Improving point matching on multimodal images using distance and orientation automatic filtering

6533b7d2fe1ef96bd125e1cd

RESEARCH PRODUCT

Improving point matching on multimodal images using distance and orientation automatic filtering

Driss Mammass Alamin Mansouri Aissam Bekkarri Gaëtan Le Goïc Hanan Anzid

subject

Histograms Computer science Feature extraction ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Image registration 02 engineering and technology image matching feature point matching RANSAC Electronic mail automatic outlier filtering Histogram automatic orientation filtering high-nonlinear intensity [ INFO.INFO-TI ] Computer Science [cs]/Image Processing 0202 electrical engineering electronic engineering information engineering automatic distance filtering Outlier detection Computer vision IR visible images Robustness multimodal images UV images Image registration image filtering Measurement business.industry Feature matching SURF 020206 networking & telecommunications Point set registration Pattern recognition Detectors detected point mismatching cultural heritage fluorescence images Electronic mail [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV]Outlier speed-up robust features Feature extraction 020201 artificial intelligence & image processing Anomaly detection Artificial intelligence business

description

International audience; 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.

year	journal	country	edition	language
2016-11-29

https://hal-univ-bourgogne.archives-ouvertes.fr/hal-01563952