Pub Date : 2014-12-01DOI: 10.1109/APSIPA.2014.7041814
P. Temdee, Wacharawan Intayoad
Web based learning has been promoting alternative way of learning for decades. The difficulty of web based learning is to provide the appropriate support for the learners so that the learners will not get lost and their learning achievements can be ensured. This paper thus proposes the method for discovering learning patterns of the learners on web based learning particularly for ensuring the learning achievement. The learning pattern is discovered by analyzing the interactions among the learners and the learning objects with social network analysis. Then, the achievement learning pattern is finally determined by analyzing the sets of obtained social network measurements. The interaction data is gathered from online course named introduction to Information Technology in the 2013 academic year, particularly for spreadsheet content module having 10 learning objects. The interaction patterns only of two groups of students including scientific and nonscientific background knowledge who pass the spreadsheet examination are analyzed. Finally, learning patterns ensuring learning achievement for spreadsheet content module of those students having different background knowledge is revealed.
{"title":"Discovering and analyzing learning pattern on web based learning using social network analysis","authors":"P. Temdee, Wacharawan Intayoad","doi":"10.1109/APSIPA.2014.7041814","DOIUrl":"https://doi.org/10.1109/APSIPA.2014.7041814","url":null,"abstract":"Web based learning has been promoting alternative way of learning for decades. The difficulty of web based learning is to provide the appropriate support for the learners so that the learners will not get lost and their learning achievements can be ensured. This paper thus proposes the method for discovering learning patterns of the learners on web based learning particularly for ensuring the learning achievement. The learning pattern is discovered by analyzing the interactions among the learners and the learning objects with social network analysis. Then, the achievement learning pattern is finally determined by analyzing the sets of obtained social network measurements. The interaction data is gathered from online course named introduction to Information Technology in the 2013 academic year, particularly for spreadsheet content module having 10 learning objects. The interaction patterns only of two groups of students including scientific and nonscientific background knowledge who pass the spreadsheet examination are analyzed. Finally, learning patterns ensuring learning achievement for spreadsheet content module of those students having different background knowledge is revealed.","PeriodicalId":231382,"journal":{"name":"Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2014 Asia-Pacific","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129695065","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 : 2014-12-01DOI: 10.1109/APSIPA.2014.7041666
Keita Shimpo, Toshihisa Tanaka
A brain-computer interface (BCI) based on steady-state visual evoked potentials (SSVEP) is one of the most practical BCI, because of high recognition accuracies and short time training. Phase of SSVEPs can be potentially applicable for generating device commands. However, the effective method of estimating the phase of SSVEPs has not yet been established, especially, in the case of using multi-channel electroencephalogram (EEG). In this paper, we propose a novel method for estimating the phase of SSVEPs from multi-channel EEG, which uses complex sparse spatial weighting. We conducted experiments with the phase-coded SSVEPs based BCI for evaluating performance of our proposed method. As a result, our proposed method showed higher recognition accuracies than conventional methods in all six subjects.
{"title":"Phase detection of multi-channel SSVEPs via complex sparse spatial weighting","authors":"Keita Shimpo, Toshihisa Tanaka","doi":"10.1109/APSIPA.2014.7041666","DOIUrl":"https://doi.org/10.1109/APSIPA.2014.7041666","url":null,"abstract":"A brain-computer interface (BCI) based on steady-state visual evoked potentials (SSVEP) is one of the most practical BCI, because of high recognition accuracies and short time training. Phase of SSVEPs can be potentially applicable for generating device commands. However, the effective method of estimating the phase of SSVEPs has not yet been established, especially, in the case of using multi-channel electroencephalogram (EEG). In this paper, we propose a novel method for estimating the phase of SSVEPs from multi-channel EEG, which uses complex sparse spatial weighting. We conducted experiments with the phase-coded SSVEPs based BCI for evaluating performance of our proposed method. As a result, our proposed method showed higher recognition accuracies than conventional methods in all six subjects.","PeriodicalId":231382,"journal":{"name":"Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2014 Asia-Pacific","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124638003","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 : 2014-12-01DOI: 10.1109/APSIPA.2014.7041537
Yanping Zhou, Y. Duan, Jun Sun, Zongming Guo
This paper focuses on the intra frame rate control of H.264/AVC High Profile and introduces a new frame gradient-based rate control algorithm. In this algorithm, a rate-gradient-quantization parameter model with frame gradient employed as frame complexity is proposed. Then, a proportional feedback scheme, along with an adaptive optimization method, is presented to achieve constant bitrate. Rigorous experiments covering various sequences of different target rates are carried out. Experimental results show that the proposed rate control method outperforms JM16.0 by offering a more constant rate output and reducing rate fluctuation, without video quality loss.
