2022 7th International Conference on Multimedia and Image Processing最新文献_第3页

Effective Speckle reduction and structure enhancement method for retinal OCT image based on VID and Retinex 基于VID和Retinex的视网膜OCT图像有效斑点消减和结构增强方法

2022 7th International Conference on Multimedia and Image Processing Pub Date : 2022-01-14 DOI: 10.1145/3517077.3517084

Biyuan Li, Yu Wang, Jun Zhang

引用次数: 0

A Novel Screening Framework for Lymph Node Metastasis in Colorectal Cancer Based on Deep Learning Approaches 基于深度学习方法的结直肠癌淋巴结转移筛查新框架

2022 7th International Conference on Multimedia and Image Processing Pub Date : 2022-01-14 DOI: 10.1145/3517077.3517082

Yeming Liu, Fulong Li, Haitao Yu, Zhiyong Zhang, Huiyan Li, Chunxiao Han

{"title":"A Novel Screening Framework for Lymph Node Metastasis in Colorectal Cancer Based on Deep Learning Approaches","authors":"Yeming Liu, Fulong Li, Haitao Yu, Zhiyong Zhang, Huiyan Li, Chunxiao Han","doi":"10.1145/3517077.3517082","DOIUrl":"https://doi.org/10.1145/3517077.3517082","url":null,"abstract":"As a diagnostic criterion for cancer, histopathology image analysis is quite critical for the subsequent therapeutic treatment of patients. Nowadays, the diagnosis is mainly depended on manually which is less precise and low-accuracy. To address the problem, we propose a novel screening framework combined image preprocess and AI approaches for the automatic detection of lymph node metastasis of colorectal cancer. First calculates the Histogram of Oriented Gradient (HOG) and Gray Level Cooccurrence Matrix (GLCM) of high-resolution digital images transformed from pathological sections. Statistical analysis show that Support Vector Machine (SVM) can be used to automatically identify cancerous areas. We further introduce deep learning models Convolutional Neural Network (CNN) into our framework, taking preprocessed images as inputs. The screening results demonstrate that the highest overlapping ratio can be achieved compared with manually annotation areas is 93.09% got by CNN, while another approaches SVM get an accuracy of 83.75%. The combination of image preprocess and deep learning can effectively improve the efficiency of lymph node metastasis screening in colorectal cancer and has great significance for the further development of Computer Aided Diagnosis (CAD) systems.","PeriodicalId":233686,"journal":{"name":"2022 7th International Conference on Multimedia and Image Processing","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130200632","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

Feature extraction of Motion-onset visual evoked potential based on CSP and FBCSP 基于CSP和FBCSP的运动诱发视觉诱发电位特征提取

2022 7th International Conference on Multimedia and Image Processing Pub Date : 2022-01-14 DOI: 10.1145/3517077.3517101

Xinglin He, Li Zhao, Tongning Meng, Zhiwen Zhang

引用次数: 0

Fatigue Driving Vigilance Detection Using Convolutional Neural Networks and Scalp EEG Signals 基于卷积神经网络和头皮脑电信号的疲劳驾驶警觉性检测

2022 7th International Conference on Multimedia and Image Processing Pub Date : 2022-01-14 DOI: 10.1145/3517077.3517099

Y. Fang, Chunxiao Han, Jing Liu, Fengjuan Guo, Yingmei Qin, Y. Che

引用次数: 0

Research on Capsule Leakage Detection Based on Linear Array Camera 基于线阵相机的胶囊泄漏检测研究

2022 7th International Conference on Multimedia and Image Processing Pub Date : 2022-01-14 DOI: 10.1145/3517077.3517094

