François Chung, Ph.D.

Tag: inspiralia

COMPAG 2015 - Article de revue scientifique

COMPAG 2015 – Article de revue scientifique

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Publication

Rafael Redondo, Gloria Bueno, François Chung, Rodrigo Nava, J. Víctor Marcos, Gabriel Cristóbal, Tomás Rodríguez, Amelia González-Porto, Cristina Pardo, Óscar Déniz, Boris Escalante-Ramírez; Pollen segmentation and feature evaluation for automatic classification in bright-field microscopy; In: Computers and Electronics in Agriculture (COMPAG), 110, pp. 56–69, 2015.

Abstract

Besides the well-established healthy properties of pollen, palynology and apiculture are of extreme importance to avoid hard and fast unbalances in our ecosystems. To support such disciplines, computer vision comes to alleviate tedious recognition tasks. In this paper, we present an applied study of the state of the art in pattern recognition techniques to describe, analyze, and classify pollen grains in an extensive dataset specifically collected (15 types, 120 samples/type). We also propose a novel contour-inner segmentation of grains, improving 50% of accuracy. In addition to published morphological, statistical, and textural descriptors, we introduce a new descriptor to measure the grain’s contour profile and a logGabor implementation not tested before for this purpose. We found a significant improvement for certain combinations of descriptors, providing an overall accuracy above 99%. Finally, some palynological features that are still difficult to be integrated in computer systems are discussed.

Mots-clés

  • apiculture
  • automatic classification
  • bright-field microscopy
  • feature extraction
  • Fisher discriminant analysis
  • image processing
  • morphology descriptors
  • pollen
  • statistical descriptors
  • texture descriptors

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Related articles

APIFRESH (projet Inspiralia)
Micron 2015 (article de revue scientifique)

Micron 2015 - Article de revue scientifique

Micron 2015 – Article de revue scientifique

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Publication

J. Víctor Marcos, Rodrigo Nava, Gabriel Cristóbal, Rafael Redondo, Boris Escalante-Ramírez, Gloria Bueno, Óscar Déniz, Amelia González-Porto, Cristina Pardo, François Chung, Tomás Rodríguez; Automated pollen identification using microscopic imaging and texture analysis; In: Micron, 68, pp. 36-46, 2015.

Abstract

Pollen identification is required in different scenarios such as prevention of allergic reactions, climate analysis or apiculture. However, it is a time-consuming task since experts are required to recognize each pollen grain through the microscope. In this study, we performed an exhaustive assessment on the utility of texture analysis for automated characterisation of pollen samples. A database composed of 1800 brightfield microscopy images of pollen grains from 15 different taxa was used for this purpose. A pattern recognition-based methodology was adopted to perform pollen classification. Four different methods were evaluated for texture feature extraction from the pollen image: Haralick's gray-level co-occurrence matrices (GLCM), log-Gabor filters (LGF), local binary patterns (LBP) and discrete Tchebichef moments (DTM). Fisher's discriminant analysis and k-nearest neighbour were subsequently applied to perform dimensionality reduction and multivariate classification, respectively. Our results reveal that LGF and DTM, which are based on the spectral properties of the image, outperformed GLCM and LBP in the proposed classification problem. Furthermore, we found that the combination of all the texture features resulted in the highest performance, yielding an accuracy of 95%. Therefore, thorough texture characterisation could be considered in further implementations of automatic pollen recognition systems based on image processing techniques.

Mots-clés

  • discrete Tchebichef moments
  • gray-level co-occurrence matrix
  • local binary patterns
  • Log-Gabor filters
  • pollen identification
  • texture analysis

Références

Publication

Version publiée (ScienceDirect)
Version pre-print (PDF)
Référence bibliographique (BibTeX)

Articles associés

APIFRESH (projet Inspiralia)
COMPAG 2015 (article de revue scientifique)

APIFRESH

APIFRESH

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Projet Inspiralia @Madrid, Espagne (2013). La compétitivité du secteur apicole européen est en plein déclin suite à la réduction de la production due à une diminution du nombre d'abeilles. En outre, les produits provenants de pays où les normes de qualité sont moindres gagnent des parts de marché en Europe grâce à une concurrence déloyale. A cela s'ajoute une absence de normes au niveau européen pour certains produits apicoles tels que le pollen et la gelée royale.

Concrètement, cela signifie qu'il est possible de trouver sur le marché des produits étiquetés comme tel sans aucun contrôle de qualité et d'authenticité. Peu de pays en Europe ont des recommandations ou des normes régionales pour des produits autres que le miel, ce qui a pour conséquence un manque de standardisation au niveau européen.