本文重点研究了H.264/AVC High Profile的帧内速率控制，提出了一种基于帧梯度的帧内速率控制算法。在该算法中，提出了一种以帧梯度作为帧复杂度的速率梯度量化参数模型。然后，提出了一种比例反馈方案，并结合自适应优化方法来实现恒定比特率。对不同目标速率的各种序列进行了严格的实验。实验结果表明，该方法在不影响视频质量的前提下，提供了更稳定的速率输出，减少了速率波动，优于JM16.0。
{"title":"Proportional feedback based rate control for intra frame of H.264/AVC high profile","authors":"Yanping Zhou, Y. Duan, Jun Sun, Zongming Guo","doi":"10.1109/APSIPA.2014.7041537","DOIUrl":"https://doi.org/10.1109/APSIPA.2014.7041537","url":null,"abstract":"This paper focuses on the intra frame rate control of H.264/AVC High Profile and introduces a new frame gradient-based rate control algorithm. In this algorithm, a rate-gradient-quantization parameter model with frame gradient employed as frame complexity is proposed. Then, a proportional feedback scheme, along with an adaptive optimization method, is presented to achieve constant bitrate. Rigorous experiments covering various sequences of different target rates are carried out. Experimental results show that the proposed rate control method outperforms JM16.0 by offering a more constant rate output and reducing rate fluctuation, without video quality loss.","PeriodicalId":231382,"journal":{"name":"Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2014 Asia-Pacific","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127201372","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 : 2014-12-01DOI: 10.1109/APSIPA.2014.7041527
Ling-song Zhou, C. Bao, Mao-shen Jia, Bing Bu
3D audio technology based on binaural reproduction requires the Head-Related Transfer Function (HRTF) datasets to be available for all possible distance. However, due to the tedious work of measurement and large volume of resulting datasets, the HRTF is typically measured only for sources located at a fixed distance. In this paper, the concept of virtual loudspeaker arrays is utilized to achieve range extrapolation of the measured HRTF datasets at a single range. The virtual loudspeaker is driven by Higher Order Ambisonics (HOA). Specially, to restrict the near-field effect of HOA, a compensation method of modified Wiener filter is proposed. The simulation results indicate that the proposed method provides effective range extrapolation of HRTF.
{"title":"Range extrapolation of Head-Related Transfer Function using improved Higher Order Ambisonics","authors":"Ling-song Zhou, C. Bao, Mao-shen Jia, Bing Bu","doi":"10.1109/APSIPA.2014.7041527","DOIUrl":"https://doi.org/10.1109/APSIPA.2014.7041527","url":null,"abstract":"3D audio technology based on binaural reproduction requires the Head-Related Transfer Function (HRTF) datasets to be available for all possible distance. However, due to the tedious work of measurement and large volume of resulting datasets, the HRTF is typically measured only for sources located at a fixed distance. In this paper, the concept of virtual loudspeaker arrays is utilized to achieve range extrapolation of the measured HRTF datasets at a single range. The virtual loudspeaker is driven by Higher Order Ambisonics (HOA). Specially, to restrict the near-field effect of HOA, a compensation method of modified Wiener filter is proposed. The simulation results indicate that the proposed method provides effective range extrapolation of HRTF.","PeriodicalId":231382,"journal":{"name":"Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2014 Asia-Pacific","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127604411","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 : 2014-12-01DOI: 10.1109/APSIPA.2014.7041569
K. Hayashi, Masanori Sakai, Takuya Kamenosono, Megumi Kaneko
The paper considers a time domain channel estimation approach for uplink OFDMA (Orthogonal Frequency Division Multiple Access) systems. Although frequency domain channel estimation schemes are widely used for those systems, we propose time domain channel estimation schemes by taking advantage of the sparsity of channel impulse response with compressed sensing. Numerical simulations show the merit of the proposed schemes, which demonstrates the validity of the time domain channel estimation approach for OFDMA systems.
