1,720,962 research outputs found
Video alignment for phylogenetic analysis
No full textThe possibility of studying multiple objects at once for forensic analysis has paved the way to the development of multimedia phylogeny algorithms. Concerning video phylogeny, a fundamental step at the base of many applications is multiple video alignment. This is, given a pool of near-duplicate video sequences partially overlapping in the temporal domain, find the relative time delay between all of them. As phylogeny methods typically takes into account huge quantities of data, the used alignment algorithms must be computationally efficient. In this paper, we propose a solution for multiple video alignment based on the minimisation of a least-square cost function. The proposed solution can be computed in closed form with reduced computational complexity. Moreover, we propose two possible solutions for refining the estimated alignment based on the removal of outlier measurements
Landmine Detection from GPR Data Using Convolutional Neural Networks
Full text linkThe presence of buried landmines is a serious threat
in many areas around the World. Despite various techniques have
been proposed in the literature to detect and recognize buried
objects, automatic and easy to use systems providing accurate
performance are still under research. Given the incredible results
achieved by deep learning in many detection tasks, in this paper
we propose a pipeline for buried landmine detection based
on convolutional neural networks (CNNs) applied to groundpenetrating
radar (GPR) images. The proposed algorithm is
capable of recognizing whether a B-scan profile obtained from
GPR acquisitions contains traces of buried mines. Validation of
the presented system is carried out on real GPR acquisitions,
albeit system training can be performed simply relying on
synthetically generated data. Results show that it is possible
to reach 95% of detection accuracy without training in real
acquisition of landmine profiles
Hash-based frame selection for video phylogeny
No full textMultimedia phylogeny is a research field that aims at tracing back past history of multimedia documents to discover their ancestral relationships. As an example, it might leverage, with the aid of other side information, forensic analysts to detect who was the first user that published online an illegal content (e.g., child pornography). Although relatively well developed for images, this field is still not fully fledged when it comes to analyzing ancestral and evolutionary relationships among digital videos. Dealing with videos is much more challenging, especially as temporal dimension comes into play. In this vein, one of the pivotal tasks for video phylogeny reconstruction is video synchronization in order to compare temporally coherent near-duplicate frames among pairs of sequences. In this work, we propose an algorithm to efficiently select synchronized frame pairs among videos before calculating their phylogenetic relationships. This approach underpins the video phylogeny reconstruction and leverages the analysis on a reduced subset of frames rather than on the full set, thus decreasing the overall computational time. Experimental results show the effectiveness of the proposed method when temporal transformations are considered (i.e., change of frame rate and temporal clipping at any point in the stream)
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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