Par conséquent, les objectifs du projet APIFRESH sont triples:

  • définir des normes européennes pour le pollen d'abeille et la gelée royale;
  • établir des critères de santé pertinents pour le pollen et la gelée royale;
  • déterminer l'authenticité du pollen et du miel.

Les partenaires de ce projet sont:

  • Balparmak (TR);
  • Campomiel (ES);
  • Centro Agrario de Marchamalo (ES);
  • CTC - Centro Tecnológico Nacional de la Conserva y Alimentación (ES);
  • EPBA - European Professional Beekeepers Association (EU);
  • FNAP - Federação Nacional dos Apicultores de Portugal (PT);
  • Inspiralia (ES);
  • OMME - Országos Magyar Méhészeti Egyesület (HU);
  • Parco Tecnologico Padano (IT);
  • TÜBITAK-MAM - Türkiye Bilimsel ve Teknolojik Araştırma Kurumu-Marmara Araştırma Merkezi (TR).

Dans ce projet, mon travail consiste à développer un logiciel pour la classification et l'authentification du pollen d'abeille. Premièrement, les boules de pollen d'abeille sont photographiées et séparées par type de pollen en utilisant une classification basée sur la couleur. Ensuite, un microscope est utilisé pour capturer une image précise des grains de pollen à partir de laquelle des caractéristiques discriminantes sont extraites afin d'identifier l'origine du pollen, et ce, en considérant le grain de pollen comme appartenant à un type connu (classification) ou à un type inconnu (authentification).

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Articles associés

EUE 2017 (livre)
COMPAG 2015 (article de revue scientifique)
Micron 2015 (article de revue scientifique)
Inspiralia 2013 (rapport technique)
Inspiralia 2012 (rapport technique)

En savoir plus

APIFRESH
Inspiralia

Inspiralia 2013 - Rapport technique

Inspiralia 2013 – Rapport technique

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Publication

François Chung, Tomás Rodríguez; A general framework for multi-focal image classification and authentication: Application to microscope pollen images; Inspiralia, Madrid, 2013.

Abstract

In this article, we propose a general framework for multi-focal image classification and authentication, the methodology being demonstrated on microscope pollen images. The framework is meant to be generic and based on a brute force-like approach aimed to be efficient not only on any kind, and any number, of pollen images (regardless of the pollen type), but also on any kind of multi-focal images. All stages of the framework's pipeline are designed to be used in an automatic fashion. First, the optimal focus is selected using the absolute gradient method. Then, pollen grains are extracted using a coarse-to-fine approach involving both clustering and morphological techniques (coarse stage), and a snake-based segmentation (fine stage). Finally, features are extracted and selected using a generalized approach, and their classification is tested with four classifiers: Weighted Neighbor Distance, Neural Network, Decision Tree and Random Forest. The latter method, which has shown the best and more robust classification accuracy results (above 97% for any number of pollen types), is finally used for the authentication stage.

Mots-clés

  • generalized feature extraction
  • image classification
  • microscope images
  • optimal focus selection
  • pollen authentication
  • Random Forest
  • snake-based segmentation
  • supervised clustering

Références

Publication

Version publiée (arXiv)
Version publiée (PDF)
Référence bibliographique (BibTeX)

Related articles

APIFRESH (projet Inspiralia)
EUE 2017 (livre)
Inspiralia 2012 (rapport technique)

En savoir plus

Inspiralia

Inspiralia 2012 - Rapport technique

Inspiralia 2012 – Rapport technique

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Publication

François Chung, Tomás Rodríguez; Automatic pollen grain and exine segmentation from microscope images; Inspiralia, Madrid, 2012.

Abstract

In this article, we propose an automatic method for the segmentation of pollen grains from microscope images, followed by the automatic segmentation of their exine. The objective of exine segmentation is to separate the pollen grain in two regions of interest: exine and inner part. A coarse-to-fine approach ensures a smooth and accurate segmentation of both structures. As a rough stage, grain segmentation is performed by a procedure involving clustering and morphological operations, while the exine is approximated by an iterative procedure consisting in consecutive cropping steps of the pollen grain. A snake-based segmentation is performed to refine the segmentation of both structures. Results have shown that our segmentation method is able to deal with different pollen types, as well as with different types of exine and inner part appearance. The proposed segmentation method aims to be generic and has been designed as one of the core steps of an automatic pollen classification framework.

Références

Publication

Version publiée (arXiv)
Version publiée (PDF)
Référence bibliographique (BibTeX)

Articles associés

APIFRESH (projet Inspiralia)
EUE 2017 (livre)
Inspiralia 2013 (rapport technique)

En savoir plus

Inspiralia

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