{"title":"Compressed sensing based channel estimation for uplink OFDMA systems","authors":"K. Hayashi, Masanori Sakai, Takuya Kamenosono, Megumi Kaneko","doi":"10.1109/APSIPA.2014.7041569","DOIUrl":"https://doi.org/10.1109/APSIPA.2014.7041569","url":null,"abstract":"The paper considers a time domain channel estimation approach for uplink OFDMA (Orthogonal Frequency Division Multiple Access) systems. Although frequency domain channel estimation schemes are widely used for those systems, we propose time domain channel estimation schemes by taking advantage of the sparsity of channel impulse response with compressed sensing. Numerical simulations show the merit of the proposed schemes, which demonstrates the validity of the time domain channel estimation approach for OFDMA systems.","PeriodicalId":231382,"journal":{"name":"Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2014 Asia-Pacific","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121555654","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 : 2014-12-01DOI: 10.1109/APSIPA.2014.7041701
Masanori Takehara, Hiroya Nojiri, S. Tamura, S. Hayamizu, T. Kurata
Human behavior sensing and their analysis are great role to improve service quality and education of employees. This paper shows novel frameworks of detection of customer communication and lead time estimation(LTE) by using multi-sensored data, sound data and accounting data in the restaurant. They are useful for management about work environments and problems for employees. Lead time from order to delivery shows the quality of the service for customers. We found sound data of an employee's speech is useful for these techniques by speech ratio smoothing and POS sound detection.
{"title":"Analysis of customer communication by employee in restaurant and lead time estimation","authors":"Masanori Takehara, Hiroya Nojiri, S. Tamura, S. Hayamizu, T. Kurata","doi":"10.1109/APSIPA.2014.7041701","DOIUrl":"https://doi.org/10.1109/APSIPA.2014.7041701","url":null,"abstract":"Human behavior sensing and their analysis are great role to improve service quality and education of employees. This paper shows novel frameworks of detection of customer communication and lead time estimation(LTE) by using multi-sensored data, sound data and accounting data in the restaurant. They are useful for management about work environments and problems for employees. Lead time from order to delivery shows the quality of the service for customers. We found sound data of an employee's speech is useful for these techniques by speech ratio smoothing and POS sound detection.","PeriodicalId":231382,"journal":{"name":"Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2014 Asia-Pacific","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131017694","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 : 2014-12-01DOI: 10.1109/APSIPA.2014.7041548
Longbiao Wang, Bo Ren, Yuma Ueda, A. Kai, Shunta Teraoka, T. Fukushima
In this paper, we propose a robust distant-talking speech recognition system with asynchronous speech recording. This is implemented by combining denoising autoencoder-based cepstral-domain dereverberation, automatic asynchronous speech (microphone or mobile terminal) selection and environment adaptation. Although applications using mobile terminals have attracted increasing attention, there are few studies that focus on distant-talking speech recognition with asynchronous mobile terminals. For the system proposed in this paper, after applying a denoising autoencoder in the cepstral domain of speech to suppress reverberation and performing Large Vocabulary Continuous Speech Recognition (LVCSR), we adopted automatic asynchronous mobile terminal selection and environment adaptation using speech segments from optimal mobile terminals. The proposed method was evaluated using a reverberant WSJCAMO corpus, which was emitted by a loudspeaker and recorded in a meeting room with multiple speakers by far-field multiple mobile terminals. By integrating a cepstral-domain denoising autoencoder and automatic mobile terminal selection with environment adaptation, the average Word Error Rate (WER) was reduced from 51.8% of the baseline system to 28.8%, i.e., the relative error reduction rate was 44.4% when using multi-condition acoustic models.
