A Fast Learning Algorithm for Image Segmentation with Max-Pooling Convolutional Networks

We present a fast algorithm for training MaxPooling Convolutional Networks to segment images. This type of network yields record-breaking performance in a variety of tasks, but is normally trained on a computationally expensive patch-by-patch basis. Our new method processes each training image in a single pass, which is vastly more efficient. We validate the approach in different scenarios and report a 1500-fold speed-up. In an application to automated steel defect detection and segmentation, we obtain excellent performance with short training times

From Supervised to Reinforcement Learning: a Kernel-based Bayesian Filtering Framework

International audienceIn a large number of applications, engineers have to estimate a function linked to the state of a dynamic system. To do so, a sequence of samples drawn from this unknown function is observed while the system is transiting from state to state and the problem is to generalize these observations to unvisited states. Several solutions can be envisioned among which regressing a family of parameterized functions so as to make it fit at best to the observed samples. This is the first problem addressed with the proposed kernel-based Bayesian filtering approach, which also allows quantifying uncertainty reduction occurring when acquiring more samples. Classical methods cannot handle the case where actual samples are not directly observable but only a non linear mapping of them is available, which happens when a special sensor has to be used or when solving the Bellman equation in order to control the system. However the approach proposed in this paper can be extended to this tricky case. Moreover, an application of this indirect function approximation scheme to reinforcement learning is presented. A set of experiments is also proposed in order to demonstrate the efficiency of this kernel-based Bayesian approach

Automatic Polynomial Form Correction with Autocorrelation Functions

12 pages, also available on the WEB in HTML at: http://cmm.ensmp.fr/~nion/Travaux/FormCorrection/Dealing with a surface's form is an essential part of any topographical study of the said surface's roughness. We present here a method for automatic correction of the form, using very simple concepts to solve a problem where top notch technologies were not enough mature to tackle. After explaining how well known mathematical tools such as the correlation function relate to this problem, we will see how they helped us to design an automatic procedure that we applied in order to get a highly reliable data set, that, in turn, underpinned a fine grain study on rough surfaces conducted at ArcelorMittal Research

Kernelizing Vector Quantization Algorithms

International audienceThe kernel trick is a well known approach allowing to implicitly cast a linear method into a nonlinear one by replacing any dot product by a kernel function. However few vector quantization algorithms have been kernelized. Indeed, they usually imply to compute linear transformations (e.g. moving prototypes), what is not easily kernelizable. This paper introduces the Kernel-based Vector Quantization (KVQ) method which allows working in an approximation of the feature space, and thus kernelizing any Vector Quantization (VQ) algorithm

Numerical simulation of thin paint film flow

International audienceBeing able to predict the visual appearance of a painted steel sheet, given its topography before paint application, is of crucial importance for car makers. Accurate modeling of the industrial painting process is required. The equations describing the leveling of the paint are complex and their numerical simulation requires advanced mathematical tools, which are described in detail in this paper. Simulations are validated using a large experimental database obtained with a wavefront sensor developed by PhasicsTM

A QUANTITATIVE METHOD FOR ANALYSING 3-D BRANCHING IN EMBRYONIC KIDNEYS: DEVELOPMENT OF A TECHNIQUE AND PRELIMINARY DATA

The normal human adult kidney contains between 300,000 and 1 million nephrons (the functional units of the kidney). Nephrons develop at the tips of the branching ureteric duct, and therefore ureteric duct branching morphogenesis is critical for normal kidney development. Current methods for analysing ureteric branching are mostly qualitative and those quantitative methods that do exist do not account for the 3- dimensional (3D) shape of the ureteric "tree". We have developed a method for measuring the total length of the ureteric tree in 3D. This method is described and preliminary data are presented. The algorithm allows for performing a semi-automatic segmentation of a set of grey level confocal images and an automatic skeletonisation of the resulting binary object. Measurements of length are automatically obtained, and numbers of branch points are manually counted. The final representation can be reconstructed by means of 3D volume rendering software, providing a fully rotating 3D perspective of the skeletonised tree, making it possible to identify and accurately measure branch lengths. Preliminary data shows the total length estimates obtained with the technique to be highly reproducible. Repeat estimates of total tree length vary by just 1-2%. We will now use this technique to further define the growth of the ureteric tree in vitro, under both normal culture conditions, and in the presence of various levels of specific molecules suspected of regulating ureteric growth. The data obtained will provide fundamental information on the development of renal architecture, as well as the regulation of nephron number

