Pub Date : 2019-07-01DOI: 10.1109/ICWAPR48189.2019.8946484
Jin Tan, Taiping Zhang, Yuanyan Tang
In recent years, sparse representation of vector signals has been successfully applied in the field of pattern recognition. However, this approach can not be used for single image, as it may require the dictionary to be overcomplete. In addition, the sparse coefficients lack some geometric explanations. This work proposes a novel sparse coding technique on single image. This sparse coding coefficients have explicitly the geometric explanations of images. It depicts the structure information of the image which is robust to variations in illumination, expression, and occlusion. Therefore, the sparse coding coefficients can be used for feature representation of images on small sample case. Experiments on face databases demonstrate the effectiveness of the new sparse coding model.
{"title":"Sparse Representation On Single Image","authors":"Jin Tan, Taiping Zhang, Yuanyan Tang","doi":"10.1109/ICWAPR48189.2019.8946484","DOIUrl":"https://doi.org/10.1109/ICWAPR48189.2019.8946484","url":null,"abstract":"In recent years, sparse representation of vector signals has been successfully applied in the field of pattern recognition. However, this approach can not be used for single image, as it may require the dictionary to be overcomplete. In addition, the sparse coefficients lack some geometric explanations. This work proposes a novel sparse coding technique on single image. This sparse coding coefficients have explicitly the geometric explanations of images. It depicts the structure information of the image which is robust to variations in illumination, expression, and occlusion. Therefore, the sparse coding coefficients can be used for feature representation of images on small sample case. Experiments on face databases demonstrate the effectiveness of the new sparse coding model.","PeriodicalId":436840,"journal":{"name":"2019 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127697741","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 : 2019-07-01DOI: 10.1109/ICWAPR48189.2019.8946463
Hiroka Rinoshika, A. Rinoshika
Three-dimensional (3D) flow structures around a wall-mounted short cylinder of an aspect ratio 1 were instantaneously measured by a high-resolution Tomographic particle image velocimetry (TPIV) in a water tunnel. Here both of the diameter D and height H of the cylinder is 70 mm. The 3D orthogonal wavelet multi-resolution technique is developed to analyze instantaneous 3D velocity fields of a high-resolution Tomographic PIV in order to clarify 3D multi-scale wake flow structures. This paper found a 3D W-type arch vortex behind the short cylinder, which is originated by the interaction between upwash and downwash flows. The head shape of arch vortex structure does not only depend on the aspect ratio of the cylinder, but also is associated with the cylinder diameter. By using the 3D orthogonal wavelet multi-resolution analysis, the instantaneous W-type arch vortex and streamwise vortices are extracted at the wavelet level 1. It also found that the intermediate-scale upwash vortices play an essential role in producing W-type head of arch vortex.
{"title":"Three-Dimensional Orthogonal Wavelet Transform of Tomographic PIV Data","authors":"Hiroka Rinoshika, A. Rinoshika","doi":"10.1109/ICWAPR48189.2019.8946463","DOIUrl":"https://doi.org/10.1109/ICWAPR48189.2019.8946463","url":null,"abstract":"Three-dimensional (3D) flow structures around a wall-mounted short cylinder of an aspect ratio 1 were instantaneously measured by a high-resolution Tomographic particle image velocimetry (TPIV) in a water tunnel. Here both of the diameter D and height H of the cylinder is 70 mm. The 3D orthogonal wavelet multi-resolution technique is developed to analyze instantaneous 3D velocity fields of a high-resolution Tomographic PIV in order to clarify 3D multi-scale wake flow structures. This paper found a 3D W-type arch vortex behind the short cylinder, which is originated by the interaction between upwash and downwash flows. The head shape of arch vortex structure does not only depend on the aspect ratio of the cylinder, but also is associated with the cylinder diameter. By using the 3D orthogonal wavelet multi-resolution analysis, the instantaneous W-type arch vortex and streamwise vortices are extracted at the wavelet level 1. It also found that the intermediate-scale upwash vortices play an essential role in producing W-type head of arch vortex.","PeriodicalId":436840,"journal":{"name":"2019 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130521616","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 : 2019-07-01DOI: 10.1109/ICWAPR48189.2019.8946483
M. Bahri, A. K. Amir, R. Ashino
In this paper, the linear canonical Hilbert transform (LCHT) is considered. Some useful properties of the transform are investigated. The Bedrosian theorem associated with the LCHT is established.
