Skip to Main content Skip to Navigation

Détection de structures fines par traitement d'images et apprentissage statistique : application au contrôle non destructif

Abstract : This PhD is dedicated to new image processing methods to extract or enhance thinobjects from an image. These methods stem from mathematical morphology, and they mainly focuson the accuracy of the detection and on the computation time. This second constraint is imposed bythe fact that we are dealing with high-throughput applications. The first part of this thesis presentsthese methods, organized according to the tortuosity of the objects to detect. We first propose afast algorithm for the computation of 1-D openings, used to extract thin and straight structures in theimages. Then, we study a new class of fast operators, parsimonious path openings, which can extractthin structures with moderate tortuosities. Finally, we propose new adaptive structuring elementsand new thinnings with geodesic and geometric attributes to filter out the noise and to enhance thinstructures of any tortuosity.Besides, we have developed a machine learning method by introducing a new adaptive penalization.We aim at creating a predictive model that minimizes a cost function (independent of the data)while preserving a good accuracy. When this cost function is linked to the computation time of eachfeature, the resulting models will optimize the timings, while preserving a good accuracy. This methodis a generalization of linear and logistic regressions with Ridge, Forward stagewise, Lar or Lassopenalization.The algorithms developed in this thesis have been used for three industrial applications. While theirobjectives are very different, the framework is the same (non-destructive testing) and they all involvea multidisciplinary approach (images processing and statistical analysis). The combination of thesetwo fields yields a higher flexibility in comparison with classical methods. Generic strategies are used,since image processing operators are associated to statistical learning (supervised or unsupervised)to make a specific treatment for each application.Keywords: Image processing, mathematical morphology, statistical analysis, pattern recognition,non destructive testing, parsimonious path openings, region growing structuring elements, geodesicand topologic attributes thinnings, adaptive coefficient shrinkage.
Document type :
Complete list of metadata
Contributor : ABES STAR :  Contact
Submitted on : Friday, January 17, 2014 - 3:37:09 PM
Last modification on : Wednesday, November 17, 2021 - 12:27:14 PM
Long-term archiving on: : Friday, April 18, 2014 - 4:43:49 AM


Version validated by the jury (STAR)


  • HAL Id : pastel-00932727, version 1


Vincent Morard. Détection de structures fines par traitement d'images et apprentissage statistique : application au contrôle non destructif. Mathématiques générales [math.GM]. Ecole Nationale Supérieure des Mines de Paris, 2012. Français. ⟨NNT : 2012ENMP0101⟩. ⟨pastel-00932727⟩



Record views


Files downloads