The Color Image team designs automatic analysis schemes for images that represent the color information in observed scenes. Its researchers work on
- Extracting local or global features from CFA or raw images acquired by color cameras equipped with one sensor,
- Selecting color spaces that are adapted to the segmentation of full color images that have been provided by three-sensor cameras,
- Recognizing objects under uncontrolled acquisition conditions by the analysis of multi-spectral images in the visible spectrum.
color vision system which detects aspect flaws occurring on the color surfaces of drinking glasses decorated thanks to an industrial silk-screen process
embedded vision system which measures the cross section of catenary contact wire
color vision system that inspects the frozen meat.
We also develop automatic diagnostic of turbo-alternator by pattern recognition with L2EP Institute.
Texture features from multispectral images acquired under uncontrolled conditions. Application to automatic identification of weeds in field crops
Edge detection on Bayer CFA image. 21/12/2017