1,883 research outputs found
A study into annotation ranking metrics in geo-tagged image corpora
Community contributed datasets are becoming increasingly common in automated image annotation systems. One important issue with community image data is that there is no guarantee that the associated metadata is relevant. A method is required that can accurately rank the semantic relevance of community annotations. This should enable the extracting of relevant subsets from potentially noisy collections of these annotations. Having relevant, non heterogeneous tags assigned to images should improve community image retrieval systems, such as Flickr, which are based on text retrieval methods. In the literature, the current state of the art approach to ranking the semantic relevance of Flickr tags is based on the widely used tf-idf metric. In the case of datasets containing landmark images, however, this metric is inefficient due to the high frequency of common landmark tags within the data set and can be improved upon. In this paper, we present a landmark recognition framework, that provides end-to-end automated recognition and annotation. In our study into automated annotation, we evaluate 5 alternate approaches to tf-idf
to rank tag relevance in community contributed landmark image corpora. We carry out a thorough evaluation of each of these ranking metrics and results of this evaluation demonstrate that four of these proposed techniques outperform the current commonly-used tf-idf approach for this task
Visual and geographical data fusion to classify landmarks in geo-tagged images
High level semantic image recognition and classification is a challenging task and currently is a very active research domain. Computers struggle with the high level task of identifying objects and scenes within digital images accurately in unconstrained environments. In this paper, we present experiments that aim to overcome the limitations of computer vision algorithms by combining them with novel contextual based features to describe geo-tagged imagery. We adopt a machine learning based algorithm with the aim of classifying classes of geographical landmarks within digital images. We use community contributed image sets downloaded from Flickr and provide a thorough investigation, the results of which are presented in an evaluation section
Analyzing image-text relations for semantic media adaptation and personalization
Progress in semantic media adaptation and personalisation requires that we know more about how different media types, such as texts and images, work together in multimedia communication. To this end, we present our ongoing investigation into image-text relations. Our idea is that the ways in which the meanings of images and texts relate in multimodal documents, such as web pages, can be classified on the basis of low-level media features and that this classification should be an early processing step in systems targeting semantic multimedia analysis. In this paper we present the first empirical evidence that humans can predict something about the main theme of a text from an accompanying image, and that this prediction can be emulated by a machine via analysis of low- level image features. We close by discussing how these findings could impact on applications for news adaptation and personalisation, and how they may generalise to other kinds of multimodal documents and to applications for semantic media retrieval, browsing, adaptation and creation
Automated annotation of landmark images using community contributed datasets and web resources
A novel solution to the challenge of automatic image annotation is described. Given an image with GPS data of its location of capture, our system returns a semantically-rich annotation comprising tags which both identify the landmark in the image, and provide an interesting fact about it, e.g. "A view of the Eiffel Tower, which was built in 1889 for an international exhibition in Paris". This exploits visual and textual web mining in combination with content-based image
analysis and natural language processing. In the first stage, an input image is matched to a set of community contributed images (with keyword tags) on the basis of its GPS information and image classification techniques. The depicted landmark is inferred from the keyword tags for the matched set. The system then takes advantage of the information written about landmarks available on the web at large to extract a fact about the landmark in the image. We report component evaluation results from an implementation of our solution on a mobile device. Image localisation and matching oers 93.6% classication accuracy; the selection of appropriate tags for use in annotation performs well (F1M of
0.59), and it subsequently automatically identies a correct toponym for use in captioning and fact extraction in 69.0% of the tested cases; finally the fact extraction returns an interesting caption in 78% of cases
Wood properties variation of Eucalyptus saligna Sm. from Angola
No presente trabalho a variação na massa volĂșmica, no comprimento das fibras e nas dimensĂ”es transversais foi estudada para vĂĄrios anĂ©is e alturas no tronco de Eucalyptus saligna Sm. de Angola.
Foi mostrado que embora a massa volĂșmica revele uma tendĂȘncia geral para crescer com a idade do cambio se detectam diferenças nĂŁo sĂł com as estaçÔes mas tambĂ©m com o nĂvel de amostragem no tronco. Quanto ao comprimento da fibra mostrou-se que a relação comprimento da fibra-idade seguia um padrĂŁo tipo jĂĄ descrito para outras espĂ©cies: rĂĄpida taxa de crescimento nos primeiros aneis prĂłximo da medula que se vai lentamente esbatendo atĂ© se atingir um comprimento mĂĄximo. O diĂąmetro da fibra cresce lentamente do centro para a periferia e decresce com a altura sendo esta tendĂȘncia mais pronunciada para o diĂąmetro tangencial. Quanto Ă espessura da parede ela foi mais pronunciada nos aneis mais velhos e nos nĂveis mais elevados.
