2021 IEEE International Conference on Signal and Image Processing Applications (ICSIPA)最新文献_第2页

Electroencephalogram Stress Classification of Single Electrode using K-means Clustering and Support Vector Machine 基于k均值聚类和支持向量机的单电极脑电信号应力分类

2021 IEEE International Conference on Signal and Image Processing Applications (ICSIPA) Pub Date : 2021-09-13 DOI: 10.1109/ICSIPA52582.2021.9576794

Tee Yi Wen, S. A. M. Aris, S. A. Jalil, S. Usman

{"title":"Electroencephalogram Stress Classification of Single Electrode using K-means Clustering and Support Vector Machine","authors":"Tee Yi Wen, S. A. M. Aris, S. A. Jalil, S. Usman","doi":"10.1109/ICSIPA52582.2021.9576794","DOIUrl":"https://doi.org/10.1109/ICSIPA52582.2021.9576794","url":null,"abstract":"Stress is the body’s natural reaction to life events and chronic stress disrupts the physiological equilibrium of the body which ultimately contributes to a negative impact on physical and mental health. Hence, an endeavor to develop a stress level monitoring system is necessary and important to clinical intervention and disease prevention. Electroencephalography (EEG) acquisition tool was used in this study to capture the brainwave signals at the prefrontal cortex (Fp1 and Fp2) from 50 participants and investigate the brain states related to stress-induced by virtual reality (VR) horror video and intelligence quotient (IQ) test. The collected EEG signals were pre-processed to remove artifacts and the EEG features associated with stress were done through frequency domain analysis to extract power spectral density (PSD) values of Theta, Alpha and Beta frequency bands. The Wilcoxon signed-rank test was carried out to find the significant difference in the absolute power between resting baseline and post-stimuli. The test reported that EEG features using a single electrode, in particular, Theta absolute power was significantly increased at Fp1 electrode (p<0.001) and Fp2 electrode (p<0.015) during post-IQ. Whereas Beta absolute power at Fp2 electrode was observed to significantly increase during both conditions, the post-VR (p<0.024) and post-IQ (p<0.011) respectively. Following this, the significant features were clustered into three groups of stress level using k-means clustering method and the labelled data was fed into support vector machine (SVM) to classify the stress levels. 10-fold cross validation was applied to evaluate the classifier’s performance, with the result confirming the highest performance of 98% accuracy in distinguishing three levels of stress states by using only the feature of Beta-band absolute power from a single electrode (Fp2).","PeriodicalId":326688,"journal":{"name":"2021 IEEE International Conference on Signal and Image Processing Applications (ICSIPA)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126219864","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}

引用次数: 1

Spoken Malay Profanity Classification Using Convolutional Neural Network 基于卷积神经网络的马来语脏话分类

2021 IEEE International Conference on Signal and Image Processing Applications (ICSIPA) Pub Date : 2021-09-13 DOI: 10.1109/ICSIPA52582.2021.9576781

A. Wazir, H. A. Karim, Nouar Aldahoul, M. F. A. Fauzi, Sarina Mansor, Mohd Haris Lye Abdullah, Hor Sui Lyn, Tabibah Zainab Zulkifli

{"title":"Spoken Malay Profanity Classification Using Convolutional Neural Network","authors":"A. Wazir, H. A. Karim, Nouar Aldahoul, M. F. A. Fauzi, Sarina Mansor, Mohd Haris Lye Abdullah, Hor Sui Lyn, Tabibah Zainab Zulkifli","doi":"10.1109/ICSIPA52582.2021.9576781","DOIUrl":"https://doi.org/10.1109/ICSIPA52582.2021.9576781","url":null,"abstract":"Foul language exists in films, video-sharing platforms, and social media platforms, which increase the risk of a viewer to be exposed to large number of profane words that have negative personal and social impact. This work proposes a CNN-based spoken Malay foul words recognition to establish the base of spoken foul terms detection for monitoring and censorship purpose. A novel foul speech containing 1512 samples are collected, processed, and annotated. The dataset then has been converted into spectral representation of Mel-spectrogram images to be used as an input to CNN model. This research proposes a lightweight CNN model with only six convolutional layers and small size filters to minimize the computational cost. The proposed model’s performance affirms the viability of the proposed visual-based classification method using CNN by achieving an average Malay foul speech terms classification accuracy of 86.50%, precision of 88.68%, and F-score of 86.83. The class of normal conversational class outperformed the class of foul words due to data imbalance and rarity of foul speech samples compared to normal speech terms.","PeriodicalId":326688,"journal":{"name":"2021 IEEE International Conference on Signal and Image Processing Applications (ICSIPA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130016403","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}