{"title":"Linear Canonical Hilbert Transform and Properties","authors":"M. Bahri, A. K. Amir, R. Ashino","doi":"10.1109/ICWAPR48189.2019.8946483","DOIUrl":"https://doi.org/10.1109/ICWAPR48189.2019.8946483","url":null,"abstract":"In this paper, the linear canonical Hilbert transform (LCHT) is considered. Some useful properties of the transform are investigated. The Bedrosian theorem associated with the LCHT is established.","PeriodicalId":436840,"journal":{"name":"2019 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)","volume":"188 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117282730","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 : 2019-07-01DOI: 10.1109/ICWAPR48189.2019.8946459
Xumin Li, Zhimin He, Huayi Xian, Haozhen Situ, Yan Zhou
How to correct test papers efficiently is an important problem that perplexes teachers in many colleges and universities. For the low efficiency of the total score calculation, this paper proposed an intelligent method based on image processing techniques and Convolutional Neural Network (CNN) to calculate the total score of each test paper automatically. Teachers can use the proposed system to calculate the total score of the test paper, which largely reduces teachers’ workload. The proposed model can quickly recognize and calculate the total score of test papers. The average time of the total score calculation of each test paper was 0.752 seconds in the experiment. Experimental result shows satisfying performance of the proposed method.
{"title":"An Intelligent Total Score Calculation System for Test Paper","authors":"Xumin Li, Zhimin He, Huayi Xian, Haozhen Situ, Yan Zhou","doi":"10.1109/ICWAPR48189.2019.8946459","DOIUrl":"https://doi.org/10.1109/ICWAPR48189.2019.8946459","url":null,"abstract":"How to correct test papers efficiently is an important problem that perplexes teachers in many colleges and universities. For the low efficiency of the total score calculation, this paper proposed an intelligent method based on image processing techniques and Convolutional Neural Network (CNN) to calculate the total score of each test paper automatically. Teachers can use the proposed system to calculate the total score of the test paper, which largely reduces teachers’ workload. The proposed model can quickly recognize and calculate the total score of test papers. The average time of the total score calculation of each test paper was 0.752 seconds in the experiment. Experimental result shows satisfying performance of the proposed method.","PeriodicalId":436840,"journal":{"name":"2019 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115176344","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 : 2019-07-01DOI: 10.1109/ICWAPR48189.2019.8946468
Daiyue Wei, Xiaoman Hu, Keke Chen, P. Chan
Generative adversarial networks (GANs) are commonly applied to example-guided identify preserving face synthesis. A binary classifier is used as style consistency discriminator in GAN model in order to ensure the consistency of style. However, the over-fitting problem of a binary classifier downgrade its discrimination ability on style consistency. In this paper, we propose a style consistency discriminator based on metric learning, which performs better in keeping identity information and guaranteeing consistency in style between input examplar and result. Through separating the positive pairs form the negative, metric learning model can efficiently measure the similarity between the synthesis face and the genuine face. The experimental results indicate that the metric learning performs better than a binary classifier in terms of preserving style consistency.
{"title":"Example-Guided Identify Preserving Face Synthesis by Metric Learning","authors":"Daiyue Wei, Xiaoman Hu, Keke Chen, P. Chan","doi":"10.1109/ICWAPR48189.2019.8946468","DOIUrl":"https://doi.org/10.1109/ICWAPR48189.2019.8946468","url":null,"abstract":"Generative adversarial networks (GANs) are commonly applied to example-guided identify preserving face synthesis. A binary classifier is used as style consistency discriminator in GAN model in order to ensure the consistency of style. However, the over-fitting problem of a binary classifier downgrade its discrimination ability on style consistency. In this paper, we propose a style consistency discriminator based on metric learning, which performs better in keeping identity information and guaranteeing consistency in style between input examplar and result. Through separating the positive pairs form the negative, metric learning model can efficiently measure the similarity between the synthesis face and the genuine face. The experimental results indicate that the metric learning performs better than a binary classifier in terms of preserving style consistency.","PeriodicalId":436840,"journal":{"name":"2019 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126424321","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 : 2019-07-01DOI: 10.1109/icwapr48189.2019.8946482
{"title":"ICWAPR 2019 List Reviewers","authors":"","doi":"10.1109/icwapr48189.2019.8946482","DOIUrl":"https://doi.org/10.1109/icwapr48189.2019.8946482","url":null,"abstract":"","PeriodicalId":436840,"journal":{"name":"2019 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123096519","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 : 2019-07-01DOI: 10.1109/ICWAPR48189.2019.8946489
Lei Zhang, Xin Liang, Weile Zhang, Ruixin Tang, Yiliang Fan, Yu Nan, Ruiqing Song
This paper proposes a behavior recognition pattern recognition method based on image recognition and applies it to the field of dance training. Dance is a performing art and graceful dance is inseparable from the dancers’ good training mode. However, not everyone could enjoy high-quality dance education. We provide a dance training system based on 2D pose estimation and binocular stereo vision. The system relies on binocular imaging principle and deep learning model instead of wearable sensing devices or depth cameras to capture dancer’s three-dimensional human movement in real time. Meanwhile, the learner’s movements will be analyzed to get the difference between the movements and the standard dance, the goodness of the movements and the corresponding scores which are feedback to the learner in order to help them correct their wrong movements and show a better dance.
