Proceedings of the 2021 3rd International Conference on Image Processing and Machine Vision最新文献

Computation Offloading for Better Real-Time Technical Market Analysis on Mobile Devices 移动设备上更好的实时技术市场分析的计算卸载

Proceedings of the 2021 3rd International Conference on Image Processing and Machine Vision Pub Date : 2021-05-22 DOI: 10.1145/3469951.3469964

Gufeng Shen

引用次数: 0

Research on UAV Signal Classification Algorithm Based on Deep Learning 基于深度学习的无人机信号分类算法研究

Proceedings of the 2021 3rd International Conference on Image Processing and Machine Vision Pub Date : 2021-05-22 DOI: 10.1145/3469951.3469956

Yunsong Zhao

引用次数: 0

Clothing Image Retrieval Based on Parts Detection and Segmentation 基于部位检测与分割的服装图像检索

Proceedings of the 2021 3rd International Conference on Image Processing and Machine Vision Pub Date : 2021-05-22 DOI: 10.1145/3469951.3469961

Qiubo Huang, X. Han, Ting Lu, Guohua Liu

{"title":"Clothing Image Retrieval Based on Parts Detection and Segmentation","authors":"Qiubo Huang, X. Han, Ting Lu, Guohua Liu","doi":"10.1145/3469951.3469961","DOIUrl":"https://doi.org/10.1145/3469951.3469961","url":null,"abstract":"With the rapid development of E-commerce, more and more users are buying clothes through the Internet, and \"image search\" for clothing images has become a popular research direction. The current \"image search\" technology mainly relies on the results of feature extraction of the whole image, but cannot focus on the parts of the clothing, and the background of the clothing image is generally complex, resulting in low accuracy of clothing image retrieval, so we propose a retrieval method based on clothing image detection and segmentation. Firstly, Mask R-CNN is used to detect and segment the image to get the information of garment body, collar parts, sleeve category and pocket positions, then VGG16 is used to extract 512-dimensional features from the garment body and collar parts, based on this information, the similarity between the garment to be retrieved and the garment in the database is calculated one by one. We calculate the similarity by weighting the cosine similarity of 512-dimensional features of the garment body and collar, as well as the similarity of the sleeves and pockets. The search results are presented to the user according to the descending order of similarity. The experimental results show that the method can focus on the whole garment as well as their parts, thus enabling retrieval based on garment style. It also allows users to adjust the weights of each part and can return the search results that best meet their individual needs","PeriodicalId":313453,"journal":{"name":"Proceedings of the 2021 3rd International Conference on Image Processing and Machine Vision","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123967839","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: 2

A Multilevel Thresholding Approach for Acne Detection in Medical Treatment 医学治疗中痤疮检测的多级阈值法

Proceedings of the 2021 3rd International Conference on Image Processing and Machine Vision Pub Date : 2021-05-22 DOI: 10.1145/3469951.3469955

Nguyen Pham Nguyen Xuan, Tham Tran Thi, Thang DO Minh, Duy Tran Ngoc Bao

{"title":"A Multilevel Thresholding Approach for Acne Detection in Medical Treatment","authors":"Nguyen Pham Nguyen Xuan, Tham Tran Thi, Thang DO Minh, Duy Tran Ngoc Bao","doi":"10.1145/3469951.3469955","DOIUrl":"https://doi.org/10.1145/3469951.3469955","url":null,"abstract":"In the quantitative assessment on the success of treatment, the automatic detection of acne pixels from digital color images would be helpful. In this paper, we proposed an automatic acne detection method through the processing of facial images taken by the smartphone based on the image processing. In this approach, the RGB image is transformed into various color spaces based on the differences between features of each acne lesion type. This method has been used the a* channel of the CIELab color space to detect the inflammatory acne (papules and pustules). The S channel of HSV color space was used to detect the non-inflammatory acne (whiteheads and blackheads). A multi-level threshold is then used to make acne extraction and blob detection. The effectiveness of the proposed procedure is shown by experimental results. We showed the possibility of detecting 4 types of acne lesions (whiteheads, blackheads, papules, pustules) with different skin colors and different smartphones in this experiment by applying a combination of several color spaces. The result shows a recall of about 85.71% in detecting different acne types at a reasonable processing time. This is the remise to help doctors to assess the level of acne on the patient's face in an effective and time-saving way.","PeriodicalId":313453,"journal":{"name":"Proceedings of the 2021 3rd International Conference on Image Processing and Machine Vision","volume":"138 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125743233","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

Towards Improving Car Point-Cloud Tracking Via Detection Updates 通过检测更新改进汽车点云跟踪

Proceedings of the 2021 3rd International Conference on Image Processing and Machine Vision Pub Date : 2021-05-22 DOI: 10.1145/3469951.3469957

Yashar Deldjoo, Tommaso Di Noia, Eugenio Di Sciascio, Gaetano Pernisco, V. Renó, E. Stella

