Scaling images and image features via the renormalization group

Geiger, Davi; Kögler, João Eduardo

doi:10.1109/cvpr.1993.341002

Cited by 11 publications

(2 citation statements)

References 10 publications

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“…Gidas (1990) proposes a method which uses the renormalization group theory, MRFs and Metropolis algorithm for global optimization. How to preserve the inter-relationships between objects in the scene using the renormalization group transformation are studied in (Geiger and Kogler Jr. 1993;Hurn and Gennison 1993;Petrou 1993). …”

Section: Multi-resolution Methodsmentioning

confidence: 99%

Markov Random Field Modeling in Computer Vision

1995

Computer Science Workbench

979

779

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Section: Multi-resolution Methodsmentioning

confidence: 99%

Markov Random Field Modeling in Computer Vision

1995

Computer Science Workbench

979

779

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“…This limits the usefulness of these methods for any real time applications [35]. This category includes methods that are based on image models such as, Markov random fields [45,46], Gibbs random field [47,48,1,4], and others [49].…”

Section: Methodsmentioning

confidence: 99%

Image expansion using segmentation-based method

Agha

Ward²,

Zahir³

1999 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM 1999). Conference Proceedings (Cat.

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In presenting this thesis in partial fulfilment of the requirements for an advanced degree at the University of British Columbia, I agree that the Library shall make it freely available for reference and study. I further agree that permission for extensive copying of this thesis for scholarly purposes may be granted by the head of my department or by his or her representatives. It is understood that copying or publication of this thesis for financial gain shall not be allowed without my written permission.

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Noise Removal and Contour Detection

Charbonnier¹,

Collet²

2013

Inverse Problems in Vision and 3D Tomography

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Scaling images and image features via the renormalization group

Cited by 11 publications

References 10 publications

Markov Random Field Modeling in Computer Vision

Markov Random Field Modeling in Computer Vision

Image expansion using segmentation-based method

Noise Removal and Contour Detection

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