on known position of the sun to remove shadow in outdoor environment, which is able to remove essential part of the shadow to suffice recognition purpose. Unlike most recognition systems that are either speed-variant, temporal-order-variant, inefficient or computational expensive, this thesis presents a near real-time system based on embedded silhouettes. Silhouettes are first embedded with isometric feature mapping, and the transformation is learned by radial basis function. Complex human activities are then learnt with spatial objects created from the patterns of embedded silhouettes.

Item Type:	Thesis (PhD)
Subjects:	T Technology > TA Engineering (General). Civil engineering (General)
Library of Congress Subject Headings (LCSH):	Computer vision
Official Date:	August 2014
Dates:	Date Event August 2014 Submitted
Institution:	University of Warwick
Theses Department:	School of Engineering
Thesis Type:	PhD
Publication Status:	Unpublished
Supervisor(s)/Advisor:	Staunton, Richard ; Tjahjadi, Tardi
Sponsors:	University of Warwick. School of Engineering
Extent:	viii, 166 leaves : illustrations
Language:	eng

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