175 research outputs found

    The Prototyping and Focused Discriminating Strategy for Pattern Recognition and one Instantiation: the MELIDIS System

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    This paper presents the Prototyping and Focused Discriminating (PFD) strategy for pattern recognition. This strategy takes benefits from the duality between model generation and discrimination. Both collaborate through a focusing mechanism that detects the conflicts between the class models and drive the discrimination. Classifiers based on this collaboration benefit from a set of useful properties. The MĂ©lidis system illustrates this strategy and extends its possibilities, using a fuzzy framework. As shown by experiments, the resulting system provides an interesting compromise between accuracy and compactness. Experiments also demonstrate the interest of the new strategy and of its focusing mechanism

    Adaptation de modèles de Markov cachés - Application à la reconnaissance de caractères imprimés

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    International audienceWe present in this paper a new algorithm for the adaptation of hidden Markov models (HMM models). The principle of our iterative adaptive algorithm is to alternate an HMM structure adaptation stage with an HMM Gaussian MAP adaptation stage. This algorithm is applied to the recognition of printed characters to adapt the models learned by a polyfont character recognition engine to new forms of characters. Comparing the results with those of MAP and MLLR classic adaptations shows a slight increase in the performance of the recognition system

    Prediction of Selection Decision of Document Using Bibliographic Data at the National Library of France (BnF)

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    p. 135-140International audienceThe selection process of the documents is a very important step in mass digitization projects. This is especially true at the BnF, where the digitization should include or not OCRization depending on the OCR results expected. Consequently, the selection task is very complex and time consuming due to the number of documents to be processed and the diversity of the selection criteria to consider. Trying to improve and simplify this task by automation, we studied the relationship between bibliographic data and the selection decisions of documents. We used two statistical analysis : a factor analysis of correspondence and a multiple correspondence analysis. Our analysis has shown that, for example, the documents in format "4 or GR FOL" and edited "between 1961 and 1990" in Morocco are more likely to be "Selected". However, the documents in format "16 or 8" and edited "between 1871 and 1800 in English or Spanish have a greater chance to be "Not Selected"

    Création automatique de classes de signatures manuscrites pour l'authentification en ligne

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    International audienceNous nous intéressons dans ce papier à l'optimisation d'un système d'authentification par signature manuscrite. Celui-ci est basé sur une approche Coarse To Fine et utilise l'algorithme Dynamic Time Warping ainsi qu'un seuil de décision global pour accepter ou rejeter un signataire. L'optimisation proposée réside dans l'utilisation d'un algorithme de classification non supervisée afin de déterminer automatiquement des classes de signatures. Pour chacune des classes, un seuil de décision spécifique est établi. Dans ces travaux, nous nous sommes plus particulièrement attaché à étudier l'impact de la classification sur les performance. Les résultats expérimentaux sur la base SVC montrent que l'on peut améliorer les performances en diminuant le taux d'erreur égale de 14,4%. Cependant la sensibilité de la classification est très grande et la notion de classe unique pour un signataire semble trop restrictive

    ADAPT Project Publications Booklet

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    Multi One-Class Incremental SVM for Document Stream Digitization

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    International audienceInside the DIGIDOC project (ANR-10-CORD-0020)-CONTenus et INTeractions (CONTINT), our approach was applied to several scenarios of classification of image streams which can cores ond to real cases in digitization projects. Most of the time, the processing of documents is considered as a well-defined task: the classes (also called concepts) are defined and known before the processing starts. But in real industrial workflows of document processes, it may frequently happen that the concepts can change during the time. In a context of document stream processing, the information and content included in the digitized pages can evolve over the time as well as the judgment of the user on what he wants to do with the resulting classification. The goal of this application is to create a module of learning, for a steam-based document images classification (especially dedicated to a digitization process with a huge volume of data), that adapts different situations for intelligent scanning tasks: adding, extending, contracting, splitting, or merging the classes in on an online mode of streaming data processing

    TRAJECTOGRAPHY ESTIMATION FOR A SMART POWERED WHEELCHAIR ORB-SLAM2 VS RTAB-MAP A PREPRINT

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    International audienceThis work is part of the ADAPT project relating to the implementation of a trajectography functionality that aims to measure the path travelled by a patient during the clinical trials. This system (hardware and software) must be reliable, quickly integrable, low-cost and real-time. Therefore, our choices have been naturally made towards visual SLAM-based solutions coupled with an Intel real-sense consumer sensors. This paper is a comparison of two well-known visual SLAM algorithms in the scientific community, ORB-SLAM2 and RTAB-Map, evaluated in different path configurations. The added value of our work lies in the accurate estimation of the trajectories achieved through the use of a VICON motion capture system

    An online background subtraction algorithm deployed on a NAO humanoid robot based monitoring system

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    In this paper, we design a fast background subtraction algorithm and deploy this algorithm on a monitoring system based on NAO humanoid robot. The proposed algorithm detects a contiguous foreground via a contiguously weighted linear regression (CWLR) model. It consists of a background model and a foreground model. The background model is a regression based low rank model. It seeks a low rank background subspace and represents the background as the linear combination of the basis spanning the subspace. The foreground model promotes the contiguity in the foreground detection. It encourages the foreground to be detected as whole regions rather than separated pixels. We formulate the background and foreground model into a contiguously weighted linear regression problem. This problem can be solved efficiently via an alternating optimization approach which includes continuous and discrete variables. Given an image sequence, we use the first few frames to incrementally initialize the background subspace, and we determine the background and foreground in the following frames in an online scheme using the proposed CWLR model, with the background subspace continuously updated using the detected background information. The proposed algorithm is implemented by Python on a NAO humanoid robot based monitoring system. This system consists of a control station and a Nao robot. The Nao robot acts as a mobile probe. It captures image sequence and sends it to the control station. The control station serves as a control terminal. It sends commands to control the behaviour of Nao robot, and it processes the image data sent by Nao. This system can be used for living environment monitoring and form the basis for many vision-based applications like fall detection and scene understanding. The experimental comparisons with most recent algorithms on both benchmark dataset and NAO captures demonstrate the high effectiveness of the proposed algorithm
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