Expanding the toolbox for nanoparticle trapping and spectroscopy with holographic optical tweezers

We have developed a workstation based on holographic tweezers to optically trap, move and characterize metal nanoparticles. Our advanced darkfield imaging system allows us to simultaneously image and take spectra of single trapped metal nanoparticles. We take advantage of the beamshaping abilities of the spatial light modulator and correct for aberrations of the trapping optics. We monitor the improvement of the optical trap with video-based nanoparticle tracking. Furthermore we theoretically assess the capabilities and limitations of video-based tracking for nanoparticle position detection, in particular with respect to acquisition frequencies below the corner frequency

Text Driven Recognition of Multiple Faces in Newspapers

Face recognition is still a hard task when performed on newspaper images, since they often show faces in non-frontal poses, prohibitive lighting conditions, and too poor quality in terms of resolution. In these cases, combining textual information derived from the page articles with visual information proves to be advantageous for improving the recognition performance. In this work, we extract characters’ names from articles and captions to restrict facial recognition to a limited set of candidates. To solve the difficulties derived from having multiple faces in the same image, we also propose a solution that enables a joint assignment of faces to characters’ names. Extensive tests in both ideal and real scenarios confirm the soundness of the proposed approach

Extended Successive Elimination Algorithm for Fast Optimal Block Matching Motion Estimation

In this paper, we propose an extended successive elimination algorithm (SEA) for fast optimal block matching motion estimation (ME). By reinterpreting the typical sum of absolute differences measure, we can obtain additional decision criteria whether to discard the impossible candidate motion vectors. Experimental results show that the proposed algorithm reduces the computational complexity up to 19.85% on average comparing with the multilevel successive elimination algorithm. The proposed algorithm can be used with other SEA to improve the ME performance

Multi-Connected Ontologies

Ontologies have been used for the purpose of bringing system and consistency to subject and knowledge areas. We present a criticism of the present mathematical structure of ontologies and indicate that they are not sufficient in their present form to represent the many different valid expressions of a subject knowledge domain. We propose an alternative structure for ontologies based on a richer multi connected complex network which contains the present ontology structure as a projection. We demonstrate how this new multi connected ontology should be represented as an asymmetric probability matrix.Comment: 8 pages, 13 figures, submitted to IARIA MMEDIA2012 Conference, Chamonix, Franc

Adaptive Multi-Class Audio Classification in Noisy In-Vehicle Environment

With ever-increasing number of car-mounted electric devices and their complexity, audio classification is increasingly important for the automotive industry as a fundamental tool for human-device interactions. Existing approaches for audio classification, however, fall short as the unique and dynamic audio characteristics of in-vehicle environments are not appropriately taken into account. In this paper, we develop an audio classification system that classifies an audio stream into music, speech, speech+music, and noise, adaptably depending on driving environments including highway, local road, crowded city, and stopped vehicle. More than 420 minutes of audio data including various genres of music, speech, speech+music, and noise are collected from diverse driving environments. The results demonstrate that the proposed approach improves the average classification accuracy up to 166%, and 64% for speech, and speech+music, respectively, compared with a non-adaptive approach in our experimental settings

The DCP Bay: Toward an Art-House Content Delivery Network for Digital Cinema

International audienceCinema theaters have arrived in the digital era. The Digital Cinema Initiatives has chosen Digital Cinema Package(DCP) as format for the distribution of feature films. No suitable economical nor technological model is proposed for DCP content delivery to art-house theaters. The existing solutions are too expensive or not adapted. Therefore, we conduct this research activity in cooperation with Utopia cinemas, a group of art-house French cinemas. Utopia’s main requirement (besides functional ones) is to provide free and open source software for DCP distribution. In this paper, we present a Content Delivery Network for DCP adapted to art-house. This network is operative since mid 2014 and based on torrent peer-to-peer technology inside a multi-point VPN

Video Watermarking Based on Interactive Detection of Feature Regions

International audienceVideo watermarking is very important in many areas of activity and especially in multimedia applications. Therefore, security of video stream has recently become a major concern and has attracted more and more attention in both the research and industrial domains. In this perspective, several video watermarking approaches are proposed but, based on our knowledge, there is no method which verified the compromise between invisibility and robustness against all usual attacks. In our previous work, we proposed a new video watermarking approach based on feature region generated from mosaic frame and multi-frequential embedding. This approach allowed obtaining a good invisibility and robustness against the maximum of usual attacks. In our future work, we propose to optimize the choice of the region of interest by using crowdsourcing technique. This last one is an emerging field of knowledge management that involves analyzing the behavior of users when the

QoE for Mobile Streaming

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