Improving point matching on multimodal images using distance and orientation automatic filtering
Résumé
Speed Up Robust Features SURF is one of the most popular and efficient methods used for image registration task. In order to achieve a correct registration, a good matching of feature point is required. However in the case of multimodal images, the high and non-linear intensity changes between different modalities led to many outliers (mismatching of detected points) and consequently a fail in the registration. Therefore, in this paper we introduce an efficient method devoted to the detection and removal of such outlier. It's based on an automatic filtering of outliers on both distance and orientation between features points. We tested our proposed method on a set of real multimodal images (4 modalities covering UV, IR Visible and fluorescence images) and compared it to classical SURF as well as SURF followed by RANSAC filtering. The results show that our method outperforms the others regarding all assessment criteria.
Mots clés
Feature extraction
Robustness
Image registration
Electronic mail
Measurement
Histograms
Detectors
image filtering
image matching
multimodal images
automatic orientation filtering
speed-up robust features
SURF
feature point matching
high-nonlinear intensity
detected point mismatching
automatic outlier filtering
UV images
IR visible images
fluorescence images
automatic distance filtering
Feature matching
Outlier detection
cultural heritage