{"title":"Behavior Recognition On Multiple View Dimension","authors":"Lei Zhang, Xin Liang, Weile Zhang, Ruixin Tang, Yiliang Fan, Yu Nan, Ruiqing Song","doi":"10.1109/ICWAPR48189.2019.8946489","DOIUrl":"https://doi.org/10.1109/ICWAPR48189.2019.8946489","url":null,"abstract":"This paper proposes a behavior recognition pattern recognition method based on image recognition and applies it to the field of dance training. Dance is a performing art and graceful dance is inseparable from the dancers’ good training mode. However, not everyone could enjoy high-quality dance education. We provide a dance training system based on 2D pose estimation and binocular stereo vision. The system relies on binocular imaging principle and deep learning model instead of wearable sensing devices or depth cameras to capture dancer’s three-dimensional human movement in real time. Meanwhile, the learner’s movements will be analyzed to get the difference between the movements and the standard dance, the goodness of the movements and the corresponding scores which are feedback to the learner in order to help them correct their wrong movements and show a better dance.","PeriodicalId":436840,"journal":{"name":"2019 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)","volume":"360 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122311168","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 : 2019-07-01DOI: 10.1109/ICWAPR48189.2019.8946486
Kohei Watarai, Teruya Minamoto
We propose a new method for early esophageal cancer detection from endoscopic images. In the proposed method, an endoscopic image is converted to the CIE $mathrm {L}^{*}mathrm {a}^{*}mathrm {b}^{*}$ color space, and the Haar wavelet transform is applied to the $mathrm {L}^{*}$ and $mathrm {a}^{*}$ components. First, we create an average image of the normal region from the $mathrm {a}^{*}$ component. Next, we calculate the threshold for detecting abnormal regions from the average image, based on a box plot. In our experiment, the $mathrm {L}^{*}$ and $mathrm {a}^{*}$ components of the endoscopic image are divided into small blocks. The $mathrm {L}^{*}$ component is normalized and binarized, to determine the analysis target. The a*component is used to calculate a trim mean, and this is compared with a threshold and binarized. Then, the logical product of the $mathrm {L}^{*}$ and $mathrm {a}^{*}$ components is computed to generate an enhanced image and detect abnormal regions. We describe the method for detecting abnormal regions in detail, and show that our proposed method is useful for early esophageal cancer detection from endoscopic images.
{"title":"Detection of Early Esophageal Cancer from Endoscopic Images Based on a Haar Wavelet Feature","authors":"Kohei Watarai, Teruya Minamoto","doi":"10.1109/ICWAPR48189.2019.8946486","DOIUrl":"https://doi.org/10.1109/ICWAPR48189.2019.8946486","url":null,"abstract":"We propose a new method for early esophageal cancer detection from endoscopic images. In the proposed method, an endoscopic image is converted to the CIE $mathrm {L}^{*}mathrm {a}^{*}mathrm {b}^{*}$ color space, and the Haar wavelet transform is applied to the $mathrm {L}^{*}$ and $mathrm {a}^{*}$ components. First, we create an average image of the normal region from the $mathrm {a}^{*}$ component. Next, we calculate the threshold for detecting abnormal regions from the average image, based on a box plot. In our experiment, the $mathrm {L}^{*}$ and $mathrm {a}^{*}$ components of the endoscopic image are divided into small blocks. The $mathrm {L}^{*}$ component is normalized and binarized, to determine the analysis target. The a*component is used to calculate a trim mean, and this is compared with a threshold and binarized. Then, the logical product of the $mathrm {L}^{*}$ and $mathrm {a}^{*}$ components is computed to generate an enhanced image and detect abnormal regions. We describe the method for detecting abnormal regions in detail, and show that our proposed method is useful for early esophageal cancer detection from endoscopic images.","PeriodicalId":436840,"journal":{"name":"2019 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)","volume":"5 12","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114030728","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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