As the development of CG industry and online games, the requirements of efficient texture synthesis methods are more and more exigent. It is one of the toughest problems while rendering the huge scenes efficiently and...
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As the development of CG industry and online games, the requirements of efficient texture synthesis methods are more and more exigent. It is one of the toughest problems while rendering the huge scenes efficiently and effectively. In this paper we propose an efficient texture synthesis algorithm by using wavelet technique. Much different from former texture synthesis methods, the method in this paper synthesizes high resolution texture by using lower resolution component decomposed by wavelet transform, which can improve the synthesis efficiency greatly for either stochastic texture or structural one. By using this method, we can also supply an effective control mechanism especially for the structural texture samples. The result shows that we make improvements both on efficiency and effect.
As a kind of powerful anti-counterfeiting device, diffractive optically variable image (DOVI) has been developed and widely used in information security field. However, the identification of DOVI today by bare eyes is...
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As a kind of powerful anti-counterfeiting device, diffractive optically variable image (DOVI) has been developed and widely used in information security field. However, the identification of DOVI today by bare eyes is not reliable. In this paper we investigate the recognition of DOVI with machine learning method, and five kinds of algorithms, namely quadratic discriminate analysis (QDA), linear discriminate analysis (LDA), regularized discriminate analysis (RDA), leave-one-out covariance matrix estimate (LOOC), and Kullback-Leibler information measure based method (KLIM) are applied to the recognition of DOVI. Considering both time cost and correct classification rate, KLIM classifier exceeds others.
Nonnegative matrix factorization (NMF) has been shown to be an efficient clustering tool. However, NMF¿s batch nature necessitates recomputation of whole basis set for new samples. Although NMF is a powerful cont...
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ISBN:
(纸本)9781424421749
Nonnegative matrix factorization (NMF) has been shown to be an efficient clustering tool. However, NMF¿s batch nature necessitates recomputation of whole basis set for new samples. Although NMF is a powerful content representation tool, this limits the use of NMF in online processing of large data sets. Another problem with NMF, like other partitional methods, is determining the actual number of clusters. Deciding the rank of the factorization is also critical since it has a significant effect on clustering performance. This paper introduces an NMF based incremental clustering algorithm which allows increasing number of clusters adaptively thus eliminates optimal rank selection problem. Test results obtained on large video data sets demonstrate that the proposed clustering scheme is capable of labeling linearly separable data as well as non-separable samples with a small false positive ratio.
Combining bottom-up and top-down attention influences, a novel region extraction model which based on object-accumulated visual attention mechanism is proposed in this paper. Compared with early research, the new appr...
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Combining bottom-up and top-down attention influences, a novel region extraction model which based on object-accumulated visual attention mechanism is proposed in this paper. Compared with early research, the new approach brings in prior information at the proper time, updates scan path dynamically, needs less computational resources and reduces the probability to direct the attention to a less-meaning area. The application to search an airport target in remote sensing image was provided, through which the novel mechanism that how visual attention chose the area was described. Compared with another two region extraction models, experimental results confirm the effectiveness of the approach proposed in this paper.
In this paper, we describe four important indirect methods which be used to extract the fetal Electrocardiogram (FECG) signal from an ECG recorded on the mother's abdomen. These methods include the following ones:...
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In this paper, we describe four important indirect methods which be used to extract the fetal Electrocardiogram (FECG) signal from an ECG recorded on the mother's abdomen. These methods include the following ones: singular value decomposition (SVD) method, independent component analysis (ICA) method, wavelet based methods and adaptive filtering method. The mentioned methods use signal processing techniques for extracting FECG from abdominal electrocardiogram (AECG). We have explained advantages and disadvantages of each method. The methods have also applied on both synthetic and real ECG signals. Efficiencies of the methods compared together based on three important criterions and results are stated and best method based on three criterions is selected.
In wireless sensor networks, to obtain a long network lifetime is a fundamental issue while without sacrificing crucial aspects of quality of service (area coverage, sensing reliability, and network connectivity). In ...
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In wireless sensor networks, to obtain a long network lifetime is a fundamental issue while without sacrificing crucial aspects of quality of service (area coverage, sensing reliability, and network connectivity). In this paper, we present a Voronoi-based sleeping configuration to deal with different sensing radii and location error. With our proposed sleeping candidate condition, redundant sensors are optionally identified and scheduled to sleep in order to extend the system lifetime while maintaining adequate sensor redundancy to tolerate sensor failures, energy depletions, and location error. Simulation results show that there is a tradeoff among energy conservation, area coverage, and fault tolerance, which varies between different sleeping candidate conditions.
The prevalence of Alzheimer's disease (AD) is rising alarmingly as the average age of our population increases. There is no treatment to halt or slow the pathology responsible for AD, however, new drugs are promis...
The prevalence of Alzheimer's disease (AD) is rising alarmingly as the average age of our population increases. There is no treatment to halt or slow the pathology responsible for AD, however, new drugs are promising to reduce the rate of progression. On the other hand, the efficacy of these new medications critically depends on our ability to diagnose AD at the earliest stage. Currently AD is diagnosed through longitudinal clinical evaluations, which are available only at specialized dementia clinics, hence beyond financial and geographic reach of most patients. Automated diagnosis tools that can be made available to community hospitals would therefore be very beneficial. To that end, we have previously shown that the event related potentials obtained from different scalp locations can be effectively used for early diagnosis of AD using an ensemble of classifiers based decision fusion approach. In this study, we expand our data fusion approach to include MRI based measures of regional brain atrophy. Our initial results indicate that ERPs and MRI carry complementary information, and the combination of these heterogeneous data sources using a decision fusion approach can significantly improve diagnostic accuracy.
This paper designs a new kind of lifting 9-7-tap wavelet filter with binary characteristics and presents a high speed VLSI structure for the wavelet filter. The coefficients of the lifting filters are binary numbers, ...
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This paper designs a new kind of lifting 9-7-tap wavelet filter with binary characteristics and presents a high speed VLSI structure for the wavelet filter. The coefficients of the lifting filters are binary numbers, so it can simplify the VLSI design. The test results for compression performances have shown that the new filter is good almost as CDF97 under PSNR. when the data has a finite accuracy, it may have a better performance than CDF97. The filter can be implemented with using adders and shifters. Therefore the hardware resources can be saved and the critical path can be shortened. According to using the folding technology and the retiming technology, the architecture of the design can be transformed into a kind of embedded folding architecture which leads to the parallel computation of addition operations. So the critical path can be shortened to nearly the time of a addition operation and the utilization of the hardware resources also has a good performance. Simulation results show that the max working frequency could almost reach 250 MHz which is the four times by the CDF97;The occupied logic cell is reduced by 66.7 compared with the CDF97+4 stages pipeline. So it is especially suitable for the high speed VLSI design.
A theoretical study for modeling technique of the remote sensing image classification based on the minimum description length (MDL) principle is presented in the paper. According to the MDL principle, modeling problem...
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A theoretical study for modeling technique of the remote sensing image classification based on the minimum description length (MDL) principle is presented in the paper. According to the MDL principle, modeling problem is an optimization procedure to find the shortest expected code length. Kullback-Leibler (KL) divergence is adopted as the system cost function to measure expected codelength, and the codelength will be the model we desired. The advantage of using the MDL principle to build appropriate model is analyzed theoretically, model optimization technique also is described.
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