Appearance modeling is an important and yet challenging issue for online visual tracking due to the accumulation of
errors which is prone to potential drifting during the self-updating with newly obtained results. In this paper, we propose
a novel online tracking algorithm using spatio-temporal cue integration. Specifically, the object is represented as a set of
local patches with respect to the spatial cue. In terms of the temporal cue, we keep the appearance models at different
time and do appearance updating alternately. Taking full advantage of both historical and current information of the
tracked object, the drift problem is alleviated. We also develop an effective cue quality measurement that combines
similarity and motion information. Both qualitative and quantitative evaluations on challenging video sequences
demonstrate that the proposed algorithm performs comparable against the state-of-the-art methods.
KEYWORDS: Digital watermarking, Gaussian filters, RGB color model, Visualization, Digital filtering, Multimedia, Matrices, Data compression, Data processing, Image processing
This paper presents a robust color image watermarking algorithm, which embeds a grayscale image into a color
image using the higher order singular value decomposition (HOSVD). We look the color image in the RGB color space
upon as a tensor rather than three independent channels. The color image is partitioned into non-overlapped patches (subtensors),
and their HOSVDs are computed. Moreover, a subtle preprocessing step, block Arnold transform, is designed to
improve the robustness to cropping attack. Experimental results show that the proposed algorithm makes the
watermarking invisible effectively and is robust against wide variety of non-geometric and geometric attacks.
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