15,465 research outputs found
Spotlight : Face Recognition Improves Security
Facial recognition technology has been widely used by the military for identity confirmation and surveillance. It is a unique biometric system because there is no contact necessary to gather images, unlike fingerprinting. Facial recognition, which is also used as a security feature on smartphones and computers, can be improved to more accurately identify a person based on their facial features. Researchers from the University of York FaceVar Lab are working on ways to improve facial recognition as a security feature that would also translate to improvements for military applications
Towards an All-Purpose Content-Based Multimedia Information Retrieval System
The growth of multimedia collections - in terms of size, heterogeneity, and
variety of media types - necessitates systems that are able to conjointly deal
with several forms of media, especially when it comes to searching for
particular objects. However, existing retrieval systems are organized in silos
and treat different media types separately. As a consequence, retrieval across
media types is either not supported at all or subject to major limitations. In
this paper, we present vitrivr, a content-based multimedia information
retrieval stack. As opposed to the keyword search approach implemented by most
media management systems, vitrivr makes direct use of the object's content to
facilitate different types of similarity search, such as Query-by-Example or
Query-by-Sketch, for and, most importantly, across different media types -
namely, images, audio, videos, and 3D models. Furthermore, we introduce a new
web-based user interface that enables easy-to-use, multimodal retrieval from
and browsing in mixed media collections. The effectiveness of vitrivr is shown
on the basis of a user study that involves different query and media types. To
the best of our knowledge, the full vitrivr stack is unique in that it is the
first multimedia retrieval system that seamlessly integrates support for four
different types of media. As such, it paves the way towards an all-purpose,
content-based multimedia information retrieval system
Control What You Include! Server-Side Protection against Third Party Web Tracking
Third party tracking is the practice by which third parties recognize users
accross different websites as they browse the web. Recent studies show that 90%
of websites contain third party content that is tracking its users across the
web. Website developers often need to include third party content in order to
provide basic functionality. However, when a developer includes a third party
content, she cannot know whether the third party contains tracking mechanisms.
If a website developer wants to protect her users from being tracked, the only
solution is to exclude any third-party content, thus trading functionality for
privacy. We describe and implement a privacy-preserving web architecture that
gives website developers a control over third party tracking: developers are
able to include functionally useful third party content, the same time ensuring
that the end users are not tracked by the third parties
Application of Time-Fractional Order Bloch Equation in Magnetic Resonance Fingerprinting
Magnetic resonance fingerprinting (MRF) is one novel fast quantitative
imaging framework for simultaneous quantification of multiple parameters with
pseudo-randomized acquisition patterns. The accuracy of the resulting
multi-parameters is very important for clinical applications. In this paper, we
derived signal evolutions from the anomalous relaxation using a fractional
calculus. More specifically, we utilized time-fractional order extension of the
Bloch equations to generate dictionary to provide more complex system
descriptions for MRF applications. The representative results of phantom
experiments demonstrated the good accuracy performance when applying the
time-fractional order Bloch equations to generate dictionary entries in the MRF
framework. The utility of the proposed method is also validated by in-vivo
study.Comment: Accepted at 2019 IEEE 16th International Symposium on Biomedical
Imaging (ISBI 2019
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