11,124 research outputs found
3D Textured Model Encryption via 3D Lu Chaotic Mapping
In the coming Virtual/Augmented Reality (VR/AR) era, 3D contents will be
popularized just as images and videos today. The security and privacy of these
3D contents should be taken into consideration. 3D contents contain surface
models and solid models. The surface models include point clouds, meshes and
textured models. Previous work mainly focus on encryption of solid models,
point clouds and meshes. This work focuses on the most complicated 3D textured
model. We propose a 3D Lu chaotic mapping based encryption method of 3D
textured model. We encrypt the vertexes, the polygons and the textures of 3D
models separately using the 3D Lu chaotic mapping. Then the encrypted vertices,
edges and texture maps are composited together to form the final encrypted 3D
textured model. The experimental results reveal that our method can encrypt and
decrypt 3D textured models correctly. In addition, our method can resistant
several attacks such as brute-force attack and statistic attack.Comment: 13 pages, 7 figures, under review of SCI
Dynamic texture recognition using time-causal and time-recursive spatio-temporal receptive fields
This work presents a first evaluation of using spatio-temporal receptive
fields from a recently proposed time-causal spatio-temporal scale-space
framework as primitives for video analysis. We propose a new family of video
descriptors based on regional statistics of spatio-temporal receptive field
responses and evaluate this approach on the problem of dynamic texture
recognition. Our approach generalises a previously used method, based on joint
histograms of receptive field responses, from the spatial to the
spatio-temporal domain and from object recognition to dynamic texture
recognition. The time-recursive formulation enables computationally efficient
time-causal recognition. The experimental evaluation demonstrates competitive
performance compared to state-of-the-art. Especially, it is shown that binary
versions of our dynamic texture descriptors achieve improved performance
compared to a large range of similar methods using different primitives either
handcrafted or learned from data. Further, our qualitative and quantitative
investigation into parameter choices and the use of different sets of receptive
fields highlights the robustness and flexibility of our approach. Together,
these results support the descriptive power of this family of time-causal
spatio-temporal receptive fields, validate our approach for dynamic texture
recognition and point towards the possibility of designing a range of video
analysis methods based on these new time-causal spatio-temporal primitives.Comment: 29 pages, 16 figure
Image Restoration Using Joint Statistical Modeling in Space-Transform Domain
This paper presents a novel strategy for high-fidelity image restoration by
characterizing both local smoothness and nonlocal self-similarity of natural
images in a unified statistical manner. The main contributions are three-folds.
First, from the perspective of image statistics, a joint statistical modeling
(JSM) in an adaptive hybrid space-transform domain is established, which offers
a powerful mechanism of combining local smoothness and nonlocal self-similarity
simultaneously to ensure a more reliable and robust estimation. Second, a new
form of minimization functional for solving image inverse problem is formulated
using JSM under regularization-based framework. Finally, in order to make JSM
tractable and robust, a new Split-Bregman based algorithm is developed to
efficiently solve the above severely underdetermined inverse problem associated
with theoretical proof of convergence. Extensive experiments on image
inpainting, image deblurring and mixed Gaussian plus salt-and-pepper noise
removal applications verify the effectiveness of the proposed algorithm.Comment: 14 pages, 18 figures, 7 Tables, to be published in IEEE Transactions
on Circuits System and Video Technology (TCSVT). High resolution pdf version
and Code can be found at: http://idm.pku.edu.cn/staff/zhangjian/IRJSM
Using Graphics Processor Units (GPUs) for automatic video structuring
The rapid pace of development of Graphic Processor Units (GPUs) in recent years in terms of performance and programmability has attracted the attention of those seeking to leverage alternative architectures for better performance than that which commodity CPUs can provide. In this paper, the potential of the GPU in automatically structuring video is examined, specifically in shot boundary detection and representative keyframe selection techniques. We first introduce the programming model of the GPU and outline the implementation of techniques for shot boundary detection and representative keyframe selection on both the CPU and GPU, using histogram comparisons. We compare the approaches and present performance results for both the CPU and GPU. Overall these results demonstrate the significant potential for the GPU in this domain
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