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CReSTIC (Centre de Recherche en Sciences et Technologies de l’Information et la Communication) , University of Reims Champagne-Ardenne

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7 Papers

A FUZZY SCHEME FOR IMAGE NOISE REDUCTION

Topic: Fuzzy Image, Speech and Signal Processing, Vision and Multimedia

Authors: Philippe Vautrot ¹ , Michel Herbin ¹ and Laurent Hussenet ²

Affiliations: ¹ CReSTIC EA 3804 and University of Reims Champagne Ardenne, France , ² IUT RCC, France

Digital Object Identifier: 10.5220/0003671604410445

Proceedings of FCTA 2011/IJCCI, ISBN: 978-989-8425-83-6

Quick Abstract

The improvement of acquisition devices increases the need for processing of multicomponent images. In this context, the noise reduction is a preliminary preprocessing step affecting the results of the other image operations. This paper proposes a framework explaining usual noise reduction methods by the means of two fuzzy logic techniques: first a pixel fuzzification and second a defuzzification for estimating the filtered values. A new density-based filter is built for removing both impulse noise and Gaussian noise. The filter we propose is robust against outliers and it improves the classical bilateral approach for noise reduction of multicomponent images.

Related Ontology Subjects/Areas/Topics (5)

Artificial Intelligence		Fuzzy Image, Speech and Signal Processing, Vision and Multimedia		Soft Computing
Computational Intelligence		Fuzzy Systems

COMPARATIVE STUDY OF BLIND SOURCE SEPARATION METHODS FOR RAMAN SPECTRA - Application on Numerical Dewaxing of Cutaneous Biopsies

Topic: Medical Signal Acquisition, Analysis and Processing

Authors: Valeriu Vrabie ¹ , Cyril Gobinet ² , Michel Herbin ¹ and Michel Manfait ²

Affiliations: ¹ CReSTIC, Université de Reims Champagne-Ardenne, France , ² MéDIAN, CNRS UMR 6142, Université de Reims Champagne-Ardenne, France

Digital Object Identifier: 10.5220/0001064903490354

Proceedings of BIOSIGNALS 2008/BIOSTEC, ISBN: 978-989-8111-18-0

Quick Abstract

Raman spectroscopy is a powerful tool for the study of molecular composition of biological samples. Digital processing techniques are needed to separate the wealthy but complex information recorded by Raman spectra. Blind source separation methods can be used to efficiently extract the spectra of chemical constituents. We propose in this study to analyze the performances of four blind source separation methods. Two Independent Component Analysis methods using the JADE and FastICA algorithms are based uniquely on the independence of the spectra. The Non-Negative Matrix Factorization takes into account only the positivity of underlying spectra and mixing coefficients. The Maximum Likelihood Positive Source Separation assumes both the independence and positivity of the spectra. A realistic simulated dataset allows a quantitative study of these methods while a real dataset recorded on a paraffin-embedded skin biopsy provides a qualitative study.

Related Ontology Subjects/Areas/Topics (5)

Applications and Services		Biomedical Signal Processing		Medical Image Detection, Acquisition, Analysis and Processing
Biomedical Engineering		Computer Vision, Visualization and Computer Graphics

SPEAKER RECOGNITION USING DECISION FUSION

Topics: Biometrics; Pattern Recognition

Authors: M. Chenafa ¹ , D. Istrate ¹ , V. Vrabie ² and M. Herbin ²

Affiliations: ¹ RMSE, ESIGETEL, France , ² CReSTIC, Université de Reims Champagne-Ardenne, France

Digital Object Identifier: 10.5220/0001065502670272

Proceedings of BIOSIGNALS 2008/BIOSTEC, ISBN: 978-989-8111-18-0

Quick Abstract

Biometrics systems have gained in popularity for the automatic identification of persons. The use of the voice as a biometric characteristic offers advantages such as: is well accepted, it works with regular microphones, the hardware costs are reduced, etc. However, the performance of a voice-based biometric system easily degrades in the presence of a mismatch between training and testing conditions due to different factors. This paper presents a new speaker recognition system based on decision fusion. The fusion is based on two identification systems: a speaker identification system (text-independent) and a keywords identification system (speaker-independent). These systems calculate the likelihood ratios between the model of a test signal and the different models of the database. The fusion uses these results to identify the couple speaker/password corresponding to the test signal. A verification system is then applied on a second test signal in order to confirm or infirm the identification. The fusion step improves the false rejection rate (FRR) from 21, 43% to 7, 14% but increase also the false acceptation rate (FAR) from 21, 43% to 28, 57%. The verification step makes however a significant improvement on the FAR (from 28, 57% to 14.28%) while it keeps constant the FRR (to 7, 14%).