引用次数: 0

Two Stream Pose Guided Network for Vehicle Re-identification 车辆再识别的双流姿态引导网络

Proceedings of the 2021 3rd International Conference on Image Processing and Machine Vision Pub Date : 2021-05-22 DOI: 10.1145/3469951.3469954

Saifullah Tumrani, Parivish Parivish, A. Khan, Wazir Ali

引用次数: 1

Reducing the Annotation Cost of Whole Slide Histology Images using Active Learning 利用主动学习方法降低切片组织图像的标注成本

Proceedings of the 2021 3rd International Conference on Image Processing and Machine Vision Pub Date : 2021-05-22 DOI: 10.1145/3469951.3469960

Xu Jin, Hong An, Jue Wang, Ke Wen, Zheng Wu

{"title":"Reducing the Annotation Cost of Whole Slide Histology Images using Active Learning","authors":"Xu Jin, Hong An, Jue Wang, Ke Wen, Zheng Wu","doi":"10.1145/3469951.3469960","DOIUrl":"https://doi.org/10.1145/3469951.3469960","url":null,"abstract":"Histopathology serves as the gold standard for tumor diagnosis. Whole slide scanners have made computer vision-based methods available for pathologists to locate regions of high diagnostic significance. An essential step of whole slide image (WSI) diagnosis is the segmentation of the tumor region by generating a tumor probability heatmap. Most WSI diagnosis methods use patch-based classifiers or segmentation models, they both require a large set of training patches from annotated WSIs. Annotating WSIs is time-consuming and laborious. Active learning is a method that can suggest the most informative unlabeled data for annotation, but traditional active learning methods are not directly applicable for WSIs. Meanwhile, unannotated WSIs also contain rich information that can be further exploited by self-supervised learning. By utilizing unannotated data alongside active learning, we proposed a self-supervised active learning framework for tumor region segmentation of WSIs. The proposed method is evaluated on the public available CAMELYON dataset and achieved satisfying performance using 3% of the annotated data.","PeriodicalId":313453,"journal":{"name":"Proceedings of the 2021 3rd International Conference on Image Processing and Machine Vision","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123294822","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: 4

A Real-Time Single-Shot Multi-Face Detection, Landmark Localization, and Gender Classification 实时单镜头多人脸检测、地标定位和性别分类

Proceedings of the 2021 3rd International Conference on Image Processing and Machine Vision Pub Date : 2021-05-22 DOI: 10.1145/3469951.3469952

T. Shen, D. Wang, Kayton Wai Keung Cheung, M. C. Chan, King Hung Chiu, Yiu Kei Li

引用次数: 1

Integration of Machine Learning with MEC for Intelligent Applications 智能应用中机器学习与MEC的集成

Proceedings of the 2021 3rd International Conference on Image Processing and Machine Vision Pub Date : 2021-05-22 DOI: 10.1145/3469951.3469966

Zhou Ye

{"title":"Integration of Machine Learning with MEC for Intelligent Applications","authors":"Zhou Ye","doi":"10.1145/3469951.3469966","DOIUrl":"https://doi.org/10.1145/3469951.3469966","url":null,"abstract":"∗In recent years, telecom operators and large companies are eager to obtain value from the edge of the network, and the priority of cloud computing has been transferred from the center to the edge. In addition, with the comprehensive deployment of 5G base station (BS), the number of 5G users has been largely increased. For 5G users, they expect to have a better experience of high bandwidth and low latency. Thus, the Mobile Edge Computing (MEC) came into being. MEC brings the capability from the center to the edge of the mobile network. Requests and data of User equipment (UE) has been underlined in MEC. These requests and data will be analyzed and disposed at the edge without being uploaded to the cloud center, which diminishes the latency efficiently. Besides, with the help of machine learning, MEC can show a better performance. This paper is aimed at studying superiorities of MEC itself and integration of machine learning with MEC, and intelligent applications they will bring. This paper first discusses the concept and architecture of MEC, then the advantages of MEC are listed. Next, the improvements of integration of machine learning with MEC and the intelligent applications which employ these technologies will be introduced. Finally, the deficiencies and future research trend of MEC will be discussed. After that, conclusion can be drought that MEC augment the performance of speed, security and privacy, energy saving and reliability. Furthermore, integration of machine learning with MEC can provide better resource management and offloading decision.","PeriodicalId":313453,"journal":{"name":"Proceedings of the 2021 3rd International Conference on Image Processing and Machine Vision","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115837346","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

Comparison of Feature Selection Methods on Arrhythmia Dataset 心律失常数据集特征选择方法的比较

Proceedings of the 2021 3rd International Conference on Image Processing and Machine Vision Pub Date : 2021-05-22 DOI: 10.1145/3469951.3469963

Liu Ziheng

引用次数: 0