L. Li, Genghuang Yang, Baoli Wang

{"title":"Research on Capsule Leakage Detection Based on Linear Array Camera","authors":"L. Li, Genghuang Yang, Baoli Wang","doi":"10.1145/3517077.3517094","DOIUrl":"https://doi.org/10.1145/3517077.3517094","url":null,"abstract":"The common detection method used for detecting capsule is to put oil blotting paper on it and to observe whether the paper is clean after the conventional time. This method could cost low payment but need spend more time. A method to detect capsule whether the leakage occurs based on linear array camera is proposed in this paper. Firstly, the capsule images are taken by linear array camera and imaged processing in computer. Secondly, Adaptive Histogram Equalization (AHE) algorithm and Sobel Operator (SO) algorithm are used to sharpen the obtained images to highlight the position of the leakage parts. Finally, the leakage positions are determined by comparing the gray value difference of each area of the images. It is proved by a large number of experiments that, in the context of real-time detection, the error rate of capsule leakage detection is reduced from 10% to 1.5% if it takes the line scan camera to capture the images of a capsule illuminated by a laser with a wavelength of 638nm and the images to process by the above algorithm. Meanwhile, under the same number of comparison experiments, the detection task can be complete seven days in advance. Therefore, the capsule detection method proposed in this paper can greatly improve the accuracy and efficiency.","PeriodicalId":233686,"journal":{"name":"2022 7th International Conference on Multimedia and Image Processing","volume":"295 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114270141","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

Frequency Domain Filtering Based Compressed Sensing Applied on Sparse-angle CT Image Reconstruction 基于频域滤波的压缩感知在稀疏角度CT图像重建中的应用

2022 7th International Conference on Multimedia and Image Processing Pub Date : 2022-01-14 DOI: 10.1145/3517077.3517089

Jian Dong, Hao Chen, Xiaoxia Yang

{"title":"Frequency Domain Filtering Based Compressed Sensing Applied on Sparse-angle CT Image Reconstruction","authors":"Jian Dong, Hao Chen, Xiaoxia Yang","doi":"10.1145/3517077.3517089","DOIUrl":"https://doi.org/10.1145/3517077.3517089","url":null,"abstract":"In the process of CT scanning, multi-angle projection data needs to be obtained from a large number of projection actions, which makes the scanned individual bear the risk of high radiation exposure. In order to solve such problems, the use of sparse projection data for CT image reconstruction is proposed as a new type of solution. The previous research can obtain good quality reconstructed images when the projection data is sparse by using the CT reconstruction technology based on the nonlinear sparsity transformation of compressed sensing. However, the heavy time loading of the image reconstruction is a practical problem that needs to be solved urgently. This study optimizes the non-linear filtering process of the regularization term of the original scheme, and proposes a novel method which replaces the original non-linear filter with a low-pass frequency domain filter. This strategy effectively utilizes the properties of low-pass frequency domain filtering in image processing. The excellent properties include high efficiency and low time complexity for image smoothing. The simulation experiment results show that in the process of CT image reconstruction using compressed sensing algorithm, the low-pass frequency domain filtering of the new scheme can greatly reduce the required time in the reconstruction of sparse projection data, and the image quality is feasibly guaranteed.","PeriodicalId":233686,"journal":{"name":"2022 7th International Conference on Multimedia and Image Processing","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134022204","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

Graph Theoretical Analysis Of Complex Networks In The Alzheimer Brain Using Navie-Bayes Classifier: An EEG And MRI Study 使用纳维-贝叶斯分类器对阿尔茨海默症大脑复杂网络的图论分析:脑电图和MRI研究

2022 7th International Conference on Multimedia and Image Processing Pub Date : 2022-01-14 DOI: 10.1145/3517077.3517079

Ruofan Wang, Y. Yin, Haodong Wang, Lianshuan Shi

{"title":"Graph Theoretical Analysis Of Complex Networks In The Alzheimer Brain Using Navie-Bayes Classifier: An EEG And MRI Study","authors":"Ruofan Wang, Y. Yin, Haodong Wang, Lianshuan Shi","doi":"10.1145/3517077.3517079","DOIUrl":"https://doi.org/10.1145/3517077.3517079","url":null,"abstract":"In order to investigate the changes of local brain regions and the differences of functional network and structural network in patients with Alzheimer's disease, the coherent functional network and structural network were constructed by using EEG signals and MRI images of patients with Alzheimer's disease and normal controls respectively. Then the brain was divided into five brain regions (frontal, parietal, occipital, temporal and central), and seven network topological features were extracted from each brain region. ANOVA1 statistical analysis of these features showed that EEG network and MRI network of AD brain had the same results, that is, there were significant differences in the number of features, and the two groups had significant differences in the frontal lobe region. In order to further analyze the abnormal topological changes of brain structure and functional networks, the single feature and the combination of features of brain regions were used as the input of Naive Bayes classifier. The classification results showed that compared with single feature EEG and MRI network feature combination, the classification accuracy was significantly improved, and the best accuracy was 0.9565 and 0.9621, respectively. This method can effectively distinguish AD group from control group and provide effective support for the study of AD brain.","PeriodicalId":233686,"journal":{"name":"2022 7th International Conference on Multimedia and Image Processing","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129808086","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