{"title":"Denoising autoencoder and environment adaptation for distant-talking speech recognition with asynchronous speech recording","authors":"Longbiao Wang, Bo Ren, Yuma Ueda, A. Kai, Shunta Teraoka, T. Fukushima","doi":"10.1109/APSIPA.2014.7041548","DOIUrl":"https://doi.org/10.1109/APSIPA.2014.7041548","url":null,"abstract":"In this paper, we propose a robust distant-talking speech recognition system with asynchronous speech recording. This is implemented by combining denoising autoencoder-based cepstral-domain dereverberation, automatic asynchronous speech (microphone or mobile terminal) selection and environment adaptation. Although applications using mobile terminals have attracted increasing attention, there are few studies that focus on distant-talking speech recognition with asynchronous mobile terminals. For the system proposed in this paper, after applying a denoising autoencoder in the cepstral domain of speech to suppress reverberation and performing Large Vocabulary Continuous Speech Recognition (LVCSR), we adopted automatic asynchronous mobile terminal selection and environment adaptation using speech segments from optimal mobile terminals. The proposed method was evaluated using a reverberant WSJCAMO corpus, which was emitted by a loudspeaker and recorded in a meeting room with multiple speakers by far-field multiple mobile terminals. By integrating a cepstral-domain denoising autoencoder and automatic mobile terminal selection with environment adaptation, the average Word Error Rate (WER) was reduced from 51.8% of the baseline system to 28.8%, i.e., the relative error reduction rate was 44.4% when using multi-condition acoustic models.","PeriodicalId":231382,"journal":{"name":"Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2014 Asia-Pacific","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128533131","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 : 2014-12-01DOI: 10.1109/APSIPA.2014.7041563
C. Phromsuthirak, W. Tangsuksant, A. Sanpanich, C. Pintavirooj
A Palmprint, biométrie characteristics, was mostly found in civil and commercial applications for security system because it has more reliable and easy to capture by low resolution devices. This paper was to develop a new contactless palmprint alignment with general USB camera on tripod. The palmprint image is acquired by this camera and using intrinsic local affine-invariant key points residing on the area patches spanning between two successive fingers to align palmprint image. The key points are relative affine invariant to affine transformations so this algorithm does not need the guidance pegs in acquisition process to fix hand position to avoid the scaling, translation and rotation problems for correctly palmprint image alignment. Finally, the developed algorithm was tested by 10 left-handed palmprint images collected from different subjects. The simulation results indicate by distance map error of 1.4899 pixels.
{"title":"Contactless palmprint alignment based on intrinsic local affine-invariant feature points","authors":"C. Phromsuthirak, W. Tangsuksant, A. Sanpanich, C. Pintavirooj","doi":"10.1109/APSIPA.2014.7041563","DOIUrl":"https://doi.org/10.1109/APSIPA.2014.7041563","url":null,"abstract":"A Palmprint, biométrie characteristics, was mostly found in civil and commercial applications for security system because it has more reliable and easy to capture by low resolution devices. This paper was to develop a new contactless palmprint alignment with general USB camera on tripod. The palmprint image is acquired by this camera and using intrinsic local affine-invariant key points residing on the area patches spanning between two successive fingers to align palmprint image. The key points are relative affine invariant to affine transformations so this algorithm does not need the guidance pegs in acquisition process to fix hand position to avoid the scaling, translation and rotation problems for correctly palmprint image alignment. Finally, the developed algorithm was tested by 10 left-handed palmprint images collected from different subjects. The simulation results indicate by distance map error of 1.4899 pixels.","PeriodicalId":231382,"journal":{"name":"Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2014 Asia-Pacific","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131795318","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 : 2014-12-01DOI: 10.1109/APSIPA.2014.7041823
Pongsametrey Sok, Nguonly Taing
This paper describes on the use of Support Vector Machine (SVM) based classification method on Khmer Printed Character-set Recognition (PCR) in bitmap document. Khmer language has been identified as one of the most complex language with the total of 74 alphabets and the wording compound can has up to 5 vertical levels. This paper proposes one new method, SVM for Khmer character classification system by using 3 different SVM kernels (Gaussian, Polynomial and Linear Kernel) on data training and recognition to find out the best kernel for Khmer language. The method allows us to use small training dataset by training different pieces of character training instead of training big amount of clusters. The classification uses binary data of 0 as white space and 1 as black pixel area of the character; each training piece of character has been stretched into a matrix of the binary data in all kinds of image size. Feature extraction is extracted from the matrix to use in SVM classification. After recognition, there are some rules to combine each cluster or character by using character levels or common mistake correction. The experiment of about pure 750 Khmer words or around 3000 characters show that SVM method with Gaussian Kernel produces a good result with better performance among all kernels. The system uses one font "Khmer OS Content" of the training data with font size 32pt to recognize 3 different font sizes. The accuracy of 28pt font size is 98.17%, 32pt is 98.62% and 36pt is 98.54% respectively.
