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Task3 : Semi-Automatic System for Pollen Recognition

Task3 : Semi-Automatic System for Pollen Recognition. Partners: REA (Barcelona) REA (Cordoba) LASMEA (Clermont-Ferrand) INRIA (Sophia-Antipolis). 1) Pollen recognition (WP 5330) Blur analysis Reticulum analysis Summary of characteristic recognition

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Task3 : Semi-Automatic System for Pollen Recognition

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  1. Task3 : Semi-Automatic System for Pollen Recognition Partners: REA (Barcelona) REA (Cordoba) LASMEA (Clermont-Ferrand) INRIA (Sophia-Antipolis) Task 3: Semi-Automatic System for Pollen Recognition

  2. 1) Pollen recognition (WP 5330) Blur analysis Reticulum analysis Summary of characteristic recognition 2) Recognition system integration (WP5330) 3) System Validation (WP6300) Plan Task 3: Semi-Automatic System for Pollen Recognition

  3. 100 images per pollen grain are digitized 10 images can be sufficient to recognize a pollen grain Problem: The 10 images differs from one grain to another Which images are necessary: Central image of the pollen grain (1) Clear images of the sequences (images of interest) (2-6) For some pollen characteristics, some images are needed to validate (2-5) Lot of images need to be digitized, but the system analyzes and chooses only few of them How many images are needed? Task 3: Semi-Automatic System for Pollen Recognition

  4. Analysis of the whole image sequence Detection of the images of interest Analysis of the images of interest Depends on pollen types Methods of segmentation, thresholding, region analysis, ... Methods to characterize an image sequence: Blur measurement (SML - Sum Modified Laplacian) Colour energy (standard deviation in colour) Clear Image Detection Task 3: Semi-Automatic System for Pollen Recognition

  5. Clear Image Detection (SML): Cupressaceae Task 3: Semi-Automatic System for Pollen Recognition

  6. Clear Image Segmentation: Cupressaceae 41 52 58 Task 3: Semi-Automatic System for Pollen Recognition

  7. Clear Image Detection (SML): Parietaria Task 3: Semi-Automatic System for Pollen Recognition

  8. Clear Image Segmentation: Parietaria 38 43 51 Task 3: Semi-Automatic System for Pollen Recognition

  9. Clear Image Detection (SML): Poaceae Task 3: Semi-Automatic System for Pollen Recognition

  10. Clear Image Segmentation: Poaceae 49 53 65 Task 3: Semi-Automatic System for Pollen Recognition

  11. Clear Image Detection (SML): Olea Task 3: Semi-Automatic System for Pollen Recognition

  12. Clear Image Segmentation: Olea 27 33 44 56 63 Task 3: Semi-Automatic System for Pollen Recognition

  13. The reticulum is located at top (or bottom) surface of the grain Reticulum Analysis • Reticulated pollen types: • Olea • Brassicaceae, Fraxinus, Ligustrum, Phillyrea, Salix Task 3: Semi-Automatic System for Pollen Recognition

  14. Steps to follow: Detection of reticulum (reticulated grain or not?) Characterization of the reticulum (Lumina / Muri) Analysis of Lumina Classification based on the reticulum Reticulum Analysis Task 3: Semi-Automatic System for Pollen Recognition

  15. Reticulum Analysis (Brassicaceae) Case 1: Lumina dark, Muri light Case 2: Lumina light, Muri dark Task 3: Semi-Automatic System for Pollen Recognition

  16. Reticulum Analysis (Brassicaceae) Problem: On some images, the lumina are dark AND light Task 3: Semi-Automatic System for Pollen Recognition

  17. Reticulum Analysis (Ligustrum) Case 1: Lumina dark, Muri light Case 2: Lumina light, Muri dark Task 3: Semi-Automatic System for Pollen Recognition

  18. Reticulum Analysis (Ligustrum) Problem: For Ligustrum, muri can be dark and light (Columelae) The analysis resulted here in light lumina Task 3: Semi-Automatic System for Pollen Recognition