引用次数: 1

Unified Discriminant and Distribution Alignment for Visual Domain Adaptation 视觉域自适应的统一判别与分布对齐

2021 IEEE International Conference on Signal and Image Processing Applications (ICSIPA) Pub Date : 2021-09-13 DOI: 10.1109/ICSIPA52582.2021.9576812

M. Samsudin, S. Abu-Bakar, M. Mokji

{"title":"Unified Discriminant and Distribution Alignment for Visual Domain Adaptation","authors":"M. Samsudin, S. Abu-Bakar, M. Mokji","doi":"10.1109/ICSIPA52582.2021.9576812","DOIUrl":"https://doi.org/10.1109/ICSIPA52582.2021.9576812","url":null,"abstract":"In visual understandings, images taken from different cameras usually have different resolutions, illumination, poses, and background views that lead to domain shift. Besides labeling these data is an expensive operation. These problems lead to the need for unsupervised domain adaptation (UDA), in which training and testing data are not drawn from the same distribution, and labels are not available in the target domain. This paper presents an improvement for unsupervised domain adaptation in transfer learning using a unified discriminant and distribution alignment (UDDA). The existing method of UDA only utilized unsupervised PCA as the dimensionality reduction process before being added to the joint objective function consisting of distribution discrepancy minimization and regularization. However, the label in the source domain has been utilized by some works (i.e., joint geometrical and statistical (JGSA)) to use the supervised method LDA and show good improvement. Nevertheless, LDA has some drawbacks that is sensitive to noise and outlier in square operations and only take part in global information. The contribution of this paper is to add local discriminant information to the proposed UDA model by adopting locality-sensitive discriminant analysis (LSDA) that strengthens the between-class and within-class discriminant in the source domain. In this method, within-class and between-class graphs will be computed, and summed up with within-class and between-class scatter matrices before being embedded into the joint domain adaptation framework. Comparing the proposed method with the state-of-the-art techniques in objects and digital datasets, it shows our UDDA improved the average accuracy by 3.43% and 7.13% compare with the second and third highest results respectively.","PeriodicalId":326688,"journal":{"name":"2021 IEEE International Conference on Signal and Image Processing Applications (ICSIPA)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127581141","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}

引用次数: 0

IRPMID: Medical XRAY Image Impulse Noise Removal using Partition Aided Median, Interpolation and DWT IRPMID:利用分割辅助中值、插值和小波变换去除医学x射线图像脉冲噪声

2021 IEEE International Conference on Signal and Image Processing Applications (ICSIPA) Pub Date : 2021-09-13 DOI: 10.1109/ICSIPA52582.2021.9576773

M. Shajahan, S. A. M. Aris, S. Usman, N. Noor

引用次数: 1

A Critical Review on Water Level Measurement Techniques for Flood Mitigation 防洪水位测量技术述评

2021 IEEE International Conference on Signal and Image Processing Applications (ICSIPA) Pub Date : 2021-09-13 DOI: 10.1109/ICSIPA52582.2021.9576793

Obaid Rafiq Jan, H. S. Jo, Riady Siswoyo Jo

引用次数: 1

Road Traffic Sign Detection and Recognition using Adaptive Color Segmentation and Deep Learning 基于自适应颜色分割和深度学习的道路交通标志检测与识别

2021 IEEE International Conference on Signal and Image Processing Applications (ICSIPA) Pub Date : 2021-09-13 DOI: 10.1109/ICSIPA52582.2021.9576798

Roozbeh KhabiriKhatiri, I. A. Latiff, Ahmad Sabry Mohamad

引用次数: 1

Region Detection Rate: An Applied Measure for Surface Defect Localization 区域检测率:表面缺陷定位的一种实用方法

