Pub Date : 2009-07-12DOI: 10.1109/ICWAPR.2009.5207490
Xiong-Bo Zheng, Xiao-Wei Zhang, Zhi-Juan Weng
Based on the theory of multiwavelet analysis and fractal dimensions, a novel adaptive image interpolation algorithm is proposed. By one level multiwavelet decomposition, the image is transformed into four low frequency bands and twelve high frequency bands. Using the fractal dimensions of high frequency bands, every band of two level multiwavelet transform for high-resolution image is obtained adaptively. Through two level inverse multiwavelet transform, more distinct interpolated image can be obtained. The experimental results shows that the new algorithm has overcome shortcomings of the blur effects that interpolating with the scalar wavelet transform and has better results comparing with the traditional image interpolation methods such as bilinear or bicubic ones.
{"title":"A novel multiwavelet-based interpolation algorithm of images","authors":"Xiong-Bo Zheng, Xiao-Wei Zhang, Zhi-Juan Weng","doi":"10.1109/ICWAPR.2009.5207490","DOIUrl":"https://doi.org/10.1109/ICWAPR.2009.5207490","url":null,"abstract":"Based on the theory of multiwavelet analysis and fractal dimensions, a novel adaptive image interpolation algorithm is proposed. By one level multiwavelet decomposition, the image is transformed into four low frequency bands and twelve high frequency bands. Using the fractal dimensions of high frequency bands, every band of two level multiwavelet transform for high-resolution image is obtained adaptively. Through two level inverse multiwavelet transform, more distinct interpolated image can be obtained. The experimental results shows that the new algorithm has overcome shortcomings of the blur effects that interpolating with the scalar wavelet transform and has better results comparing with the traditional image interpolation methods such as bilinear or bicubic ones.","PeriodicalId":424264,"journal":{"name":"2009 International Conference on Wavelet Analysis and Pattern Recognition","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124927016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-07-12DOI: 10.1109/ICWAPR.2009.5207443
Li-ying Lang, Na Cai
There are many noises in the Fibre-opitc strain sensor system, which directly affects the resolution and stability. For the practicability of the system, the denoising to the output signal must be carried out. In this paper, the original spatial correlation wavelet denoising has been improved and applied to analyze the data of Fibre-optic strain sensor signal. The simulation results show that the improved spatial correlation denoising can perfectly remove the noise from the sensor signal.
{"title":"The application of improved spatial correlation denoising in Fibre-optic strain sensor system","authors":"Li-ying Lang, Na Cai","doi":"10.1109/ICWAPR.2009.5207443","DOIUrl":"https://doi.org/10.1109/ICWAPR.2009.5207443","url":null,"abstract":"There are many noises in the Fibre-opitc strain sensor system, which directly affects the resolution and stability. For the practicability of the system, the denoising to the output signal must be carried out. In this paper, the original spatial correlation wavelet denoising has been improved and applied to analyze the data of Fibre-optic strain sensor signal. The simulation results show that the improved spatial correlation denoising can perfectly remove the noise from the sensor signal.","PeriodicalId":424264,"journal":{"name":"2009 International Conference on Wavelet Analysis and Pattern Recognition","volume":"86 14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122216540","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-07-12DOI: 10.1109/ICWAPR.2009.5207407
Huifeng Shi, Yanxia Lu
Is this paper, Bayesian approach was used to learn the artificial neural network. In Bayesian ANN, the error function consists of two terms: first term is the error term of entire data, second term is the extra regularizing term(also called weight decay term ) which can penalize large weight. Each weight and the error were considered as random variables, their prior probability distributions are normal with zero mean, and their variances constant called the hyper-parameters. The main work of Bayesian approach is obtain the most probable values of hyper-parameters, such that Margin likelihood get maximum values. We used Bayesian Neural network and ordinary ANN as base models to forecast the hour power load. The forecasting results show that the MAPE and RMSE of the Bayesian ANN are all less than that of other Classical ANN. Bayesian ANN has better performance, it can be applied to real forecasting work.
{"title":"Bayesian Neural networks for short term load forecasting","authors":"Huifeng Shi, Yanxia Lu","doi":"10.1109/ICWAPR.2009.5207407","DOIUrl":"https://doi.org/10.1109/ICWAPR.2009.5207407","url":null,"abstract":"Is this paper, Bayesian approach was used to learn the artificial neural network. In Bayesian ANN, the error function consists of two terms: first term is the error term of entire data, second term is the extra regularizing term(also called weight decay term ) which can penalize large weight. Each weight and the error were considered as random variables, their prior probability distributions are normal with zero mean, and their variances constant called the hyper-parameters. The main work of Bayesian approach is obtain the most probable values of hyper-parameters, such that Margin likelihood get maximum values. We used Bayesian Neural network and ordinary ANN as base models to forecast the hour power load. The forecasting results show that the MAPE and RMSE of the Bayesian ANN are all less than that of other Classical ANN. Bayesian ANN has better performance, it can be applied to real forecasting work.","PeriodicalId":424264,"journal":{"name":"2009 International Conference on Wavelet Analysis and Pattern Recognition","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125538787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-07-12DOI: 10.1109/ICWAPR.2009.5207436
Zeng-Shun Zhao
This paper addresses a major problem in the context of visual robot localization. Vision-based localization easily leads to ambiguities in large-scale environments. A probabilistic method is proposed for mobile robots to recognize scenes for topological localization. Appearance-based scene classes are automatically learned from composite features which combine global and local image features extracted from sets of training images. A modified Scale Invariant Feature Transform (SIFT) feature descriptor, which integrates color with local structure, is used as local features to disambiguate the identification of features easily confused. The environment is defined as a topological graph where each node corresponds to a place and edges are paths connecting one node with another. In the course of traveling, each detected interest point vote for the most likely location, and the correct location is the one getting the largest number of votes. In the case of perceptual aliasing, a Hidden Markov Model (HMM) is used to increase the robustness of location recognition. Experimental results show that application of the proposed feature and decision fusion can largely reduce wrong matches and the proposed method is effective.
{"title":"Decision fusion of global and local image features for Markov localization","authors":"Zeng-Shun Zhao","doi":"10.1109/ICWAPR.2009.5207436","DOIUrl":"https://doi.org/10.1109/ICWAPR.2009.5207436","url":null,"abstract":"This paper addresses a major problem in the context of visual robot localization. Vision-based localization easily leads to ambiguities in large-scale environments. A probabilistic method is proposed for mobile robots to recognize scenes for topological localization. Appearance-based scene classes are automatically learned from composite features which combine global and local image features extracted from sets of training images. A modified Scale Invariant Feature Transform (SIFT) feature descriptor, which integrates color with local structure, is used as local features to disambiguate the identification of features easily confused. The environment is defined as a topological graph where each node corresponds to a place and edges are paths connecting one node with another. In the course of traveling, each detected interest point vote for the most likely location, and the correct location is the one getting the largest number of votes. In the case of perceptual aliasing, a Hidden Markov Model (HMM) is used to increase the robustness of location recognition. Experimental results show that application of the proposed feature and decision fusion can largely reduce wrong matches and the proposed method is effective.","PeriodicalId":424264,"journal":{"name":"2009 International Conference on Wavelet Analysis and Pattern Recognition","volume":"06 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127360486","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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