François Chung, Ph.D.

Tag: 2013

UREBA Exceptionnel

UREBA Exceptionnel

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E²=MC project @Jambes, Belgium (2013). The UREBA Exceptionnel subsidies are subsidies granted by the Walloon Region (BE) for the execution of works, such as the purchase and installation of materials or equipment, aiming to improve the energy efficiency and the rational use of energy in buildings. These subsidies are intended for persons of public law and non-commercial organisations, such as municipalities, schools, hospitals, swimming pools and community services.

The main works taken into account by these subsidies include:

  • the thermal insulation of building walls;
  • the replacement or upgrading of the heating system;
  • the improvement of the lighting system;
  • the implementation of a district heating.

The analytical criteria used to assess both the relevance and effectiveness of the proposed works are:

  • technical characteristics;
  • working hypotheses;
  • sizing of the works;
  • energy saving;
  • reduction of polluting emissions (e.g. CO2 and SO2);
  • economic assessment (e.g. costs and return on investment);
  • relevance of chosen techniques;
  • standards and codes of good behaviour.

As a Consultant in Energy Efficiency, my work consists in analysing these technical, energetic and economic criteria so as to determine the eligibility of the proposed works according to their relevance, and then to rank them according to their energy efficiency.

References

CVIU 2013 - Academic journal

CVIU 2013 – Academic journal article

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Publication

François Chung, Hervé Delingette; Regional appearance modeling based on the clustering of intensity profiles; In: Computer Vision and Image Understanding (CVIU), 117 (6), pp. 705-717, 2013.

Abstract

Model-based image segmentation is a popular approach for the segmentation of anatomical structures from medical images because it includes prior knowledge about the shape and appearance of structures of interest. This paper focuses on the formulation of a novel appearance prior that can cope with large variability between subjects, for instance due to the presence of pathologies. Instead of relying on Principal Component Analysis (PCA) such as in Statistical Appearance Models (SAMs), our approach relies on a multimodal intensity profile atlas from which a point may be assigned to several profile modes consisting of a mean profile and its covariance matrix. These profile modes are first estimated without any intra-subject registration through a boosted Expectation-Maximization (EM) classification based on spectral clustering. Then, they are projected on a reference mesh whose role is to store the appearance information in a common geometric representation. We show that this prior leads to better performance than the classical monomodal PCA approach while relying on fewer profile modes.

Keywords

  • appearance modeling
  • medical imaging
  • model-based image segmentation
  • unsupervised clustering

References

Publication

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

Related articles

3D Anatomical Human (INRIA project)
Ph.D. Thesis 2011 (doctoral thesis)

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

Related articles

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

Learn more

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

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