0000000000671095
AUTHOR
Gildardo Lozano Vega
Image-based detection and classification of allergenic pollen
The correct classification of airborne pollen is relevant for medical treatment of allergies, and the regular manual process is costly and time consuming. An automatic processing would increase considerably the potential of pollen counting. Modern computer vision techniques enable the detection of discriminant pollen characteristics. In this thesis, a set of relevant image-based features for the recognition of top allergenic pollen taxa is proposed and analyzed. The foundation of our proposal is the evaluation of groups of features that can properly describe pollen in terms of shape, texture, size and apertures. The features are extracted on typical brightfield microscope images that enable…
Sketch of an automatic image based pollen detection system
The ability of measuring accurately airborne pollen concentration in the environment is an important goal for palynology. It has been unsatisfactory for agile usage to date. Huge volumes of airborne particles prevent palynologists from opportunely processing statistically suitable information. Additionally, measurements from stationary pollen monitors cannot be accurately associated to individuals. In the context of computer vision, this paper presents the outline for the structure of an image based pollen detection system, under the framework of the Personalized Pollen Profiling and Geospatial Mapping project based on individual information of allergic patient profile measured at multiple …