本文介绍了基于支持向量机(SVM)的分类方法在位图文档高棉印刷字符集识别(PCR)中的应用。高棉语被认为是最复杂的语言之一，共有74个字母，措辞复合可以有多达5个垂直层次。本文提出了一种新的方法——支持向量机(SVM)用于高棉语字符分类系统，通过使用3种不同的支持向量机核(高斯核、多项式核和线性核)进行数据训练和识别，找出高棉语的最佳核。该方法允许我们通过训练不同的字符训练片段来使用小的训练数据集，而不是训练大量的聚类。分类使用二进制数据0作为字符的空白区域，1作为字符的黑色像素区域;每个训练字符块被拉伸成各种图像大小的二进制数据矩阵。从矩阵中提取特征提取用于支持向量机分类。识别后，通过使用字符级别或常见错误纠错来组合每个聚类或字符。对750个高棉纯词或3000个左右字符的实验表明，高斯核支持向量机方法取得了较好的结果，在所有核中都有较好的性能。系统使用字号为32pt的训练数据中的一种字体“Khmer OS Content”来识别3种不同的字体大小。字号28pt的准确率为98.17%，字号32pt的准确率为98.62%，字号36pt的准确率为98.54%。
{"title":"Support Vector Machine (SVM) based classifier for Khmer Printed Character-set Recognition","authors":"Pongsametrey Sok, Nguonly Taing","doi":"10.1109/APSIPA.2014.7041823","DOIUrl":"https://doi.org/10.1109/APSIPA.2014.7041823","url":null,"abstract":"This paper describes on the use of Support Vector Machine (SVM) based classification method on Khmer Printed Character-set Recognition (PCR) in bitmap document. Khmer language has been identified as one of the most complex language with the total of 74 alphabets and the wording compound can has up to 5 vertical levels. This paper proposes one new method, SVM for Khmer character classification system by using 3 different SVM kernels (Gaussian, Polynomial and Linear Kernel) on data training and recognition to find out the best kernel for Khmer language. The method allows us to use small training dataset by training different pieces of character training instead of training big amount of clusters. The classification uses binary data of 0 as white space and 1 as black pixel area of the character; each training piece of character has been stretched into a matrix of the binary data in all kinds of image size. Feature extraction is extracted from the matrix to use in SVM classification. After recognition, there are some rules to combine each cluster or character by using character levels or common mistake correction. The experiment of about pure 750 Khmer words or around 3000 characters show that SVM method with Gaussian Kernel produces a good result with better performance among all kernels. The system uses one font \"Khmer OS Content\" of the training data with font size 32pt to recognize 3 different font sizes. The accuracy of 28pt font size is 98.17%, 32pt is 98.62% and 36pt is 98.54% respectively.","PeriodicalId":231382,"journal":{"name":"Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2014 Asia-Pacific","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134026233","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 : 2014-12-01DOI: 10.1109/APSIPA.2014.7041514
T. Pham
The description of information content in images is imprecise in nature. Quantification of uncertainty in images for pattern analysis has been addressed with the theories of probability and fuzzy sets. In this paper, an approach for modeling the spatial uncertainty of images is proposed in the setting of geostatistics and probability measure of fuzzy events. The proposed approach can be utilized to extract an effective feature for image classification.
{"title":"Modeling spatial uncertainty of imprecise information in images","authors":"T. Pham","doi":"10.1109/APSIPA.2014.7041514","DOIUrl":"https://doi.org/10.1109/APSIPA.2014.7041514","url":null,"abstract":"The description of information content in images is imprecise in nature. Quantification of uncertainty in images for pattern analysis has been addressed with the theories of probability and fuzzy sets. In this paper, an approach for modeling the spatial uncertainty of images is proposed in the setting of geostatistics and probability measure of fuzzy events. The proposed approach can be utilized to extract an effective feature for image classification.","PeriodicalId":231382,"journal":{"name":"Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2014 Asia-Pacific","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134376025","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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