9,087 research outputs found
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
Spread spectrum-based video watermarking algorithms for copyright protection
Merged with duplicate record 10026.1/2263 on 14.03.2017 by CS (TIS)Digital technologies know an unprecedented expansion in the last years. The consumer can
now benefit from hardware and software which was considered state-of-the-art several years
ago. The advantages offered by the digital technologies are major but the same digital
technology opens the door for unlimited piracy. Copying an analogue VCR tape was certainly
possible and relatively easy, in spite of various forms of protection, but due to the analogue
environment, the subsequent copies had an inherent loss in quality. This was a natural way of
limiting the multiple copying of a video material. With digital technology, this barrier
disappears, being possible to make as many copies as desired, without any loss in quality
whatsoever. Digital watermarking is one of the best available tools for fighting this threat.
The aim of the present work was to develop a digital watermarking system compliant with the
recommendations drawn by the EBU, for video broadcast monitoring. Since the watermark
can be inserted in either spatial domain or transform domain, this aspect was investigated and
led to the conclusion that wavelet transform is one of the best solutions available. Since
watermarking is not an easy task, especially considering the robustness under various attacks
several techniques were employed in order to increase the capacity/robustness of the system:
spread-spectrum and modulation techniques to cast the watermark, powerful error correction
to protect the mark, human visual models to insert a robust mark and to ensure its invisibility.
The combination of these methods led to a major improvement, but yet the system wasn't
robust to several important geometrical attacks. In order to achieve this last milestone, the
system uses two distinct watermarks: a spatial domain reference watermark and the main
watermark embedded in the wavelet domain. By using this reference watermark and techniques
specific to image registration, the system is able to determine the parameters of the attack and
revert it. Once the attack was reverted, the main watermark is recovered. The final result is a
high capacity, blind DWr-based video watermarking system, robust to a wide range of attacks.BBC Research & Developmen
Historical forest biomass dynamics modelled with Landsat spectral trajectories
Acknowledgements National Forest Inventory data are available online, provided by Ministerio de Agricultura, Alimentación y Medio Ambiente (España). Landsat images are available online, provided by the USGS.Peer reviewedPostprin
Substructure recovery by 3D Discrete Wavelet Transforms
We present and discuss a method to identify substructures in combined
angular-redshift samples of galaxies within Clusters. The method relies on the
use of Discrete Wavelet Transform (hereafter DWT) and has already been applied
to the analysis of the Coma cluster (Gambera et al. 1997). The main new
ingredient of our method with respect to previous studies lies in the fact that
we make use of a 3D data set rather than a 2D. We test the method on mock
cluster catalogs with spatially localized substructures and on a N-body
simulation. Our main conclusion is that our method is able to identify the
existing substructures provided that: a) the subclumps are detached in part or
all of the phase space, b) one has a statistically significant number of
redshifts, increasing as the distance decreases due to redshift distortions; c)
one knows {\it a priori} the scale on which substructures are to be expected.
We have found that to allow an accurate recovery we must have both a
significant number of galaxies ( for clusters at z or
about 800 at z 0.4) and a limiting magnitude for completeness .
The only true limitation to our method seems to be the necessity of knowing
{\it a priori} the scale on which the substructure is to be found. This is an
intrinsic drawback of the method and no improvement in numerical codes based on
this technique could make up for it.Comment: Accepted for publication in MNRAS. 7 pages, 2 figure
- …