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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) Recognition system integration (WP5330) Global measures computed Flowering information Specific characteristics (cytoplasm of Cupressaceae)

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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) Recognition system integration (WP5330) Global measures computed Flowering information Specific characteristics (cytoplasm of Cupressaceae) Partial integration 2) System Validation (WP6300) Plan Task 3: Semi-Automatic System for Pollen Recognition

  3. Two steps for pollen recognition - Compute global measures on the grain - Search specific characteristics Integration of all components almost finished for the first step in progress for the second step Steps for recognition Task 3: Semi-Automatic System for Pollen Recognition

  4. Size, colour (RGB), shape, convexity Flowering period (if given) These measures give the first estimations about the type help to select which characteristics to search Output: sorted list of possible types Examples: Grain(Olea) : Olea (86%), Salix (35%), Quercus (34%) Grain(Populus) : Populus (58%), Cupressaceae (47%), Plantago (46%) Grain(Brassicaceae) : Salix (54%), Brassicaceae (41%), Plantago (27%) Grain(Celtis) : Cupressaceae (70%), Plantago (53%), Platanus (51%) Compute Global Measures Task 3: Semi-Automatic System for Pollen Recognition

  5. Methodology: Segmentation of the central image Image measures done on this image Covariance matrices computed for each type Classification done using Mahalanobis distance For each grain, a list of possible types is built Improvement (in progress): Principal component analysis on the features Compute Global Measures Task 3: Semi-Automatic System for Pollen Recognition

  6. Intermediate results on reference images: On the four ASTHMA types: 99 % On 30 different types: 66 % Separation in two classes: Cupressaceae and Brassicaceae Some false positives for Cupressaceae and Poaceae Intermediate results on aerobiological images: On the four ASTHMA types: N.A. On different types:  30% Need more information for classification for redundancies to work on other types (other cities) Compute Global Measures Task 3: Semi-Automatic System for Pollen Recognition

  7. Similar types using colour and size for classification: Cupressaceae: Plantago, Platanus, Populus Olea: Quercus, Salix, Alnus Parietaria: Plantago, Cupressaceae, Brassicaceae (??) Poaceae: none Types with big variances (with lot of false positives): Coriaria, Plantago, Ambrosia, Quercus, Alnus, Brassicaceae Solutions: Separate in different classes some types More accurate classification: discrete component analysis Compute Global Measures Task 3: Semi-Automatic System for Pollen Recognition

  8. Flowering information Task 3: Semi-Automatic System for Pollen Recognition

  9. Flowering information used mean weekly pollen concentration tested with data of Manresa (Barcelona pilot site) Classification methodology Input: sampling date (week) Two possible functions: Probability(type) = Concentration (type,week) type Concentration (type, week) Probability(type) = Concentration (type,week) week Concentration (type, week) Flowering information Task 3: Semi-Automatic System for Pollen Recognition

  10. Classification using only flowering information Random test with 1000 samples per class Probability(type) = Concentration (type,week) type Concentration (type, week) Flowering information Results: Task 3: Semi-Automatic System for Pollen Recognition

  11. Classification using only flowering information Random test with 1000 samples per class Probability(type) = Concentration (type,week) week Concentration (type, week) Flowering information Results: Task 3: Semi-Automatic System for Pollen Recognition

  12. Problem Can increase the number of false positives Examples (Manresa): Cupressaceae, Quercus, Pinus, Urticaceae (Parietaria), Chenopodiaceae-Amarathaceae Solutions Give a smaller weight in the global evaluation Different evaluation functions for the flowering period Can help at the end to discriminate similar pollen types Confused types are not the same than with image processing Flowering information Task 3: Semi-Automatic System for Pollen Recognition

  13. Second step of recognition Look for cytoplasm, reticulum, pores, … Search done for the most probable types Search uses 3D information (10 images) Two steps: Segmentation of several chosen 2D images Validation of results in 3D on all segmented images Specific Characteristics Task 3: Semi-Automatic System for Pollen Recognition

  14. Specific Characteristics(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

  15. Cytoplasm is in the center of the pollen grains Easily visible for the Cupressaceae type No precise shape to look for Methodology to detect it: Look for bright regions in images above the center Look for dark regions in images below the center Compare bright and dark regions (overlapping) Specific Characteristics(cytoplasm of Cupressaceae) Task 3: Semi-Automatic System for Pollen Recognition

