In this paper, we present an automated video surveillance system designed to 1) ensure efficient selective storage of data, 2) provide means for enhancing privacy protection, and 3) secure visual data against maliciou...
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ISBN:
(纸本)9781424418343
In this paper, we present an automated video surveillance system designed to 1) ensure efficient selective storage of data, 2) provide means for enhancing privacy protection, and 3) secure visual data against malicious attacks. The proposed solution is a 3-module system processing captured video data before storage. Salient motion detection is used to retain relevant sequences and identify regions of interest with potential privacy-sensitive details. Then, an invertible and secure privacy preserving process is performed using a DCT-based scrambling technique on selected regions. To secure visual data and allow for data authentication, a self-embedding watermarking technique is applied on each image sequence. It offers the capability of proving authenticity as well as locating manipulated regions. Furthermore, this technique is also able to recover and reconstruct a good approximation of original lost content. In addition to a low computational complexity, simulation results show the effectiveness of the whole system in achieving its goals in terms of security and privacy enhancement of automated video surveillance data.
Recent years have witnessed the great progress on deep image deraining networks. On the one hand, deraining performance has been significantly improved by designing complex network architectures, yielding high computa...
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ISBN:
(纸本)9781538662496
Recent years have witnessed the great progress on deep image deraining networks. On the one hand, deraining performance has been significantly improved by designing complex network architectures, yielding high computational cost. On the other hand, several lightweight networks try to improve computational efficiency, but at the cost of notable degrading deraining performance. In this paper, we propose a dual recursive network (DRN) for fast image deraining as well as comparable or superior deraining performance compared with state-of-the-art approaches. Specifically, our DRN utilizes a residual network (ResNet) with only 2 residual blocks (Res-Block), which is recursively unfolded to remove rain streaks in multiple stages. Meanwhile, the 2 ResBlocks can be recursively computed in one stage, forming the dual recursive network. Experimental results show that DRN is very computationally efficient and can achieve favorable deraining results on both synthetic and real rainy images.
Multi-wavelet is extension of wavelet theory, it provides a more precise way of image analysis than wavelet multi-resolution. Two images can be fused to a fused picture by means of multi-wavelet analysis, the method o...
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Drainage-network extraction from grid terrain datasets has a marked influence on many applications such as hydrologic analysis, soil erosion and geomorphology. However, the drainage-network extraction is normally very...
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ISBN:
(纸本)9780769550794
Drainage-network extraction from grid terrain datasets has a marked influence on many applications such as hydrologic analysis, soil erosion and geomorphology. However, the drainage-network extraction is normally very time-consuming using the sequential program, especially for grid terrain datasets with high resolutions and large scopes. This paper proposed a set of parallel algorithms to preprocess DEM, determine flow routing and delineate drainage network in an MPI programming model. Both depressions and flat areas can be processed by the DEM preprocessing algorithm. For using the number of upslope-dependence neighbors of a cell, the flow-routing algorithm makes the flow-accumulation calculation similar to a local algorithm. The experiment results show that the proposed parallel approach to extract the drainage network performs much better than the sequential algorithm and has a better parallel efficiency.
This paper focuses on the problem of devising a computationally tractable procedure for representing the natural language understanding (NLU). It approaches this goal, by using distributional models of meaning through...
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ISBN:
(纸本)9781479999118
This paper focuses on the problem of devising a computationally tractable procedure for representing the natural language understanding (NLU). It approaches this goal, by using distributional models of meaning through a method from graph-based digital signalprocessing (DSP) which only recently grabbed the attention of researchers from the field of natural language processing (NLP) related to big data analysis. The novelty of our approach lies in the combination of three domains: advances in deep learning algorithms for word representation, dependency parsing for modeling inter-word relations and convolution using orthogonal Hadamard codes for composing the two previous areas, generating a unique representation for the sentence. Two types of problems are resolved in a new unified way: sentence similarity given by the cos function of the corresponding vectors and question-answering where the query is matched to possible answers. This technique resembles the spread spectrum methods from telecommunication theory where multiple users share a common channel, and are able to communicate without interference. In the content of this paper the case of individual words play the role of users sharing the same sentence. Examples of the method application to a standard set of sentences, used for benchmarking the accuracy and the execution time is also given.
The first part of a cone's signal transduction is investigated from an imageprocessing perspective in order to find out what differentiates (human) vision from computer vision. We found that the activity of cone ...
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ISBN:
(纸本)9789897581496
The first part of a cone's signal transduction is investigated from an imageprocessing perspective in order to find out what differentiates (human) vision from computer vision. We found that the activity of cone opsins-visual pigments that are activated by the impact of a photon-can be described as an approximation of a fractional integrator of order 0.1-0.2 on frequencies between 1-30 Hz. We explore how this affects the output signal and provide examples of how this can be used for noise reduction and imageprocessing. We also present a simplified model since these processes require excessive computational power for computer vision modeling.
image inpainting is one of the most widely used imageprocessing techniques which removes an object from a picture or reconstructs images with lost parts. A method is presented in this article that takes a picture and...
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The design of a computational system based on a synchronous feedback neural network for the online event processing of a photon counting intensified CCD is presented. The detector head consists of a microchannel plate...
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ISBN:
(纸本)0818674563
The design of a computational system based on a synchronous feedback neural network for the online event processing of a photon counting intensified CCD is presented. The detector head consists of a microchannel plate intensifier optically coupled to a fast read out CCD. Pixel data are sequentially fed to a layer of 3/spl times/3 neurons that is to analyse data from the CCD frame and identify photon events against spurious and/or noise events. Accepted events are then passed to a centroiding unit to determine the event centroids to sub-pixel accuracy, to be accumulated in an image memory. The performance of the system is evaluated in terms of result quality and algorithm robustness.
Active contour model has been widely used in imageprocessing applications such as boundary delineation, image segmentation, stereo matching, shape recognition and object tracking. In this paper a novel particle swarm...
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Conventional minutiae-based fingerprint recognition approaches consider only local characteristics and their accuracy dramatically decreases as the number of available minutiae decreases. We propose new features based...
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ISBN:
(纸本)9781479923410
Conventional minutiae-based fingerprint recognition approaches consider only local characteristics and their accuracy dramatically decreases as the number of available minutiae decreases. We propose new features based on Abstracted Radon Profile (ARP). Proposed method uses global properties of an image and it does not necessitate any heavy preprocessing as in classical methods. By using independent gradual patching via proposed multilayer architecture, local characteristics of an image are also preserved. ARP features have an advantage of being robust to zero mean additive noise. For sparse signal representation, dictionary is constructed from the ARP features of the training samples. Recognition is done by l(1)-minimization with quadratic constraints, so this framework can handle dense noise by exploiting the fact that these errors are often sparse. Experimental results in assessing recognition performance demonstrate the proposed approach outperforms the conventional approaches in correlation and distance based comparisons. computational time comparison result shows the proposed feature is more efficient than brute-force method of image alignment and promising for handling other pattern recognition problems as well.
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