François Chung, Ph.D.

Tag: analyse de texture

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)

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.

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Version publiée (arXiv)
Version publiée (PDF)
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Articles associés

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

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