Apesar da grande variabilidade encontrada de ĂĄrvore para ĂĄrvore os modelos de correlação estudados revelaram que amostragens limitadas ao nĂvel do DAP descreveram com elevado grau de confiança os valores mĂ©dios ponderados da massa volĂșmica, comprimento, diĂąmetro radial e tangencial e a espessura da fibra bem como as percentagens de fibras e parĂȘnquimainfo:eu-repo/semantics/publishedVersio
Efficiency of Higher Order Finite Elements for the Analysis of Seismic Wave Propagation
The analysis of wave propagation problems in linear damped media must take
into account both propagation features and attenuation process. To perform
accurate numerical investigations by the finite differences or finite element
method, one must consider a specific problem known as the numerical dispersion
of waves. Numerical dispersion may increase the numerical error during the
propagation process as the wave velocity (phase and group) depends on the
features of the numerical model. In this paper, the numerical modelling of wave
propagation by the finite element method is thus analyzed and dis-cussed for
linear constitutive laws. Numerical dispersion is analyzed herein through 1D
computations investigating the accuracy of higher order 15-node finite elements
towards numerical dispersion. Concerning the numerical analy-sis of wave
attenuation, a rheological interpretation of the classical Rayleigh assumption
has for instance been previously proposed in this journal
Investigating keyframe selection methods in the novel domain of passively captured visual lifelogs
The SenseCam is a passive capture wearable camera, worn around the neck, and when worn continuously it takes an average of 1,900 images per day. It can be used to create a personal lifelog or visual recording of the wearerâs life which can be helpful as an aid to human memory. For such a large amount of visual information to be useful, it needs to be structured into âeventsâ, which can be achieved through automatic segmentation. An important component of this structuring process is the selection of keyframes to represent individual events. This work investigates a variety of techniques for the selection of a single representative keyframe image from each event, in order to provide the user with an instant visual summary of that event. In our experiments we use a large test set of 2,232 lifelog events collected by 5 users over a time period of one month each. We propose a novel keyframe selection technique which seeks to select the image with the highest âqualityâ as the keyframe. The inclusion of âqualityâ approaches in keyframe selection is demonstrated to be useful owing to the high variability in image visual quality within passively captured image collections
Gravitational waves from self-ordering scalar fields
Gravitational waves were copiously produced in the early Universe whenever
the processes taking place were sufficiently violent. The spectra of several of
these gravitational wave backgrounds on subhorizon scales have been extensively
studied in the literature. In this paper we analyze the shape and amplitude of
the gravitational wave spectrum on scales which are superhorizon at the time of
production. Such gravitational waves are expected from the self ordering of
randomly oriented scalar fields which can be present during a thermal phase
transition or during preheating after hybrid inflation. We find that, if the
gravitational wave source acts only during a small fraction of the Hubble time,
the gravitational wave spectrum at frequencies lower than the expansion rate at
the time of production behaves as with an
amplitude much too small to be observable by gravitational wave observatories
like LIGO, LISA or BBO. On the other hand, if the source is active for a much
longer time, until a given mode which is initially superhorizon (), enters the horizon, for , we find that the gravitational
wave energy density is frequency independent, i.e. scale invariant. Moreover,
its amplitude for a GUT scale scenario turns out to be within the range and
sensitivity of BBO and marginally detectable by LIGO and LISA. This new
gravitational wave background can compete with the one generated during
inflation, and distinguishing both may require extra information.Comment: 21 pages, 2 figures, added discussion about numerical integration and
a new figure to illustrate the scale-invariance of the GW power spectrum,
conclusions unchange
Functional and molecular analysis of proprioceptive sensory neuron excitability in mice
Neurons located in dorsal root ganglia (DRG) are crucial for transmitting peripheral sensations such as proprioception, touch, temperature, and nociception to the spinal cord before propagating these signals to higher brain structures. To date, difficulty in identifying modality-specific DRG neurons has limited our ability to study specific populations in detail. As the calcium-binding protein parvalbumin (PV) is a neurochemical marker for proprioceptive DRG cells we used a transgenic mouse line expressing green fluorescent protein (GFP) in PV positive DRGs, to study the functional and molecular properties of putative proprioceptive neurons. Immunolabeled DRGs showed a 100% overlap between GFP positive (GFP+) and PV positive cells, confirming the PVeGFP mouse accurately labeled PV neurons. Targeted patch-clamp recording from isolated GFP+ and GFP negative (GFPâ) neurons showed the passive membrane properties of the two groups were similar, however, their active properties differed markedly. All GFP+ neurons fired a single spike in response to sustained current injection and their action potentials (APs) had faster rise times, lower thresholds and shorter half widths. A hyperpolarization-activated current (Ih) was observed in all GFP+ neurons but was infrequently noted in the GFPâ population (100% vs. 11%). For GFP+ neurons, Ih activation rates varied markedly, suggesting differences in the underlying hyperpolarization-activated cyclic nucleotide-gated channel (HCN) subunit expression responsible for the current kinetics. Furthermore, quantitative polymerase chain reaction (qPCR) showed the HCN subunits 2, 1, and 4 mRNA (in that order) was more abundant in GFP+ neurons, while HCN 3 was more highly expressed in GFPâ neurons. Likewise, immunolabeling confirmed HCN 1, 2, and 4 protein expression in GFP+ neurons. In summary, certain functional properties of GFP+ and GFPâ cells differ markedly, providing evidence for modality-specific signaling between the two groups. However, the GFP+ DRG population demonstrates considerable internal heterogeneity when hyperpolarization-activated cyclic nucleotide-gated channel (HCN channel) properties and subunit expression are considered. We propose this heterogeneity reflects the existence of different peripheral receptors such as tendon organs, muscle spindles or mechanoreceptors in the putative proprioceptive neuron population
The angular distribution of the reaction
The reaction is very important for low-energy
( MeV) antineutrino experiments. In this paper we calculate
the positron angular distribution, which at low energies is slightly backward.
We show that weak magnetism and recoil corrections have a large effect on the
angular distribution, making it isotropic at about 15 MeV and slightly forward
at higher energies. We also show that the behavior of the cross section and the
angular distribution can be well-understood analytically for MeV by calculating to , where is the nucleon mass. The
correct angular distribution is useful for separating events from other reactions and detector backgrounds, as well as for
possible localization of the source (e.g., a supernova) direction. We comment
on how similar corrections appear for the lepton angular distributions in the
deuteron breakup reactions and . Finally, in the reaction , the
angular distribution of the outgoing neutrons is strongly forward-peaked,
leading to a measurable separation in positron and neutron detection points,
also potentially useful for rejecting backgrounds or locating the source
direction.Comment: 10 pages, including 5 figure
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