François Chung, Ph.D.

Tag: pollen identification

EUE 2017 - Book

EUE 2017 – Book

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Publication

François Chung, Tomás Rodríguez; Multi-focal Image Segmentation, Classification and Authentication: A General Framework applied on Microscope Pollen Images; Éditions universitaires européennes (EUE), Saarbrücken, 2017; ISBN: 978-3841677907.

Abstract

In this book, we propose a general framework for multi-focal image segmentation, 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 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 from microscope images, followed by the automatic segmentation of their exine. A coarse-to-fine approach ensures a smooth and accurate segmentation of both structures. Finally, feature extraction and selection are performed on pollen grains using a generalized approach and the pollen 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 a final authentication stage.

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Publication

Amazon (book)
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APIFRESH (Inspiralia project)

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EUE – Éditions universitaires européennes

Micron 2015 - Academic journal

Micron 2015 – Academic journal article

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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.

Keywords

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

References

Publication

Published version (ScienceDirect)
Preprint version (PDF)
Bibliographic reference (BibTeX)

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APIFRESH (Inspiralia project)
COMPAG 2015 (academic journal article)

APIFRESH

APIFRESH

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Inspiralia project @Madrid, Spain (2013). The competitiveness of the European beekeeping sector is progressively falling due to the reduction of production as a direct consequence of the decrease in bee population. In addition, beekeeping products from countries with lower quality standards are gaining market share in Europe through an unfair competition. Furthermore, there is a lack of standards at European level for certain bee products like pollen and royal jelly.

This means that it is possible to find in the market products under these labels without any control of quality and authenticity. Few countries in Europe have some guidelines or regional standards for products other than honey, which results in a lack of standardization at the European level.

Therefore, the objectives of the APIFRESH project are threefold:

  • to develop European standards for bee pollen and royal jelly;
  • to establish health-relevant criteria for pollen and royal jelly;
  • to determine the authenticity of both pollen and honey.

Partners of this project include:

  • 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).

In this project, my work consists in the development of a software for the bee pollen classification and authentication. In a first step, bee pollen loads captured from a camera are separated by pollen type using a classification based on color. In a second step, a microscope is used to capture an accurate image of pollen grains from which discriminative features are extracted to identify the pollen origin, i.e. by considering the pollen grain as belonging either to a known type (pollen classification) or to an unknown type (outlier detection).

References

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EUE 2017 (book)
COMPAG 2015 (academic journal article)
Micron 2015 (academic journal article)
Inspiralia 2013 (technical report)
Inspiralia 2012 (technical report)

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APIFRESH
Inspiralia

Inspiralia 2013 - Technical report

Inspiralia 2013 – Technical report

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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.

Keywords

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

References

Publication

Published version (arXiv)
Published version (PDF)
Bibliographic reference (BibTeX)

Related articles

APIFRESH (Inspiralia project)
EUE 2017 (book)
Inspiralia 2012 (technical report)

Learn more

Inspiralia

Inspiralia 2012 - Technical report

Inspiralia 2012 – Technical report

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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.

References

Publication

Published version (arXiv)
Published version (PDF)
Bibliographic reference (BibTeX)

Related articles

APIFRESH (Inspiralia project)
EUE 2017 (book)
Inspiralia 2013 (technical report)

Learn more

Inspiralia

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