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Invariancy Methods for Points, Curves and Surfaces in Computational Vision

Åström, Karl LU (1996) In University of Lund, Institute of Technology, Department of Mathematics 1996:2.
Abstract
Many issues in computational vision can be understood from the interplay between camera geometry and the structure of images and objects. Typically, the image structure is available and the goal is to reconstruct object structure and camera geometry. This is often difficult due to the complex interdependence between these three entities. The theme of this thesis is to use invariants to solve these and other problems of computational vision. Two types of invariancies are discussed; view-point invariance and object invariance.



A view-point invariant does not depend on the camera geometry. The classical cross ratio of four collinear points is a typical example. A number of invariants for planar curves are developed and... (More)
Many issues in computational vision can be understood from the interplay between camera geometry and the structure of images and objects. Typically, the image structure is available and the goal is to reconstruct object structure and camera geometry. This is often difficult due to the complex interdependence between these three entities. The theme of this thesis is to use invariants to solve these and other problems of computational vision. Two types of invariancies are discussed; view-point invariance and object invariance.



A view-point invariant does not depend on the camera geometry. The classical cross ratio of four collinear points is a typical example. A number of invariants for planar curves are developed and discussed. View-point invariants are useful for many purposes, for example to solve recognition problems. This idea is applied to navigation of laser guided vehicles and to the recognition of planar curves.



An object invariant does not depend on the object structure. The epipolar constraint is a typical example. The epipolar constraint is generalised in several directions. Multilinear constraints are derived for both continuous and discrete time motion. Similar constraints are used to solve navigation problems. Generalised epipolar constraints are derived for curves and surfaces.



The invariants are based on pure geometrical properties. To apply these ideas to real images it is necessary to consider practical issues such as noise. Stochastic properties of low-level vision are investigated to give guidelines for design of practical algorithms. A theory for interpolation and scale-space smoothing is developed. The resulting low-level algorithms, for example edge-detection and correlation, are invariant with respect to the position of the discretisation grid. The ideas are useful in order to understand existing algorithms and to design new ones. (Less)
Please use this url to cite or link to this publication:
author
opponent
  • Prof. Yuille, Alan, Harvard Robotics Laboratory, Harvard University
organization
publishing date
type
Thesis
publication status
published
subject
keywords
curved surface, space curve, planar curve, laser guided vehicles, AGV, autonomous guided vehicles, recognition, reconstruction, image sequence, multiple view geometry, affine, invariant, projective, Mathematics, Matematik
in
University of Lund, Institute of Technology, Department of Mathematics
volume
1996:2
pages
212 pages
publisher
Department of Mathematics, Lund University
defense location
MH-building, MH:C, Lund
defense date
1996-05-30 10:15
external identifiers
  • other:ISRN: LUTTFD2/TFMA--96/1006-SE
ISSN
0347-8475
ISBN
91-628-2022-2
language
English
LU publication?
yes
id
961ab7e6-9d47-4e1a-b994-b104c80095c5 (old id 17808)
date added to LUP
2007-05-24 10:33:48
date last changed
2016-09-19 08:44:52
@phdthesis{961ab7e6-9d47-4e1a-b994-b104c80095c5,
  abstract     = {Many issues in computational vision can be understood from the interplay between camera geometry and the structure of images and objects. Typically, the image structure is available and the goal is to reconstruct object structure and camera geometry. This is often difficult due to the complex interdependence between these three entities. The theme of this thesis is to use invariants to solve these and other problems of computational vision. Two types of invariancies are discussed; view-point invariance and object invariance.<br/><br>
<br/><br>
A view-point invariant does not depend on the camera geometry. The classical cross ratio of four collinear points is a typical example. A number of invariants for planar curves are developed and discussed. View-point invariants are useful for many purposes, for example to solve recognition problems. This idea is applied to navigation of laser guided vehicles and to the recognition of planar curves.<br/><br>
<br/><br>
An object invariant does not depend on the object structure. The epipolar constraint is a typical example. The epipolar constraint is generalised in several directions. Multilinear constraints are derived for both continuous and discrete time motion. Similar constraints are used to solve navigation problems. Generalised epipolar constraints are derived for curves and surfaces.<br/><br>
<br/><br>
The invariants are based on pure geometrical properties. To apply these ideas to real images it is necessary to consider practical issues such as noise. Stochastic properties of low-level vision are investigated to give guidelines for design of practical algorithms. A theory for interpolation and scale-space smoothing is developed. The resulting low-level algorithms, for example edge-detection and correlation, are invariant with respect to the position of the discretisation grid. The ideas are useful in order to understand existing algorithms and to design new ones.},
  author       = {Åström, Karl},
  isbn         = {91-628-2022-2},
  issn         = {0347-8475},
  keyword      = {curved surface,space curve,planar curve,laser guided vehicles,AGV,autonomous guided vehicles,recognition,reconstruction,image sequence,multiple view geometry,affine,invariant,projective,Mathematics,Matematik},
  language     = {eng},
  pages        = {212},
  publisher    = {Department of Mathematics, Lund University},
  school       = {Lund University},
  series       = {University of Lund, Institute of Technology, Department of Mathematics},
  title        = {Invariancy Methods for Points, Curves and Surfaces in Computational Vision},
  volume       = {1996:2},
  year         = {1996},
}