1,721,081 research outputs found
TIFS: A Hybrid Scheme Integrating PIFS and Linear Transforms
No full textSince its introduction, fractal theory has been exploited in many research topics such as
image processing, pattern recognition and image coding, because of the intrinsic properties which
make fractals highly suited to tasks such as segmentation, feature extraction and indexing, just to
name a few. Unfortunately, they are based on a strong asymmetric scheme, consequently suffering
from very high coding times. On the other hand, linear transfoms are quite time balanced, allowing
them to be usefully exploited in real-time applications, but they do not provide comparable
performance with respect to the image quality for high bit-rates. Here, different levels of embedding
linear transforms in the fractal coding scheme are investigated. Experimental results have been
organised to point out the contribution of each embedding step to the objective quality of the
decoded image
ES-RU: an entropy based rule to select representative templates in face surveillance
No full textES-RU is a system for video sequence indexing. Video frames are annotated according to the identities of appearing subjects. The system architecture is designed by distributing the different processing steps across dedicated modules. These modules interact with each other to accomplish the final task. Such modularity is also designed to allow a high system flexibility, because it is possible to independently substitute each component with a different one performing the same task using a different method. As an example, face detection is presently performed by Viola–Jones algorithm, but the corresponding module might be substituted by one exploiting neural networks or support vector machines (which are actually more computationally demanding). In detail, ES-RU implements both face location and analysis, and an algorithm to select the most representative templates for the selected identities. The novelty of the algorithm for template analysis and selection relies on the proposed use of the concept of entropy. This concept is the base of most techniques that exploit relative entropy to estimate the degree of uniqueness which is assured by a biometric trait, when processed by a Feature Extraction Technique (FET). In this paper, entropy is introduced as a tool to evaluate the contribution of each sample in guaranteeing a suitable diversification of the templates that make up the gallery of a relevant subject. Video-surveillance activities cause to gather a huge amount of templates to be used for tracking and re-identifying subjects. However, most of these templates are not informative enough to be useful. The aim of our approach is to provide an effective technique to keep only the most “representative” of them, i.e. those that provide a sufficient level of diversification. This allows faster processing (less comparisons) and better results (it is possible to recognize a subject under different conditions). ES-RU was tested on six video clips and on a subset of the SCFace database to assess its performances
Eye movement analysis for human authentication: a critical survey
No full textThis paper addresses the active and dynamic nature of biometrics, in general, and gaze analysis, in particular, including motivation and background. The paper includes a critical survey of existing gaze analysis methods, challenges due to uncontrolled settings and lack of standards, and outlines promising future R&D directions. Criteria for performance evaluation are proposed, and state-of-the art gaze analysis methods are compared on the same database set. Performance improvement would come from richer stimuli including task dependent user profiles, with applications going much beyond identity management to include personalized medical care and rehabilitation, privacy, marketing, and education
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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