Performance Evaluation of User Independent Score Normalization Based Quadratic Function in Multimodal Biometric

Normalization is an essential step in multimodal biometric system that involves various nature and scale of outputs from different modalities before employing any fusion techniques. This paper proposes score normalization technique based on mapping function to increase the separation of score at overlap region and reduce the effect of overlap region on fusion algorithm. The effect of the proposed normalization technique on recognition system performance for different fusion methods is examined. Experiments on three different NIST databases suggest that integrating the proposed normalization technique with the classical simple rule fusion strategies (sum, min and max) and SVM-based fusion results significant improvement compared to other baseline normalization techniques used in this work

WebMonitoring software system: Finite state machines for monitoring the web

This paper presents a software system called WebMonitoring. The system is designed for solving certain problems in the process of information search on the web. The first problem is improving entering of queries at search engines and enabling more complex searches than keyword-based ones. The second problem is providing access to web page content that is inaccessible by common search engines due to search engine’s crawling limitations or time difference between the moment a web page is set up on the Internet and the moment the crawler finds it. The architecture of the WebMonitoring system relies upon finite state machines and the concept of monitoring the web. We present the system’s architecture and usage. Some modules were originally developed for the purpose of the WebMonitoring system, and some rely on UNITEX, linguistically oriented software system. We hereby evaluate the WebMonitoring system and give directions for further development

Visual computing techniques for automated LIDAR annotation with application to intelligent transport systems

106 p.The concept of Intelligent Transport Systems (ITS) refers to the application of communication and information technologies to transport with the aim of making it more efficient, sustainable, and safer. Computer vision is increasingly being used for ITS applications, such as infrastructure management or advanced driver-assistance systems. The latest progress in computer vision, thanks to the Deep Learning techniques, and the race for autonomous vehicle, have created a growing requirement for annotated data in the automotive industry. The data to be annotated is composed by images captured by the cameras of the vehicles and LIDAR data in the form of point clouds. LIDAR sensors are used for tasks such as object detection and localization. The capacity of LIDAR sensors to identify objects at long distances and to provide estimations of their distance make them very appealing sensors for autonomous driving.This thesis presents a method to automate the annotation of lane markings with LIDAR data. The state of the art of lane markings detection based on LIDAR data is reviewed and a novel method is presented. The precision of the method is evaluated against manually annotated data. Its usefulness is also evaluated, measuring the reduction of the required time to annotate new data thanks to the automatically generated pre-annotations. Finally, the conclusions of this thesis and possible future research lines are presented

Visual computing techniques for automated LIDAR annotation with application to intelligent transport systems

106 p.The concept of Intelligent Transport Systems (ITS) refers to the application of communication and information technologies to transport with the aim of making it more efficient, sustainable, and safer. Computer vision is increasingly being used for ITS applications, such as infrastructure management or advanced driver-assistance systems. The latest progress in computer vision, thanks to the Deep Learning techniques, and the race for autonomous vehicle, have created a growing requirement for annotated data in the automotive industry. The data to be annotated is composed by images captured by the cameras of the vehicles and LIDAR data in the form of point clouds. LIDAR sensors are used for tasks such as object detection and localization. The capacity of LIDAR sensors to identify objects at long distances and to provide estimations of their distance make them very appealing sensors for autonomous driving.This thesis presents a method to automate the annotation of lane markings with LIDAR data. The state of the art of lane markings detection based on LIDAR data is reviewed and a novel method is presented. The precision of the method is evaluated against manually annotated data. Its usefulness is also evaluated, measuring the reduction of the required time to annotate new data thanks to the automatically generated pre-annotations. Finally, the conclusions of this thesis and possible future research lines are presented

UNE APPROCHE MULTIMODALE POUR LA VERIFICATION BIOMETRIQUE

Aujourd’hui, la biométrie est un domaine de recherche en pleine expansion, plusieurs systèmes d’identification et de vérification sont à présent développés, cependant leurs performances restent insuffisantes face aux besoins accrus de plus de sécurité. L’utilisation d’une seule modalité biométrique diminue, dans la plupart des cas, la fiabilité de ces systèmes, ce qui nous a incités à combiner plusieurs modalités. Dans cet article, nous prposons une approche de fusion multibiométrique pour la vérification de l’identité. En effet, nous utilisons deux types de biométries : l’empreinte digitale et la signature. Notre approche d’intégration de ces modalités se base sur l’utilisation des séparateurs à vaste marge (SVM), cette fusion est réalisée au niveau des scores générés à partir d’une combinaison de classifieurs neuronaux de type perceptrons multicouches. La décision est prise, par la suite, selon le score émis par le classifieur SVM. Nous avons ainsi conçu et réalisé un système multibiométrique par la fusion des deux modalités, cette fusion a permis d’améliorer significativement les performances de vérification. Les résultats obtenus confirment la supériorité de la multibiométrie par rapport aux systèmes biométriques unimodaux

Exploring Invariant Hybrid Color Image Features for Face Recognition Under Illumination Variation

In this thesis, a novel analysis framework is presented in order to automate testing response of an image-feature descriptor algorithm for face recognition under different illumination conditions and white balance calibration over intra- and inter-color space. The experimental results on the OPFD database show that our analysis framework finds the least sensitive channel of a color space for recognizing a face under unknown illumination, unknown white balance, and the both unknown illumination and white balance conditions. The results also show the combination of channels in a color space which are best suited face recognition

An Adequate Approach to Image Retrieval Based on Local Level Feature Extraction

ABSTRAC