  19. Possible to say if pollen grain is reticulated or not Difficult for Fraxinus and Phillyrea Possible to distinguish between lumina and muri in most cases Difficult to classify the grain based on reticulum analysis Segmentation is difficult Region analysis and characterization is partly discriminant Reticulum analysis Task 3: Semi-Automatic System for Pollen Recognition

  20. Summary of pollen recognition(estimation on reference grains) Cupressaceae Characteristics: Cytoplasm  Granules  Intine  Broken grains  Global recognition Parietaria Characteristics: Pores  Exine  Global recognition Poaceae Characteristics: Pores  Cytoplasm  Intine  Global recognition Olea Characteristics: Reticulum  Colpi  Exine  Global recognition Ok  Maybe  Difficult / Don't know  Impossible Task 3: Semi-Automatic System for Pollen Recognition

  21. Summary of pollen recognition(estimation on reference grains) Similar pollen types Populus: Intine  Granules  Brassicaceae: Reticulum   Colpi  Exine  Fraxinus: Reticulum  Colpi  Exine  Ligustrum: Reticulum  Colpi  Exine  Phillyrea: Reticulum  Colpi  Exine  Salix: Reticulum  Colpi  Exine  Celtis: Pores  Coriaria: Pores  Broussonetia: Pores  Morus: Pores  Urtica Membranacea: Pores  Ok  Maybe  Difficult / Don't know  Impossible Task 3: Semi-Automatic System for Pollen Recognition

  22. Next: Aerobiological Images • Good classification on reference images do not imply a good classification on aerobiological images • To do: • Clean dust, pollution and bubbles from the pollen masks • Work with partial pollen grain (replace dust with empty spaces) Task 3: Semi-Automatic System for Pollen Recognition

  23. 1) Pollen recognition (WP 5330) Blur analysis Reticulum analysis Summary of characteristic recognition 2) Recognition system integration (WP5330) 3) System Validation (WP6300) Plan Task 3: Semi-Automatic System for Pollen Recognition

  24. Lot of different and separate tools had been developed Still some tools to develop to recognize characteristics No integration is done yet Time estimated to perform all integration: 2 months + Precise recognition results will be available when integration will be done System Status Task 3: Semi-Automatic System for Pollen Recognition

  25. Global measures of the pollen grain Size, Colour, Shape, Convexity (central image) Blur curve analysis (3D) Flowering Period (if given) These measures will give first estimations about the possible type of the grain ex. Cupressaceae 80%, Celtis 75%, Poaceae 70%, … These estimations will help to look deeper in the grain Classification Schema1) Global measures on grain Task 3: Semi-Automatic System for Pollen Recognition

  26. Some specific pollen characteristics are tested depending on first estimations ex. Cupressaceae cytoplasm, Poaceae pore, … All results help to update the estimations ex. A pore is found (probability of 70%) … Cupressaceae 80% 50%, Celtis 75%  80%, Poaceae 70%  85% The system loops until ... no possible confusion nothing more to test Classification Schema2) Specific grain characteristics Task 3: Semi-Automatic System for Pollen Recognition

  27. 1) Pollen recognition (WP 5330) Blur analysis Reticulum analysis Summary of characteristic recognition 2) Recognition system integration (WP5330) 3) System Validation (WP6300) Plan Task 3: Semi-Automatic System for Pollen Recognition

  28. Modules will be validated separately Acquisition module (LASMEA) Recognition module (INRIA) New images will be digitized to validate both modules Validation will be supervised by REA Validation results will be detailed to understand how the system works (or fails) System Validation Task 3: Semi-Automatic System for Pollen Recognition

  29. Preparation of pollen slides for validation Reference slides Aerobilogical slides Validation of LASMEA module (image acquisition) Validation with slides Does the system can extract all pollen grains? Will start in february for about 2 months (system testing + result analysis) Validation of INRIA module (pollen recognition) Validation with image sequences Does the system can recognize the pollen types (identification)? Will start in june for about 2 months (system testing + result analysis) Steps for Validation • 4 ASTHMA pollen types • Similar pollen types • Other pollen types Task 3: Semi-Automatic System for Pollen Recognition

  30. Planning for Task 3 Task 3: Semi-Automatic System for Pollen Recognition

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