8/12/2000task 3: semi-automatic system for pollen recognition 1 partners: –rea (barcelona) –rea...

8/12/2000 Task 3: Semi-Automatic System for Pollen Recognition

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Task3 : Semi-Automatic System for Pollen Recognition

Partners:–REA (Barcelona)–REA (Cordoba)–LASMEA (Clermont-Ferrand)–INRIA (Sophia-Antipolis)


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Plan

1) Pollen recognition (WP 5330)• Blur analysis• Reticulum analysis• Summary of characteristic recognition

2) Recognition system integration

(WP5330)

3) System Validation (WP6300)


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How many images are needed?

– 100 images per pollen grain are digitized

– 10 images can be sufficient to recognize a pollen grainProblem: 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


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Clear Image Detection

– 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)


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Clear Image Detection (SML): Cupressaceae


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Clear Image Segmentation: Cupressaceae

41 52 58


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Clear Image Detection (SML): Parietaria


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Clear Image Segmentation: Parietaria

38 43 51


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Clear Image Detection (SML): Poaceae


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Clear Image Segmentation: Poaceae

49 53 65


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Clear Image Detection (SML): Olea


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Clear Image Segmentation: Olea

27 33 44 56 63


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– Reticulated pollen types: • Olea

• Brassicaceae, Fraxinus, Ligustrum, Phillyrea, Salix

Reticulum Analysis

– The reticulum is located at top (or bottom) surface of the grain


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Reticulum Analysis

– Steps to follow:• Detection of reticulum (reticulated grain or not?)• Characterization of the reticulum (Lumina / Muri)• Analysis of Lumina• Classification based on the reticulum


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Reticulum Analysis (Brassicaceae)

Case 1: Lumina dark, Muri light

Case 2: Lumina light, Muri dark


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Reticulum Analysis (Brassicaceae)

Problem: On some images, the lumina are dark AND light


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Reticulum Analysis (Ligustrum)

Case 1: Lumina dark, Muri light

Case 2: Lumina light, Muri dark


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Reticulum Analysis (Ligustrum)

Problem: For Ligustrum, muri can be dark and light (Columelae)

The analysis resulted here in light lumina


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Reticulum analysis

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


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Summary of pollen recognition

(estimation on reference grains)CupressaceaeCharacteristics: Cytoplasm Granules Intine Broken grains Global recognition

ParietariaCharacteristics: Pores Exine Global recognition

PoaceaeCharacteristics: Pores Cytoplasm Intine Global recognition

OleaCharacteristics: Reticulum Colpi Exine Global recognition

Ok Maybe Difficult / Don't know Impossible


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Similar pollen typesPopulus: 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

Summary of pollen recognition

(estimation on reference grains)

Ok Maybe Difficult / Don't know Impossible


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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)


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Plan


2) Recognition system integration (WP5330)



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System Status

– 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


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Classification Schema1) Global measures on grain

– 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


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Classification Schema2) Specific grain characteristics

– 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


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Plan


2) Recognition system integration (WP5330)



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System Validation

– 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)


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Steps for Validation

– 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)

• 4 ASTHMA pollen types• Similar pollen types• Other pollen types


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Planning for Task 3

8/12/2000task 3: semi-automatic system for pollen recognition 1 partners: –rea (barcelona) –rea...

Documents

semiautomatic system

pollen recognitionpartners

pollen recognitionhow

pollen grainproblem

pollen characteristics

grainreticulated pollen

central image

image sequencedetection