Faugeras and Berthod Gradient Optimization Methods

Chapter Contents (Back)
Constraint Satisfaction. Matching, Relaxation. Relaxation, Continuous.

Faugeras, O.D., and Berthod, M.,
Improving Consistency and Reducing Ambiguity in Stochastic Labeling: An Optimization Approach,
PAMI(3), No. 4, July 1981, pp. 412-424. This paper describes the essence of the gradient optimization approach to relaxation labeling. A global criterion is defined that combines the concepts of ambiguity and consistency. The relaxation procedure is used to minimize the criterion, by moving in the direction of the strongest gradient. This basic technique was used by other researchers in matching problems as in Bhanu (
See also Shape Matching of Two-Dimensional Objects. ) and Faugeras-Price (
See also Semantic Description of Aerial Images Using Stochastic Labeling. ). BibRef 8107

Faugeras, O.D., and Berthod, M.,
Scene Labeling: An Optimization Approach,
PR(12), No. 5, 1980, pp. 339-347.
Elsevier DOI BibRef 8000
Earlier: PRIP79(318-326). Relaxation, Theory. BibRef

Faugeras, O.D.[Olivier D.],
Decomposition and Decentralization Techniques in Relaxation Labeling,
CGIP(16), No. 4, August 1981, pp. 341-355.
Elsevier DOI BibRef 8108

Faugeras, O.D.[Olivier D.],
An Optimization Approach for Using Contextual Information in Computer Vision,
AAAI-80(56-60). BibRef 8000

Faugeras, O.D., and Berthod, M.,
Using Context in the Global Recognition of a Set of Objects: An Optimization Approach,
presented at the 8th World Computer Congress, IFIP1980. More of the basic optimization approach. BibRef 8000

Yu, S., Berthod, M.,
A Game Strategy Approach for Image Labeling,
CVIU(61), No. 1, January 1995, pp. 32-37.
DOI Link BibRef 9501

Berthod, M.,
Semi-Consistency: A Solution to the No-Label Problem,
CVPR83(555-556).. Similar: BibRef 8300
Global Optimization of a Consistent Labeling,
IJCAI83(1065-1067). BibRef

Faugeras, O.D.,
Relaxation Labeling and Evidence Gathering,
PRIP82(672-677). BibRef 8200
And: ICPR82(405-412). The problem is how to represent non-support (negative support) and ignorance. Normalization of the Q vectors is not needed. Interesting-new numbers and computation-try it out to see what it really means! The second version says about the same as the first, but there is more of it. BibRef

Berthod, M., Long, P.,
Graph Matching by Parallel Optimization Methods: An Application to Stereo Vision,
ICPR84(841-843). BibRef 8400

Chapter on Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants continues in
Discrete Relaxation Methods .

Last update:Apr 10, 2024 at 09:54:40