François Chung, Ph.D.

Tag: processament d’imatges

EUE 2017 - Llibre

EUE 2017 – Llibre

Publicació

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.

Referències

Publicació

Amazon (llibre)
MoreBooks (llibre)
Referència bibliogràfica (BibTeX)

Article relacionat

APIFRESH (projecte Inspiralia)

Més informació

EUE – Éditions universitaires européennes

CVIU 2013 - Article de revista científica

CVIU 2013 – Article de revista científica

Publicació

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.

Paraules clau

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

Referències

Publicació

Articles relacionats

3D Anatomical Human (projecte INRIA)
Ph.D. Thesis 2011 (tesi doctoral)

APIFRESH

APIFRESH

Projecte Inspiralia @Madrid, Espanya (2013). La competitivitat del sector apícola europeu està caient a causa de la reducció de la producció dels apicultors com a conseqüència directa de la disminució de la població d'abelles. A més, els productes de països amb estàndards de qualitat inferiors estan guanyant quota de mercat a Europa a través d'una competència deslleial. A tot això es suma una manca de normes a nivell europeu per a certs productes del rusc.

Això significa que és possible trobar al mercat productes sota aquestes etiquetes sense cap control de qualitat i autenticitat. Pocs països a Europa tenen unes pautes o normes regionals per als productes diferents de la mel, el que es tradueix en una manca d'estandardització a nivell europeu.

Per tant, els objectius del projecte APIFRESH són tres:

  • desenvolupar normes europees per al pol·len d'abella i la gelea reial;
  • establir criteris de salut rellevants per al pol·len i la gelea reial;
  • determinar l'autenticitat del pol·len i de la mel.

Els socis d'aquest projecte són:

  • 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 aquest projecte, la meva feina consisteix en el desenvolupament d'un programari per a la classificació i l'autenticació del pol·len d'abella. Primer, les càrregues de pol·len d'abella capturats mitjançant una càmera estan separades per tipus de pol·len utilitzant una classificació basada en el color. Segon, un microscopi s'utilitza per a capturar una imatge precisa dels grans de pol·len de la qual s'extreuen característiques discriminatives per a identificar l'origen del pol·len, és a dir, considerant el gra de pol·len com pertanyent a un tipus conegut (classificació) o a un tipus desconegut (autenticació).

Referències

Articles relacionats

EUE 2017 (llibre)
COMPAG 2015 (article de revista científica)
Micron 2015 (article de revista científica)
Inspiralia 2013 (informe tècnic)
Inspiralia 2012 (informe tècnic)

Més informació

Inspiralia 2013 - Informe tècnic

Inspiralia 2013 – Informe tècnic

Publicació

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.

Paraules clau

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

Referències

Publicació

Articles relacionats

APIFRESH (projecte Inspiralia)
EUE 2017 (llibre)
Inspiralia 2012 (informe tècnic)

Més informació

Inspiralia 2012 - Informe tècnic

Inspiralia 2012 – Informe tècnic

Publicació

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.

Referències

Publicació

Articles relacionats

APIFRESH (projecte Inspiralia)
EUE 2017 (llibre)
Inspiralia 2013 (informe tècnic)

Més informació