21,145 research outputs found
Video Prioritization for Unequal Error Protection
We analyze the effect of packet losses in video sequences and propose a lightweight Unequal Error Protection strategy which, by choosing which packet is discarded, reduces strongly the Mean Square Error of the received sequenc
A Framework for Evaluating Land Use and Land Cover Classification Using Convolutional Neural Networks
Analyzing land use and land cover (LULC) using remote sensing (RS) imagery is essential
for many environmental and social applications. The increase in availability of RS data has led to the
development of new techniques for digital pattern classification. Very recently, deep learning (DL)
models have emerged as a powerful solution to approach many machine learning (ML) problems.
In particular, convolutional neural networks (CNNs) are currently the state of the art for many image
classification tasks. While there exist several promising proposals on the application of CNNs to
LULC classification, the validation framework proposed for the comparison of different methods
could be improved with the use of a standard validation procedure for ML based on cross-validation
and its subsequent statistical analysis. In this paper, we propose a general CNN, with a fixed
architecture and parametrization, to achieve high accuracy on LULC classification over RS data
from different sources such as radar and hyperspectral. We also present a methodology to perform
a rigorous experimental comparison between our proposed DL method and other ML algorithms
such as support vector machines, random forests, and k-nearest-neighbors. The analysis carried out
demonstrates that the CNN outperforms the rest of techniques, achieving a high level of performance
for all the datasets studied, regardless of their different characteristics.Ministerio de EconomĂa y Competitividad TIN2014-55894-C2-1-RMinisterio de EconomĂa y Competitividad TIN2017-88209-C2-2-
Tracking-Based Non-Parametric Background-Foreground Classification in a Chromaticity-Gradient Space
This work presents a novel background-foreground classification technique based on adaptive non-parametric kernel estimation in a color-gradient space of components. By combining normalized color components with their gradients, shadows are efficiently suppressed from the results, while the luminance information in the moving objects is preserved. Moreover, a fast multi-region iterative tracking strategy applied over previously detected foreground regions allows to construct a robust foreground modeling, which combined with the background model increases noticeably the quality in the detections. The proposed strategy has been applied to different kind of sequences, obtaining satisfactory results in complex situations such as those given by dynamic backgrounds, illumination changes, shadows and multiple moving objects
Real-time shot detection based on motion analysis and multiple low-level techniques
To index, search, browse and retrieve relevant material, indexes describing the video content are required. Here, a new and fast strategy which allows detecting abrupt and gradual transitions is proposed. A pixel-based analysis is applied to detect abrupt transitions and, in parallel, an edge-based analysis is used to detect gradual transitions. Both analysis are reinforced with a motion analysis in a second step, which significantly simplifies the threshold selection problem while preserving the computational requirements. The main advantage of the proposed system is its ability to work in real time and the experimental results show high recall and precision values
Towards Noncommutative Linking Numbers Via the Seiberg-Witten Map
In the present work some geometric and topological implications of
noncommutative Wilson loops are explored via the Seiberg-Witten map. In the
abelian Chern-Simons theory on a three dimensional manifold, it is shown that
the effect of noncommutativity is the appearance of new knots at the
-th order of the Seiberg-Witten expansion. These knots are trivial homology
cycles which are Poincar\'e dual to the high-order Seiberg-Witten potentials.
Moreover the linking number of a standard 1-cycle with the Poincar\'e dual of
the gauge field is shown to be written as an expansion of the linking number of
this 1-cycle with the Poincar\'e dual of the Seiberg-Witten gauge fields. In
the process we explicitly compute the noncommutative 'Jones-Witten' invariants
up to first order in the noncommutative parameter. Finally in order to exhibit
a physical example, we apply these ideas explicitly to the Aharonov-Bohm
effect. It is explicitly displayed at first order in the noncommutative
parameter, we also show the relation to the noncommutative Landau levels.Comment: 19 pages, 1 figur
Conditional t-SNE: Complementary t-SNE embeddings through factoring out prior information
Dimensionality reduction and manifold learning methods such as t-Distributed
Stochastic Neighbor Embedding (t-SNE) are routinely used to map
high-dimensional data into a 2-dimensional space to visualize and explore the
data. However, two dimensions are typically insufficient to capture all
structure in the data, the salient structure is often already known, and it is
not obvious how to extract the remaining information in a similarly effective
manner. To fill this gap, we introduce \emph{conditional t-SNE} (ct-SNE), a
generalization of t-SNE that discounts prior information from the embedding in
the form of labels. To achieve this, we propose a conditioned version of the
t-SNE objective, obtaining a single, integrated, and elegant method. ct-SNE has
one extra parameter over t-SNE; we investigate its effects and show how to
efficiently optimize the objective. Factoring out prior knowledge allows
complementary structure to be captured in the embedding, providing new
insights. Qualitative and quantitative empirical results on synthetic and
(large) real data show ct-SNE is effective and achieves its goal
Phonemic errors with words but semantic errors with numbers: is number production special?
Paradoxically, brain-damaged people with impairments
in the phonological output buffer produce phonemic
paraphasias with content words (e.g., bitar-butter) but
semantic paraphasias with number words (e.g., twenty
five-thirty eight). This is known as the Stimulus Type
Effect on Phonological and Semantic errors (STEPS).
Explanations for this phenomenon consider that preassembled
phonological representations exist for
numbers but not for content words in the phonological
output buffer. Here we explore two alternative
hypotheses based on the existence of two
methodological confounds: numbers are always
presented in homogeneous blocks and words in
heterogeneous blocks; number words are usually word
sequences that are compared to single content-words.
Two conduction aphasics took part in the study.
Experiment 1 did not confirm the role of lists in causing
the STEPS. Experiment 2 found more semantic
paraphasias (compared to phonemic paraphasias) both in
the repetition of multidigits (e.g., 673) and, more
importantly, in the repetition of color word sequences
(e.g., red-blue-green). The STEPS arises as consequence
of differences in resource demands. Number words have
not a special status in the phonological output buffer.Universidad de MĂĄlaga. Campus de Excelencia Internacional AndalucĂa Tech
Kernel bandwidth estimation for moving object detection in non-stabilized cameras
The evolution of the television market is led by 3DTV technology, and this tendency can accelerate during the next years according to expert forecasts. However, 3DTV delivery by broadcast networks is not currently developed enough, and acts as a bottleneck for the complete deployment of the technology. Thus, increasing interest is dedicated to ste-reo 3DTV formats compatible with current HDTV video equipment and infrastructure, as they may greatly encourage 3D acceptance. In this paper, different subsampling schemes for HDTV compatible transmission of both progressive and interlaced stereo 3DTV are studied and compared. The frequency characteristics and preserved frequency content of each scheme are analyzed, and a simple interpolation filter is specially designed. Finally, the advantages and disadvantages of the different schemes and filters are evaluated through quality testing on several progressive and interlaced video sequences
Extraordinary absorption of decorated undoped graphene
We theoretically study absorption by an undoped graphene layer decorated with
arrays of small particles. We discuss periodic and random arrays within a
common formalism, which predicts a maximum absorption of for suspended
graphene in both cases. The limits of weak and strong scatterers are
investigated and an unusual dependence on particle-graphene separation is found
and explained in terms of the effective number of contributing evanescent
diffraction orders of the array. Our results can be important to boost
absorption by single layer graphene due to its simple setup with potential
applications to light harvesting and photodetection based on energy (F\"orster)
rather than charge transfer.Comment: 5 pages, 3 figure
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