A Comparative Study of Fingerprint Image-Quality Estimation Methods

One of the open issues in fingerprint verification is the lack of robustness against image-quality degradation. Poor-quality images result in spurious and missing features, thus degrading the performance of the overall system. Therefore, it is important for a fingerprint recognition system to estimate the quality and validity of the captured fingerprint images. In this work, we review existing approaches for fingerprint image-quality estimation, including the rationale behind the published measures and visual examples showing their behavior under different quality conditions. We have also tested a selection of fingerprint image-quality estimation algorithms. For the experiments, we employ the BioSec multimodal baseline corpus, which includes 19 200 fingerprint images from 200 individuals acquired in two sessions with three different sensors. The behavior of the selected quality measures is compared, showing high correlation between them in most cases. The effect of low-quality samples in the verification performance is also studied for a widely available minutiae-based fingerprint matching system.     

Semantics of Schedules for the Fuzzy Job-Shop Problem

In the sequel, we consider the fuzzy job-shop problem, which is a variation of the job-shop problem where duration of tasks may be uncertain and where due-date constraints are allowed to be flexible. Uncertain durations are modeled using triangular fuzzy numbers, and due-date constraints are fuzzy sets with decreasing membership functions expressing a flexible threshold ldquoless than.rdquo Also, the objective function is built using fuzzy decision-making theory. We propose the use of a genetic algorithm (GA) to find solutions to this problem. Our aim is to provide a semantics for this type of problems and use this semantics in a methodology to analyze, evaluate, and, therefore, compare solutions. Finally, we present the results obtained using the GA and evaluate them using the proposed methodology.     

SEGMENTATION AND RECOGNITION IN VISUAL CHROMATIC SPACES

The discriminatory additional information supplied by chromatic components of an image is useful for segmentation techniques that allow fast and efficient separation of the objects in a scene. We show a method that has two essential processes for separating and recognizing pans of a scene in the UVY visual chromatic space. In the learning process, each part of the scene is analyzed, extracting all its discriminatory chromatic features. For this purpose we use a clustering analysis, which divides the scene in several sections with similar chromatic features. This set of features constitutes the “learning bank.” The recognition process consists of recognizing, using the learning bank, each section obtained in clustering analysis.     

Device-independent Color Correction for Multimedia Applications Using Neural Networks and Abductive Modeling Approaches

Although color has appeal for developers and consumers alike, color reproduction poses a major problem in many computer based applications including multimedia and desktop publishing. The problem arises because of the device-independence of color, and the way each device processes color. Matching the appearance of monitor and print images, and achieving satisfactory results is complex. Not only are there fundamental differences between computer screen (additive) and printers (subtractive), but subtractive color is in general more prone to errors due to dye inadequacies. In order to control the error in porting color, different techniques have been applied. In this paper, the utilization of artificial neural networks as well as abductive modeling approaches to color error reduction are introduced from an RGB (Red Green Blue) color model perspective. Analysis of the results and on-going research issues are discussed.     

Collecting and Analyzing Data from Distributed Control Programs

This paper describes a set of tools that allows a developer to instrument a C/C++ program to log data at run-time and then analyze that data to verify correct behavior. The logging tools provide the developer with a means to log a variety of different data to a variety of different outputs. They also allow for synchronized logging of data from distributed programs. One logging output option is an SQL database. We have developed a set of analysis tools that retrieve data from the database to answer common developer questions. The analysis tools use an interval temporal logic to frame database queries. The data logging tools are fully implemented and performance results are given in this paper. The data analysis tools are currently being tested on data from real NASA applications.     

Heuristics for railway infrastructure saturation

This research concerns the problem of the evaluation of the railway infrastructure capacity. It is an important question when railway authorities have to choose between different infrastructure investment projects. We developped independently two heuristic approaches to solve the infrastructure saturation problem. The first is based on a constraint programming model which is solved using a greedy heuristic. The second approach identifies the saturation problem as a unicost set packing problem and its resolution is ensured by an adaption of GRASP metaheuristic. Currently, both resolution techniques are not in competition. The goal is to grasp the resolution ability of the heuristics and to analyse the kind of solutions produced. The Pierrefitte-Gonesse junction has been used as experimental support. A software environment allows to simulate several timetables involving TGV, Inter City and Freight trains.     

Physically Abusive Mothers' Responses Following Episodes of Child Noncompliance and Compliance.

The authors used sequential analysis to examine specific interaction patterns between physically abusive mothers and their children following episodes of noncompliance and compliance. Fifteen abusive and 15 nonabusive, low-risk mother-child dyads were observed, and their behaviors were coded for specific interactions. The children in the study ranged in age from 2 to 6 years. Results indicated that after noncompliance occurred, physically abusive mothers were more likely than nonabusive mothers to respond negatively and give another command. When child compliance occurred, there were no group differences in the likelihood of praising their children's prosocial behavior, but physically abusive mothers were less likely than nonabusive mothers to engage in other forms of positive behavior (e.g., positive touch) after compliance. Clinical implications are discussed in the context of working with physically abusive families. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Wireless CDN video streaming architecture for IPTV

Wireless IPTV (Internet Protocol TeleVision) aims to make the traditional IPTV and related services available to users anywhere, anytime, on any device, and through any network. Mobile streaming TV is one of the alternatives to deploy such a system; others are DVB-H or MediaFLO. In this paper we propose an alternative CDN-based architecture to distribute contents to different access networks, in order to create a triple screen platform. The core elements of the system are the video streamers, acting as surrogates of the mobile CDN-based architecture, installed in the access networks (including mobile), and the transcoding servers in the premises of the content providers. The paper focused on the wireless part of the system, analyzing the architecture and performance results related with the video coding and the efficiency obtained due to the placement of the streaming servers. The system has been deployed by a Spanish media company for its use with different Telcos, ISPs and media companies.     

Bayesian adaptation for user-dependent multimodal biometric authentication

A novel score-level fusion strategy based on Bayesian adaptation for user-dependent multimodal biometric authentication is presented. In the proposed method, the fusion function is adapted for each user based on prior information extracted from a pool of users. Experimental results are reported using on-line signature and fingerprint verification subsystems on the MCYT real bimodal database. The proposed scheme outperforms both user-independent and user-dependent standard approaches. As compared to non-adapted user-dependent fusion, relative improvements of 80% and 55% are obtained for small and large training set sizes, respectively.