François Chung, Ph.D.

Tag: microscopio

EUE 2017 - Libro

EUE 2017 – Libro

Publicación

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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Publicación

Amazon (libro)
MoreBooks (libro)
Referencia bibliográfica (BibTeX)

Artículo relacionado

APIFRESH (proyecto Inspiralia)

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EUE – Éditions universitaires européennes

COMPAG 2015 - Artículo de revista científica

COMPAG 2015 – Artículo de revista científica

Publicación

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.

Palabras clave

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

Referencias

Publicación

Artículos relacionados

APIFRESH (proyecto Inspiralia)
Micron 2015 (artículo de revista científica)

APIFRESH

APIFRESH

Proyecto Inspiralia @Madrid, España (2013). La competitividad del sector apícola europeo se va cayendo debido a la reducción de la producción como consecuencia de la disminución de la población de abejas. Además, los productos de países con estándares de calidad inferiores están ganando partes de mercado en Europa a través de una competencia desleal. A todo eso se suma una falta actual de normas a nivel europeo para ciertos productos de la colmena.

Esto significa que es posible encontrar en el mercado productos con estas etiquetas sin ningún control de calidad y autenticidad. Pocos países en Europa disponen de unas pautas o normas regionales para los productos distintos de la miel, lo que se traduce en una falta de estandarización a nivel europeo.

Por lo tanto, los objetivos del proyecto APIFRESH son tres:

  • desarrollar normas europeas para el polen de abeja y la jalea real;
  • establecer criterios de salud relevantes para el polen y la jalea real;
  • determinar la autenticidad del polen y de la miel.

Los socios de este proyecto son:

  • 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).

En este proyecto, mi trabajo consiste en el desarrollo de un software para la clasificación y la autenticación del polen de abeja. Primero, las cargas de polen de abeja capturadas a partir de una cámara están separadas por tipo de polen utilizando una clasificación basada en el color. Segundo, un microscopio se utiliza para capturar una imagen precisa de los granos de polen a partir de la cual se extraen características discriminativas para identificar el origen del polen, es decir, considerando el grano de polen como perteneciente a un tipo conocido (clasificación) o a un tipo desconocido (autenticación).

Referencias

Artículos relacionados

EUE 2017 (libro)
COMPAG 2015 (artículo de revista científica)
Micron 2015 (artículo de revista científica)
Inspiralia 2013 (informe técnico)
Inspiralia 2012 (informe técnico)

Más información

Inspiralia 2013 - Informe técnico

Inspiralia 2013 – Informe técnico

Publicación

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.

Palabras clave

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

Referencias

Publicación

Artículos relacionados

APIFRESH (proyecto Inspiralia)
EUE 2017 (libro)
Inspiralia 2012 (informe técnico)

Learn more

Inspiralia 2012 - Informe técnico

Inspiralia 2012 – Informe técnico

Publicación

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.

Referencias

Publicación

Artículos relacionados

APIFRESH (proyecto Inspiralia)
EUE 2017 (libro)
Inspiralia 2013 (informe técnico)

Más información