20,344 research outputs found
Foreground detection enhancement using Pearson correlation filtering
Foreground detection algorithms are commonly employed as an initial module in video processing pipelines for automated surveillance. The resulting masks produced by these algorithms are usually postprocessed in order to improve their quality. In this work, a postprocessing filter based on the Pearson correlation among the pixels in a neighborhood of the pixel at hand is proposed. The flow of information among pixels is controlled by the correlation that exists among them. This way, the filtering performance is enhanced with respect to some state of the art proposals, as demonstrated with a selection of benchmark videos.Universidad de Málaga. Campus de Excelencia Internacional AndalucÃa Tech
Foreground Detection in Camouflaged Scenes
Foreground detection has been widely studied for decades due to its
importance in many practical applications. Most of the existing methods assume
foreground and background show visually distinct characteristics and thus the
foreground can be detected once a good background model is obtained. However,
there are many situations where this is not the case. Of particular interest in
video surveillance is the camouflage case. For example, an active attacker
camouflages by intentionally wearing clothes that are visually similar to the
background. In such cases, even given a decent background model, it is not
trivial to detect foreground objects. This paper proposes a texture guided
weighted voting (TGWV) method which can efficiently detect foreground objects
in camouflaged scenes. The proposed method employs the stationary wavelet
transform to decompose the image into frequency bands. We show that the small
and hardly noticeable differences between foreground and background in the
image domain can be effectively captured in certain wavelet frequency bands. To
make the final foreground decision, a weighted voting scheme is developed based
on intensity and texture of all the wavelet bands with weights carefully
designed. Experimental results demonstrate that the proposed method achieves
superior performance compared to the current state-of-the-art results.Comment: IEEE International Conference on Image Processing, 201
Full Reference Objective Quality Assessment for Reconstructed Background Images
With an increased interest in applications that require a clean background
image, such as video surveillance, object tracking, street view imaging and
location-based services on web-based maps, multiple algorithms have been
developed to reconstruct a background image from cluttered scenes.
Traditionally, statistical measures and existing image quality techniques have
been applied for evaluating the quality of the reconstructed background images.
Though these quality assessment methods have been widely used in the past,
their performance in evaluating the perceived quality of the reconstructed
background image has not been verified. In this work, we discuss the
shortcomings in existing metrics and propose a full reference Reconstructed
Background image Quality Index (RBQI) that combines color and structural
information at multiple scales using a probability summation model to predict
the perceived quality in the reconstructed background image given a reference
image. To compare the performance of the proposed quality index with existing
image quality assessment measures, we construct two different datasets
consisting of reconstructed background images and corresponding subjective
scores. The quality assessment measures are evaluated by correlating their
objective scores with human subjective ratings. The correlation results show
that the proposed RBQI outperforms all the existing approaches. Additionally,
the constructed datasets and the corresponding subjective scores provide a
benchmark to evaluate the performance of future metrics that are developed to
evaluate the perceived quality of reconstructed background images.Comment: Associated source code: https://github.com/ashrotre/RBQI, Associated
Database:
https://drive.google.com/drive/folders/1bg8YRPIBcxpKIF9BIPisULPBPcA5x-Bk?usp=sharing
(Email for permissions at: ashrotreasuedu
Constraining the epoch of reionization with the variance statistic: simulations of the LOFAR case
Several experiments are underway to detect the cosmic redshifted 21-cm signal
from neutral hydrogen from the Epoch of Reionization (EoR). Due to their very
low signal-to-noise ratio, these observations aim for a statistical detection
of the signal by measuring its power spectrum. We investigate the extraction of
the variance of the signal as a first step towards detecting and constraining
the global history of the EoR. Signal variance is the integral of the signal's
power spectrum, and it is expected to be measured with a high significance. We
demonstrate this through results from a simulation and parameter estimation
pipeline developed for the Low Frequency Array (LOFAR)-EoR experiment. We show
that LOFAR should be able to detect the EoR in 600 hours of integration using
the variance statistic. Additionally, the redshift () and duration
() of reionization can be constrained assuming a parametrization. We
use an EoR simulation of and to test the
pipeline. We are able to detect the simulated signal with a significance of 4
standard deviations and extract the EoR parameters as and in 600 hours,
assuming that systematic errors can be adequately controlled. We further show
that the significance of detection and constraints on EoR parameters can be
improved by measuring the cross-variance of the signal by cross-correlating
consecutive redshift bins.Comment: 13 pages, 14 figures, Accepted for publication in MNRA
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