2021 IEEE International Conference on Signal and Image Processing Applications (ICSIPA) Pub Date : 2021-09-13 DOI: 10.1109/ICSIPA52582.2021.9576810

T. Huxohl, F. Kummert

引用次数: 1

Sparse Checkerboard Corner Detection from Global Perspective 全局视角下的稀疏棋盘角点检测

2021 IEEE International Conference on Signal and Image Processing Applications (ICSIPA) Pub Date : 2021-09-13 DOI: 10.1109/ICSIPA52582.2021.9576808

Jiwoo Kang, H. Yoon, Seongmin Lee, Sanghoon Lee

{"title":"Sparse Checkerboard Corner Detection from Global Perspective","authors":"Jiwoo Kang, H. Yoon, Seongmin Lee, Sanghoon Lee","doi":"10.1109/ICSIPA52582.2021.9576808","DOIUrl":"https://doi.org/10.1109/ICSIPA52582.2021.9576808","url":null,"abstract":"Detecting corners from an image is an essential step for camera calibration in geometric computer vision and image processing applications. In this paper, a novel framework is proposed to detect sparse checkerboard corners with a global context from an image. The proposed framework addresses two major problems that the previous neural network-based corner detection networks have had: locality and non-sparsity. Our framework encodes the global context from an image and uses the context to determine the per-patch existence of the checkerboard. It enables the network to distinguish between the checkerboard pattern and pattern-like noise in the image background while preserving pixel-level detection details. Also, the patch-wise sparse regularization is introduced using counting distribution to obtain clear-cut predictions while maintaining the true positive rate. The experimental results demonstrate that parsing the global context helps the proposed network to decrease false positive detection significantly. Also, the proposed counting regularization improves to detect true positives while decreasing false negatives concurrently. It enables the proposed network to precisely detect sparse checkerboard corners, leading to significant improvements over the state-of-the-art methods.","PeriodicalId":326688,"journal":{"name":"2021 IEEE International Conference on Signal and Image Processing Applications (ICSIPA)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114943658","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}

引用次数: 1

Dynamic U-Net Using Residual Network for Iris Segmentation 基于残差网络的动态U-Net虹膜分割

2021 IEEE International Conference on Signal and Image Processing Applications (ICSIPA) Pub Date : 2021-09-13 DOI: 10.1109/ICSIPA52582.2021.9576775

Nurul Amirah Mashudi, N. Ahmad, N. Noor

{"title":"Dynamic U-Net Using Residual Network for Iris Segmentation","authors":"Nurul Amirah Mashudi, N. Ahmad, N. Noor","doi":"10.1109/ICSIPA52582.2021.9576775","DOIUrl":"https://doi.org/10.1109/ICSIPA52582.2021.9576775","url":null,"abstract":"Biometric applications have taken tremendous attention these days due to technological advancements and the high demand for safety and security systems. Regardless of the existing biometric traits such as fingerprints, palm, face, retina, voice, and gait, the iris is known as the most consistent and precise trait. Iris segmentation is the most significant and essential stage in the iris recognition process. The segmentation method is precisely related to the performance accuracy of iris recognition. In this study, we proposed a Dynamic U-Net using ResNet-34 to improve the segmentation results based on the F1 score. The proposed method would produce a better accuracy on the condition of applying post-processing. However, based on the comparative analysis with other methods in the literature, our proposed method has produced a higher F1 score. The segmentation results were compared with the Unified IrisParseNet. Our proposed method has produced 93.66% accuracy, which higher than Unified IrisParseNet at 93.05%, respectively. The computational time is also high, which can be further improved in future work.","PeriodicalId":326688,"journal":{"name":"2021 IEEE International Conference on Signal and Image Processing Applications (ICSIPA)","volume":" 21","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132041603","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}

引用次数: 1

Fusion of 2.5D Face Recognition through Extreme Learning Machine via Manifold Flattening 基于流形平坦化的极限学习机融合2.5D人脸识别

2021 IEEE International Conference on Signal and Image Processing Applications (ICSIPA) Pub Date : 2021-09-13 DOI: 10.1109/ICSIPA52582.2021.9576768

L. Chong, S. Chong

引用次数: 0