2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)最新文献_第2页

Direct Position Determination of Emitters using Single Moving Coprime Array 利用单运动主素阵列直接确定发射体位置

2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2021-10-23 DOI: 10.1109/CISP-BMEI53629.2021.9624408

Ximeng Zhang, Qingping Wang, Zhaoyu Huang, N. Yuan, W. Hu

引用次数: 3

SLBNet: Shallow and Lightweight Bilateral Network for Pose Estimation SLBNet:姿态估计的浅轻量级双边网络

2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2021-10-23 DOI: 10.1109/CISP-BMEI53629.2021.9624376

Mao-qing Zhou, W. Sun, F. Yang, Sheng Zhang

引用次数: 0

GLM-Net: A multi-scale image segmentation network for brain abnormalities based on GLCM GLM-Net:基于GLCM的脑异常多尺度图像分割网络

2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2021-10-23 DOI: 10.1109/CISP-BMEI53629.2021.9624341

Fuchun Zhang, Yuwen Wang, Liang Wu, Mingtao Liu, Shunbo Hu, Meng Li

{"title":"GLM-Net: A multi-scale image segmentation network for brain abnormalities based on GLCM","authors":"Fuchun Zhang, Yuwen Wang, Liang Wu, Mingtao Liu, Shunbo Hu, Meng Li","doi":"10.1109/CISP-BMEI53629.2021.9624341","DOIUrl":"https://doi.org/10.1109/CISP-BMEI53629.2021.9624341","url":null,"abstract":"In medical image processing, robust brain Magnetic Resonance (MR) images segmentation algorithm is one of the most concerned research fields. It plays an important role in distinguishing healthy tissues from diseased tissues. Because of the complex structure and unpredictable appearance of the brain, it is a complex task to segment tissue parts from brain MR images. The current brain segmentation methods are mostly based on the deep convolution network model, which has the problem of large loss of information in the encoding and decoding process. In order to solve this problem, we propose a brain MR images tissue segmentation method, Gray Level Multiscale Network (GLM-Net), based on three-dimensional U-Net network. The input of the network is constructed of the original image and four characteristic images. The original image is enhanced by Contrast-Limited Adaptive Histogram Equalization (CLAHE) algorithm, and the four characteristic images are generated by Gray Level Co-occurrence Matrix (GLCM) method. The network fused the residual module and dilated convolution for multi-scale feature restoration in the upsampling process of the network decoder to segment the brain MR images into white matter (WM), gray matter (GM), cerebrospinal fluid (CSF) and tumor. The skip connection is used to transmit each set of feature maps generated on the encoder path to the corresponding feature map on the decoder path. Tested and trained on the BraTS 2020 dataset, the average Dice coefficients of WM, GM, CSF and tumor in the segmentation results of the model are about 0.92, 0.91, 0.92 and 0.82 respectively.","PeriodicalId":131256,"journal":{"name":"2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124152707","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 Multi-Sensor Multi-Target Tracker Based on Labeled MS-CPHD Filter 基于标记MS-CPHD滤波的多传感器多目标跟踪器

2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2021-10-23 DOI: 10.1109/CISP-BMEI53629.2021.9624356

Zhiguo Zhang, Jinping Sun, Xiaoke Lu

引用次数: 0

A New Semantic SLAM Mapping Algorithm Based on Improved YOLOv5 基于改进YOLOv5的语义SLAM映射新算法

2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2021-10-23 DOI: 10.1109/CISP-BMEI53629.2021.9624443

Weixiang Shen, Yongxing Jia, Mingcan Li, Junchao Zhu

引用次数: 0

Effect of smartphone camera settings in colorimetric measurements under controlled illumination 智能手机相机设置对受控照明下比色测量的影响

2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2021-10-23 DOI: 10.1109/CISP-BMEI53629.2021.9624389

Ritambhara Thakur, Sunita Bhatt, S. Dubey

{"title":"Effect of smartphone camera settings in colorimetric measurements under controlled illumination","authors":"Ritambhara Thakur, Sunita Bhatt, S. Dubey","doi":"10.1109/CISP-BMEI53629.2021.9624389","DOIUrl":"https://doi.org/10.1109/CISP-BMEI53629.2021.9624389","url":null,"abstract":"Smartphone based point-of-care diagnostics can be a viable alternative for lab-based methods in low resource settings. However smartphone applications for quantification of colorimetric tests are not robust enough and lack field portability as they require external add-on device to mitigate ambient lighting conditions. To make the system accessory free, we need some features which do not change with ambient light conditions. However, camera ISP system adjust itself to give best image by compromising spectral changes. In this study, we investigated smartphone camera settings; ISO and Focus to study the effect of these settings on colorimetric values in controlled lighting conditions. A total of 198 images were acquired using smartphone, where ISO and focus, were changed manually in different lighting conditions; 3 different color temperature and 10 focus points. It was observed that “Hue” parameter of HSV color space and “a” parameter of CIEL*a*b color space depicts least variation with lighting and ISO variation. Also, colorimetric values change with the focus on strip. Therefore, ISO and Focus settings of smartphone should be taken into account for accurate colorimetric estimation.","PeriodicalId":131256,"journal":{"name":"2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129374550","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