A preliminary study of challenges in extracting purity videos from the AV Speech Benchmark 从AV语音基准中提取纯度视频挑战的初步研究

2022 7th International Conference on Multimedia and Image Processing Pub Date : 2022-01-14 DOI: 10.1145/3517077.3517091

Haoran Yan, Huijun Lu, Dunbo Cai, Tao Hang, Ling Qian

{"title":"A preliminary study of challenges in extracting purity videos from the AV Speech Benchmark","authors":"Haoran Yan, Huijun Lu, Dunbo Cai, Tao Hang, Ling Qian","doi":"10.1145/3517077.3517091","DOIUrl":"https://doi.org/10.1145/3517077.3517091","url":null,"abstract":"Recently reported deep audiovisual models have shown promising results on solving the cocktail party problem and are attracting new studies. Audiovisual datasets are an important basis for these studies. Here we investigate the AVSpeech dataset[1], a popular dataset that was launched by the Google team, for training deep audio-visual models for multi-talker speech separation. Our goal is to derive a special kind of video, called purity video, from the dataset. A piece of purity video contains continuous image frames of the same person with a face within a time. A natural question is how we can extract purity videos, as many as possible, from the AVSpeech dataset. This paper presents the tools and methods we utilized, problems we encountered, and the purity video we obtained. Our main contributions are as follows: 1) We propose a solution to extract a derivation subset of the AVSpeech dataset that is of high quality and more than the existing training sets publicly available. 2) We implemented the above solution to perform experiments on the AVSpeech dataset and got insightful results; 3) We also evaluated our proposed solution on our manually labeled dataset called VTData. Experiments show that our solution is effective and robust. We hope this work can help the community in exploiting the AVSpeech dataset for other video understanding tasks.","PeriodicalId":233686,"journal":{"name":"2022 7th International Conference on Multimedia and Image Processing","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123507108","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

Multi-Focus Image Fusion Based on Improved CNN 基于改进CNN的多焦点图像融合

2022 7th International Conference on Multimedia and Image Processing Pub Date : 2022-01-14 DOI: 10.1145/3517077.3517093

Lixia Zhang

引用次数: 0

Establishment of Speaker Recognition Corpus for Intelligent Attendance System 智能考勤系统中说话人识别语料库的建立

2022 7th International Conference on Multimedia and Image Processing Pub Date : 2022-01-14 DOI: 10.1145/3517077.3517118

Shuxi Chen, Yiyang Sun

{"title":"Establishment of Speaker Recognition Corpus for Intelligent Attendance System","authors":"Shuxi Chen, Yiyang Sun","doi":"10.1145/3517077.3517118","DOIUrl":"https://doi.org/10.1145/3517077.3517118","url":null,"abstract":"With the rapid development of information technology, student attendance has changed from paper attendance to machine attendance, such as taking photos, scanning QR codes, positioning, etc. These attendance needs to turn on the camera to take photos, which is slightly inefficient, or turn on the positioning service. However, many people think that turning on the positioning service will infringe on personal privacy. Therefore, we need to consider a more efficient Attendance method that does not infringe on personal privacy. Voice, as a signal that can quickly obtain and contain a variety of information, can be used for class students' attendance. Speaker recognition corpus is the basis of speech speaker recognition research. Diversified, large-scale and high-quality speaker recognition corpus plays an important role in improving the performance of speaker recognition system. At present, although there are many standardized corpora, there are few corpora for student attendance scenes. Therefore, this topic studies the speaker's speech feature parameters, and selects the appropriate Chinese phrases to establish the speaker's corpus.","PeriodicalId":233686,"journal":{"name":"2022 7th International Conference on Multimedia and Image Processing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123967966","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