11,799 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
On the Global Convergence of Continuous-Time Stochastic Heavy-Ball Method for Nonconvex Optimization
We study the convergence behavior of the stochastic heavy-ball method with a
small stepsize. Under a change of time scale, we approximate the discrete
method by a stochastic differential equation that models small random
perturbations of a coupled system of nonlinear oscillators. We rigorously show
that the perturbed system converges to a local minimum in a logarithmic time.
This indicates that for the diffusion process that approximates the stochastic
heavy-ball method, it takes (up to a logarithmic factor) only a linear time of
the square root of the inverse stepsize to escape from all saddle points. This
results may suggest a fast convergence of its discrete-time counterpart. Our
theoretical results are validated by numerical experiments.Comment: accepted at IEEE International Conference on Big Data in 201
A Fusion Framework for Camouflaged Moving Foreground Detection in the Wavelet Domain
Detecting camouflaged moving foreground objects has been known to be
difficult due to the similarity between the foreground objects and the
background. Conventional methods cannot distinguish the foreground from
background due to the small differences between them and thus suffer from
under-detection of the camouflaged foreground objects. In this paper, we
present a fusion framework to address this problem in the wavelet domain. We
first show that the small differences in the image domain can be highlighted in
certain wavelet bands. Then the likelihood of each wavelet coefficient being
foreground is estimated by formulating foreground and background models for
each wavelet band. The proposed framework effectively aggregates the
likelihoods from different wavelet bands based on the characteristics of the
wavelet transform. Experimental results demonstrated that the proposed method
significantly outperformed existing methods in detecting camouflaged foreground
objects. Specifically, the average F-measure for the proposed algorithm was
0.87, compared to 0.71 to 0.8 for the other state-of-the-art methods.Comment: 13 pages, accepted by IEEE TI
On the fast convergence of random perturbations of the gradient flow
We consider in this work small random perturbations (of multiplicative noise
type) of the gradient flow. We prove that under mild conditions, when the
potential function is a Morse function with additional strong saddle condition,
the perturbed gradient flow converges to the neighborhood of local minimizers
in time on the average, where is the
scale of the random perturbation. Under a change of time scale, this indicates
that for the diffusion process that approximates the stochastic gradient
method, it takes (up to logarithmic factor) only a linear time of inverse
stepsize to evade from all saddle points. This can be regarded as a
manifestation of fast convergence of the discrete-time stochastic gradient
method, the latter being used heavily in modern statistical machine learning.Comment: Revise and Resubmit at Asymptotic Analysi
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