Analysis of Handle System and Performance Optimization Research Based on Industrial Internet Scenario 基于工业互联网场景的处理系统分析及性能优化研究

2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2021-10-23 DOI: 10.1109/CISP-BMEI53629.2021.9624343

Yang‐Cheng‐Kuang Chen, Zhongbin Zheng, Shiqiang Li

引用次数: 0

Multi-instance Reservoir Sampling and Selection for Online Continual Detection over VHR Remote Sensing Images VHR遥感图像在线连续检测的多实例库采样与选择

2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2021-10-23 DOI: 10.1109/CISP-BMEI53629.2021.9624392

Jie Jiang, Zhen Han, Sheng Wang, C. Wang

{"title":"Multi-instance Reservoir Sampling and Selection for Online Continual Detection over VHR Remote Sensing Images","authors":"Jie Jiang, Zhen Han, Sheng Wang, C. Wang","doi":"10.1109/CISP-BMEI53629.2021.9624392","DOIUrl":"https://doi.org/10.1109/CISP-BMEI53629.2021.9624392","url":null,"abstract":"Convolutional neural networks (CNNs) have shown outstanding performance in object detection over very-high-resolution (VHR) remote sensing images. However, the regular offline learning mode suffers from catastrophic forgetting problems and performs poorly on the non-stationary and never-ending data. To address this issue, a multi-instance reservoir sampling and selection method (MIRSS) is proposed for the continual detection on continuously generated remote sensing images. A multi-instance reservoir sampling module is used to build a size-fixed buffer and stores the previously learned samples for memory consolidation. Meanwhile, the situation that several objects may exist in each class of an image is focused. Moreover, samples in the buffer are selected with the reservoir selection module for retraining detectors. The experimental results based on three publicly available VHR satellite images, including images from the NWPU VHR-10, RSOD and DOTA data sets, highlight the effectiveness and practicality of the method.","PeriodicalId":131256,"journal":{"name":"2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127816898","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

Visual Question Answering Based on Position Alignment 基于位置对齐的可视化问答

2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2021-10-23 DOI: 10.1109/CISP-BMEI53629.2021.9624447

Qihao Xia, Chao Yu, Pingping Peng, Henghao Gu, Zhengqi Zheng, Kun Zhao

{"title":"Visual Question Answering Based on Position Alignment","authors":"Qihao Xia, Chao Yu, Pingping Peng, Henghao Gu, Zhengqi Zheng, Kun Zhao","doi":"10.1109/CISP-BMEI53629.2021.9624447","DOIUrl":"https://doi.org/10.1109/CISP-BMEI53629.2021.9624447","url":null,"abstract":"The alignment of information from images and questions is of great significance in the visual question answering task. Whether an object in image is related to the question or not is the basic judgement relied on the feature alignment. Many previous works have proposed different alignment methods to build better cross modality interaction. The attention mechanism is the most used method in making alignment. The classical bottom up and top down model builds a top down attention distribution by concatenating question features to each image features and calculates the attention weights between question and image. However, the bottom up and top down model didn't consider the positional information in image and question. In this paper, we revisit the attention distribution from a position perspective which aligns question to object's positional information. We first embed the positional information of each object in image and calculate a position attention distribution to indicate the relevance between objects' positions in context of the current question. Through the attention distribution model can select the related position in image to answer the given question. The position attention distribution is concatenated to the feature attention distribution to get the final distribution. We evaluate our method on visual question answering (VQA2.0) dataset, and show that our method is effective in multimodal alignment.","PeriodicalId":131256,"journal":{"name":"2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"1 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131879150","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

False Cores Reduction Based on Global Features of Fingerprint Orientation Field 基于指纹方向场全局特征的伪核减少

2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2021-10-23 DOI: 10.1109/CISP-BMEI53629.2021.9624425

X. Ye, Hepeng Wang, Rumeng Zou, Yirui Liu, Huahua Chen, Yuzhong Shen

{"title":"False Cores Reduction Based on Global Features of Fingerprint Orientation Field","authors":"X. Ye, Hepeng Wang, Rumeng Zou, Yirui Liu, Huahua Chen, Yuzhong Shen","doi":"10.1109/CISP-BMEI53629.2021.9624425","DOIUrl":"https://doi.org/10.1109/CISP-BMEI53629.2021.9624425","url":null,"abstract":"Singular point detection is a primary step in fingerprint recognition. Many approaches have been proposed, but the positive error rate is still too high. This paper proposes a new method based on global information of fingerprint orientation field to reduce false cores. The proposed method first estimates pixel-level orientation field and extract candidate singular points using the classic nested-Poincare index-based method. Then, both the opening direction of each candidate cores and the corresponding region along the opposite direction to the opening direction are calculated. Finally, the angle between the opening direction of a core and the orientation field in the region along the opposite direction of the opening direction is calculated as a new feature to further reduce false cores in the candidate set. Experimental results show that the error rate of the proposed method is lower than traditional algorithms. The total error rate decreases 29.5% and the false positive rate of cores decreases 35.1% comparing with NPI-based [12] in the database of FVC2000-DB2a.","PeriodicalId":131256,"journal":{"name":"2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115729264","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