In order to improve the accuracy of the image segmentation in video surveillance sequences and to overcome the limits of the traditional clustering algorithms that can not accurately model the image data sets which Co...
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In order to improve the accuracy of the image segmentation in video surveillance sequences and to overcome the limits of the traditional clustering algorithms that can not accurately model the image data sets which Contains noise data,the paper presents an automatic and accurate video image segmentation algorithm,according to the spatial properties,which uses the Gaussian mixture models to segment the imageBut the expectation-maximization algorithm is very sensitive to initial values,and easy to fall into local optimums,so the paper presents a differential evolution-based parameters estimation for Gaussian mixture modelsThe experiment result shows that the segmentation accuracy has been improved greatly than by the traditional segmentation algorithms
The stability of Boolean networks and the stabilization of Boolean control networks are *** semi-tensor product of matrices and the matrix expression of logic,the dynamics of a Boolean(control) network can be converte...
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The stability of Boolean networks and the stabilization of Boolean control networks are *** semi-tensor product of matrices and the matrix expression of logic,the dynamics of a Boolean(control) network can be converted to a discrete time linear(bilinear) dynamics,called the algebraic form of the Boolean(control) *** provide a framework for this *** results consist of two parts:(i) Using logic coordinate transformation,the known sufficient condition based on incidence matrix has been *** is also used in stabilizer design.(ii)Based on algebraic form,necessary and sufficient conditions for stability of Boolean networks and stabilization of Boolean control networks respectively are obtained.
To avoid the complexity and inefficiency for specific applications of the current software architecture, a novel approach using partial evaluation is proposed to improve the running performance of components. The gene...
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To avoid the complexity and inefficiency for specific applications of the current software architecture, a novel approach using partial evaluation is proposed to improve the running performance of components. The generic program was specialized into domain-specific realization for the known knowledge and environments. The syntax and semantic(adj.) were analyzed based on byte code instruction sequences, and partial evaluation rules depicted how to perform the specialization. The partial evaluation for object-oriented programs was implemented. The experimental results show that partial evaluation is effective to speed up the running efficiency. The more generality and scalability can be obtained by the integration of partial evaluation with the favorable design mechanisms and compiler optimization technology.
This paper proposes an unsupervised approach to automatically interpret noun compounds using semantic similarity. Our proposed unsupervised method is based on obtaining a large amount of robust evidence for NC interpr...
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ISBN:
(纸本)9781424427802
This paper proposes an unsupervised approach to automatically interpret noun compounds using semantic similarity. Our proposed unsupervised method is based on obtaining a large amount of robust evidence for NC interpretation. In order to obtain evidence sentences for semantic relations (SRs), we first acquired sentences containing both a head noun and its modifier in the form of SR definitions. Then we determined the semantic relations represented in the sentences by looking at the nouns in the test instances (noun mapping) and verbs in the SR definitions (verb mapping). In the noun mapping, we measured the similarity between nouns in test instances and nouns in the collected sentences. In the verb mapping, we mapped the verbs ofsentences onto those in the SR definitions. Finally, we built a statistical classifier to interpret noun compounds and evaluated it over 17 SRs defined in [1].
This paper proposes an unsupervised approach to automatically interpret noun compounds using semantic similarity. Our proposed unsupervised method is based on obtaining a large amount of robust evidence for NC interpr...
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This paper proposes an unsupervised approach to automatically interpret noun compounds using semantic similarity. Our proposed unsupervised method is based on obtaining a large amount of robust evidence for NC interpretation. In order to obtain evidence sentences for semantic relations (SRs), we first acquired sentences containing both a head noun and its modifier in the form of SR defi-nitions. Then we determined the semantic relations represented in the sentences by looking at the nouns in the test instances (noun mapping) and verbs in the SR definitions (verb mapping). In the noun mapping, we measured the similarity between nouns in test instances and nouns in the collected sentences. In the verb mapping, we mapped the verbs of sentences onto those in the SR definitions. Finally, we built a statistical classifier to interpret noun compounds and evaluated it over 17 SRs defined in [1].
Most of research efforts have elaborated on k-anonymity for location privacy. The general architecture for implementing k-anonymity is that there is one trusted server (referred to as location anonymizer) responsible ...
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ISBN:
(纸本)9780769531588;9780769534114
Most of research efforts have elaborated on k-anonymity for location privacy. The general architecture for implementing k-anonymity is that there is one trusted server (referred to as location anonymizer) responsible for cloaking at least k users' locations for protecting location privacy. A location anonymizer will generate cloaked regions in which there are at least k users for query processing. Prior works only explore grid shape cloaked regions. However, grid shape cloaked regions result in a considerable amount of query results, thereby increasing the overhead of filtering unwanted query results. In this paper, we propose a cloaking algorithm in which cloaked regions are generated acording to the features of spatial networks. By exploring the features of spatial networks, the cloaked regions are very efficient for reducing query results and improving cache utilization of mobile devices. Furthermore, an index structure for spatial networks is built and in light of the proposed index structure, we develop a Spatial-Temporal Connective Cloaking algorithm(abbreviated as STCC). A simulator is implemented and extensive experiments are conducted. Experimental results show that our proposed algorithm out-performs prior cloaking algorithms in terms of the candidate query results and the cache utilization.
After the modification of the fuzzy ART neural network operations, An algorithm based on fuzzy ART neural network which can deal with online-learning and recognition of the known and unknown faces at the same time is ...
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After the modification of the fuzzy ART neural network operations, An algorithm based on fuzzy ART neural network which can deal with online-learning and recognition of the known and unknown faces at the same time is designed and realized. The simulation experiment results show an online maximum recognition rate is 86.3％ and offline recognition rates are nearly 100％ when the proper network parameters are selected. 400 images of 40 persons in the publicly available AT&T face database are used for simulation experiment.
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