  16. Above central image Below central image Specific Characteristics(cytoplasm of Cupressaceae) Sum of bright regions Sum of dark regions Task 3: Semi-Automatic System for Pollen Recognition

  17. Validation use the same tools than 1st step measures covariance matrices on selected criteria For cytoplasm, 4 criteria are used shape, colour, size and overlapping are used Classification results using only the cytoplasm detection on reference images 7 / 12 Cupressaceae grains with cytoplasm detected (58%) 5 false positives on more than 350 grains tested Similar types : Poaceae, Salix and Parietaria different list than with measures of 1st step Specific Characteristics(cytoplasm of Cupressaceae) Task 3: Semi-Automatic System for Pollen Recognition

  18. Simple test of classification on reference images with several criteria 40% global measures 30% cytoplasm detection 15% flowering information (relative on week) 15% flowering information (relative on type) Results: On the four ASTHMA types: 97 % On 30 different types: 73 % Overall number of false positives has decreased No results so far on aerobiological images Partial Integration Task 3: Semi-Automatic System for Pollen Recognition

  19. Results can be improved (on reference images) better combination than just a weighed function refinement of the criteria Redundancy is necessary to improve recognition of various grains to work with aerobiological images Several methods are combined Each of them give a sorted list of possible types Similar types are different between methods Partial Integration Task 3: Semi-Automatic System for Pollen Recognition

  20. Next: Aerobiological Images • Good classification on reference images does 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

  21. WP6300 Semi-automatic system for pollen recognition: validation phase • System Validation: evaluate the quality of the developed pollen recognition system. Responsible: REA Partners: INRIA, LASMEA Start: T30 Finish: T36 • The modules of the system will be validated separately • Acquisition module (LASMEA). • Recognition module (INRIA). Task 3: Semi-Automatic System for Pollen Recognition

  22. System Validation (WP6300) Steps for validation of the acquisition module (LASMEA) 1st step: Pollen slides to test the detection and localization of the main pollen types of ASTHMA. Started at the end of February in Clermont. 2nd step: Aerobiological slides to test the detection and localization in real conditions. Projected to do in April. In both cases the sequences will be used to validate the recognition module (INRIA) Task 3: Semi-Automatic System for Pollen Recognition

  23. Pollen Type Poaceae Cupressaceae Parietaria Olea Total pollen analysed 273 325 89 - Number of pollen located 259 29 1 89 - Nº pollen not located 14 34 0 - Percentage of location 94,9 % 89,5 % 100 % 0% Validation of the image acquisition module (LASMEA): Results of the 1st step: to test the detection and localization of the main pollen types of ASTHMA. Task 3: Semi-Automatic System for Pollen Recognition

  24. Total number of sequences to validate the recognition module (INRIA) Pollen type digitised Nº Slide Pollen type Nº sequences Poaceae R121 Poaceae 20 Cupressaceae R123 Cupressaceae 25 Parietaria R135 Parietaria 20 Ol ea R133 Olea 21 Populus R127 Populus 5 Broussonetia R129 Mixture 4 Fraxinus R129 Mixture 6 Phillyrea R129 Mixture 6 Pinus R129 Mixture 1 Morus R129 Mixture 6 Brassicaceae R129 Mixture 5 Ligustrum R129 Mixture 5 Urtica membranaceae R129 Mixture 6 Salix R118 Salix 5 Celtis R77 Celtis 3 Coriaria R81 Coriaria 5 Quercus R73 Quercus 3 Platanus R63 Platanus 3 TOTAL 149 Task 3: Semi-Automatic System for Pollen Recognition

  25. Conclusions • The detection and localisation of Poaceae, Cupressaceae and Parietaria by the LASMEA system give us excellent results (near to 95%). • The localisation is specially accurate in the small types like Parietaria. • The fault in the localisation is due to pollen grouped in most of the cases,. Pollen grouped is a normal effect in pollen slides but it is not frequent in aerobiological slides. • At present, the system can not detect and localise the Olea pollen grains. The reason of this problem could be the different coloration of the Olea pollen type. As possible solutions to improve the detection we are considering: • The use of a blue filter to minimize the yellow effect in the microscopy lamp. • Some adjustments in the detection and localisation parameters. • To use other magnification that minimise this problem. E.g. 10x although other problems can be encountered. Task 3: Semi-Automatic System for Pollen Recognition

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