Abstract: Since the advances in digital imaging technologies have led to
development of high quality digital devices, there are a lot of illegal copies
of copyrighted video content on the Internet. Also, unauthorized editing is
occurred frequently. Thus, we propose an editing prevention technique for
high-quality (HQ) video that can prevent these illegally edited copies from
spreading out. The proposed technique is applied spatial and temporal gradient
methods to improve the fidelity and detection performance. Also, the scheme
duplicates the embedding signal temporally to alleviate the signal reduction
caused by geometric and signal-processing distortions. Experimental results
show that the proposed scheme achieves better performance than previously
proposed schemes and it has high fidelity. The proposed scheme can be used
in unauthorized access prevention method of visual communication or traitor
tracking applications which need fast detection process to prevent illegally
edited video content from spreading out.
Abstract: Variational methods for optical flow estimation are
known for their excellent performance. The method proposed by Brox
et al. [5] exemplifies the strength of that framework. It combines
several concepts into single energy functional that is then minimized
according to clear numerical procedure. In this paper we propose
a modification of that algorithm starting from the spatiotemporal
gradient constancy assumption. The numerical scheme allows to
establish the connection between our model and the CLG(H) method
introduced in [18]. Experimental evaluation carried out on synthetic
sequences shows the significant superiority of the spatial variant of
the proposed method. The comparison between methods for the realworld
sequence is also enclosed.
Abstract: This paper presents an adaptive differentiator
of sequential data based on the adaptive control theory. The
algorithm is applied to detect moving objects by estimating a
temporal gradient of sequential data at a specified pixel. We
adopt two nonlinear intensity functions to reduce the influence
of noises. The derivatives of the nonlinear intensity functions
are estimated by an adaptive observer with σ-modification
update law.