Différences Temporelles de Kalman : le cas stochastique

Les différences temporelles de Kalman (KTD pour Kalman Temporal Differences) sont un cadre de travail statistique qui traite de l'approximation de la fonction de valeur et de qualité en apprentissage par renforcement. Son principe est d'adopter une représentation paramétrique de la fonction de valeur, de modéliser les paramètres associés comme des variables aléatoires et de minimiser l'espérance de l'erreur quadratique moyenne des paramètres conditionnée à l'ensemble des récompenses observées. Ce paradigme s'est montré efficace en terme d'échantillons (i.e. convergence rapide), capable de prendre en compte la non-stationnarité ainsi que de fournir une information d'incertitude. Cependant ce cadre de travail était restreint au processus décisionnels de Markov bénéficiant de transitions déterministes. Dans cette contribution nous proposons d'étendre le modèle au transitions stochastiques à l'aide d'un bruit coloré, ce qui mène aux différences temporelles de Kalman étendues (XKTD pour eXtended KTD). L'approche proposée est illustrée sur des problèmes usuels en apprentissage par renforcement

Propriétés morphologiques et optiques des surfaces rugueuses

Topographical, physical and chemical properties of rough surfaces are strongly involved in their visual. The understanding of the phenomena determining the appearance of a product represents a growing scientific as well as economic challenge. In this thesis, we introduce morphological and statistical features designed for characterising textures in a qualitative as well as quantitative way. These data are processed by factorial analysis methods, systematic bootstrap over-sampling and bayesian classification techniques used together to produce an automatic algorithm of surface appearance quality estimation. These theoretical advances have been applied to an industrial context. A vision system enables us to take pictures continuously during the production process of orange peel surfaces. These images taken on line are then processed to analyse the visual quality of the product.Les propriétés topographiques et physico-chimiques d'une surface conditionnent en grande partie la qualité de son aspect. La maîtrise des phénomènes déterminant l'apparence d'un produit représente aujourd'hui un fort enjeu industriel et scientifique. Dans cette thèse nous introduisons des descripteurs morphologiques et statistiques permettant de caractériser une texture aussi bien qualitativement que quantitativement. Ces informations sont exploitées par l'intermédiaire de techniques d'analyse factorielle, de sur-échantillonage systématique de type "bootstrap" et de classification bayesienne ayant permis la conception d'un algorithme automatique d'estimation de la qualité de l'apparence d'une surface. Ces développements théoriques ont été appliqués au contrôle qualité des surfaces sur ligne de production grâce à un système de vision permettant d'acquérir des images en continu

Différences Temporelles de Kalman

Cette contribution traite de l'approximation de la fonction de valeur ainsi que de la Q-fonction dans des processus décisionnels de Markov déterministes. Un cadre de travail statistique général inspiré du filtrage de Kalman est introduit. Son principe est d'adopter une représentation paramétrique de la fonction de valeur (ou de la Q-fonction), de modéliser le vecteur de paramètres associé comme une variable aléatoire et de minimiser l'erreur quadratique sur les paramètres conditionnée aux récompenses observées depuis l'origine des temps. De ce paradigme général, que nous nommons Différences Temporelles de Kalman (KTD pour Kalman Temporal Differences), et en utilisant un schéma d'approximation appelé transformation non-parfumée, une famille d'algorithmes est dérivée, à savoir KTD-V, KTD-SARSA et KTD-Q, qui ont respectivement comme objectif l'évaluation de la fonction de valeur pour une politique donnée, l'évaluation de la Q-fonction pour une politique donnée, et l'évaluation de la Q-fonction optimal. Cette approche présente un certain nombre d'avantages tels que la capacité à prendre en compte une paramétrisation non-linéaire, l'efficacité de l'apprentissage en terme d'échantillons observés, la prise en compte d'environnements non-stationnaires ou encore la possibilité d'obtenir une information d'incertitude, que nous utiliserons pour proposer une forme d'apprentissage actif. Ces différents aspects seront discutés et illustrés au travers de plusieurs expériences