Related Ontology Subjects/Areas/Topics (19)

Applications	Health Engineering and Technology Applications	Neurotechnology, Electronics and Informatics
Artificial Intelligence	Human-Computer Interaction	Pattern Recognition
Biomedical Engineering	Methodologies and Methods	Physiological Computing Systems
Biomedical Signal Processing	Multimedia	Sensor Networks
Biometrics	Multimedia Signal Processing	Soft Computing
Biometrics and Pattern Recognition	Neurocomputing	Telecommunications
Data Manipulation

IMAGE RETRIEVAL WITH BINARY HAMMING DISTANCE

Topic: Object, Event and Scene Recognition, Retrieval and Indexing

Authors: Jérôme Landré ¹ and Frédéric Truchetet ²

Affiliations: ¹ CReSTIC, Université de Reims-Champagne-Ardenne, France , ² Le2i, Université de Bourgogne, France

Digital Object Identifier: 10.5220/0002046002370240

Proceedings of VISAPP 2007/VISIGRAPP, ISBN: 978-972-8865-74-0

Quick Abstract

This article proposes a content-based indexing and retrieval (CBIR) system based on query-by-visual-example using hierarchical binary signatures. Binary signatures are obtained through a described binarization process of classical features (color, texture and shape). The Hamming binary distance (based on binary XOR operation) is used for computing distances. This technique was tested on a real natural image collection containing 10 000 images and on a virtual collection of one million images. Results are very good both in terms of speed and accuracy allowing near real-time image retrieval in very large image collections.

ROBUST ADAPTIVE WAVELET NEURAL NETWORK TO CONTROL A CLASS OF NONLINEAR SYSTEMS

Topic: Neural Networks Based Control Systems

Authors: A. Hussain ¹ , N. Essounbouli ¹ , A. Hamzaoui ¹ and J. Zaytoon ²

Affiliations: ¹ CReSTIC, IUT de Troyes, France , ² CReSTIC, Uiversité de Reims Champagne-Ardenne, France

Digital Object Identifier: 10.5220/0001640400600067

Proceedings of ICINCO 2007, ISBN: 978-972-8865-82-5

Quick Abstract

This paper deals with the synthesis of a Wavelet Neural Network adaptive controller for a class of second order systems. Due to its fast convergence, the wavelet neural network is used to approximate the unknown system dynamics. The proposed approximator will be on-line adjusted according to the adaptation laws deduced from the stability analysis. To ensure the robustness of the closed loop system, a modified sliding mode control signal is used. In this work, variable sliding surface is considered to reduce the starting energy without deteriorating the tracking performances. Furthermore, the knowledge of the upper bounds of both the external disturbances and the approximation errors is not needed. The global stability of the closed loop system is guaranteed in the sense of Lyapunov. Finally, a simulation example is presented to illustrate the efficiency of the developed approach.

Related Ontology Subjects/Areas/Topics (3)

Informatics in Control, Automation and Robotics

Intelligent Control Systems and Optimization

Neural Networks Based Control Systems

A New RBF Classifier for Buried Tag Recognition

Authors: Larbi Beheim , Adel Zitouni and Fabien Belloir

Affiliation: CReSTIC (Centre de Recherche en Sciences et Technologies de l’Information et la Communication) , University of Reims Champagne-Ardenne, France

Digital Object Identifier: 10.5220/0002562800220032

Proceedings of PRIS 2005/ICEIS, ISBN: 972-8865-28-7

Quick Abstract

This article presents noticeable performances improvement of an RBF neural classifier. Based on the Mahalanobis distance, this new classifier increases relatively the recognition rate while decreasing remarkably the number of hidden layer neurons. We obtain thus a new very general RBF classifier, very simple, not requiring any adjustment parameter, and presenting an excellent ratio performances/neurons number. A comparative study of its performances is presented and illustrated by examples on real databases. We present also the recognition improvements obtained by applying this new classifier on buried tag.

Independent Component Analysis and Raman Microspectroscopy on Paraffinised Non Dewaxed Cutaneous Biopsies: A promising Methodology for Melanoma Early Diagnosis

Authors: Cyril Gobinet ¹ , Ali Tfayli ² , Olivier Piot ² , Valeriu Vrabie ¹ and Régis Huez ¹

Affiliations: ¹ CReSTIC, URCA, France , ² Unité MéDIAN, CNRS UMR 6142, URCA, France

Digital Object Identifier: 10.5220/0001195500190026

Proceedings of BPC 2005/ICINCO, ISBN: 972-8865-35-X

Quick Abstract

This paper deals with a promising methodology for melanoma early diagnosis. Raman spectroscopy is used to record vibrational information of paraffinised tumoral tissues. Independent Component Analysis (ICA) is performed on Raman spectra to numerically deparaffinise spectra. Resulting deparaffinised spectra are used to extract discriminant information specific to malignant and benign tumors. These spectral specificities can be employed as molecular descriptors of the type of pathology. A comparison with Principal Component Analysis (PCA) shows that ICA is more suited to process this kind of problem.