Research Group Image Analysis

Group leader: [link]Dr. Evgeny Gladilin

 

Rapid advances in automated plant imaging enable researchers to collect a large amount of multi-modal image data, including visible light, fluorescence, near-infrared and 3D images. To process large multimodal image data and to derive useful information about structural, developmental and functional plant properties, efficient computational solutions for quantitative image analysis are required.

 

The research group Image Analysis focuses on development of advanced algorithms and an integrative software environment for quantitative characterization (phenotyping) of morphological, developmental and physiological plant traits from multimodal and multidimensional image data such as visible light (VIS), fluorescence (FLU), near-infrared (NIR), 3D macroscopic and microscopic images.

 

The tasks of image analysis include image enhancement, supervised and unsupervised image segmentation, registration, pattern recognition and classification, computational modelling and phenotypic description of relevant plant structures (e.g., shoots, roots, seeds, cells).

 

The group is closely collaborating with plant biologists, bioinformaticians and IT scientists.