Authors:
Desire Sidibe
;
Philippe Montesinos
and
Stefan Janaqi
Affiliation:
LGI2P/EMA - Ales School of Mines, France
Keyword(s):
Relaxation, Image matching, Point matching, Scale invariant features.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computer Vision, Visualization and Computer Graphics
;
Data Manipulation
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Matching Correspondence and Flow
;
Methodologies and Methods
;
Motion, Tracking and Stereo Vision
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Soft Computing
Abstract:
This paper tackles the difficult, but fundamental, problem of image matching under projective transformation. Recently, several algorithms capable of handling large changes of viewpoint as well as large scale changes have been proposed. They are based on the comparison of local, invariants descriptors which are robust to these transformations. However, since no image descriptor is robust enough to avoid mismatches, an additional step of outliers rejection is often needed. The accuracy of which strongly depends on the number of mismatches. In this paper, we show that the matching process can be made robust to ensure a very few number of mismatches based on a relaxation labeling technique. The main contribution of this work is in providing an efficient and fast implementation of a relaxation method which can deal with large sets of features. Futhermore, we show how the contextual information can be obtained and used in this robust and fast algorithm. Experiments with real data and comp
arison with other matching methods, clearly show the improvements in the matching results.
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