... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging最新文献_第2页

Modified SLIC segmentation for medical hyperspectral cell images 改进的SLIC分割医学高光谱细胞图像

... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging Pub Date : 2021-08-06 DOI: 10.1117/12.2604859

Tingting Qiao, Meng Lv, Wei Li, Yu-wen Guo, X. Qiu

{"title":"Modified SLIC segmentation for medical hyperspectral cell images","authors":"Tingting Qiao, Meng Lv, Wei Li, Yu-wen Guo, X. Qiu","doi":"10.1117/12.2604859","DOIUrl":"https://doi.org/10.1117/12.2604859","url":null,"abstract":"Simple linear iterative clustering (SLIC) is a fast and effective method for superpixel segmentation. However, the similarity measurement method of typical SLIC based on spatial and spectral features fails to get precise segmentation boundaries, especially for the images with complex and irregular shapes. To address this issue, a modified SLIC (MSLIC) method based on spectral, color, and texture information is proposed for medical hyperspectral cell images. The Gabor filter is used to exploit detailed texture features, which processes the image by using signal Fourier transform in the frequency domain. The MSLIC employs normalization, Gamma correction, and principal component analysis (PCA) to preprocess medical hyperspectral images, in which the texture features are integrated with spectral and spatial features to measure the distance. The under-segmentation error and boundary recall are used as the criterion of segmentation. Experiments for two medical datasets indicate that MSLIC achieves better segmentation performance than the typical SLIC method.","PeriodicalId":90079,"journal":{"name":"... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging","volume":"1 1","pages":"119130K - 119130K-8"},"PeriodicalIF":0.0,"publicationDate":"2021-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75313607","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

Improved error-correcting from extracted handwritings in Chinese 改进了中文手写体摘编的纠错功能

... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging Pub Date : 2021-08-06 DOI: 10.1117/12.2604703

Hao Bai

引用次数: 0

Contrastive learning for solar cell micro-crack detection 太阳能电池微裂纹检测的对比学习

... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging Pub Date : 2021-08-06 DOI: 10.1117/12.2604745

Yiqin Wang, Shuo Shan, Nawei Zhang, Kanjian Zhang, Haikun Wei

引用次数: 0

Tensor-patch-based discriminative marginalized least squares regression for membranous nephropathy hyperspectral data classification 基于张量补丁的判别边缘最小二乘回归用于膜性肾病高光谱数据分类

... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging Pub Date : 2021-08-06 DOI: 10.1117/12.2604862

Tianhong Chen, Meng Lv, Yue Yang, Tianqi Tu, Wei Li, Wenge Li

{"title":"Tensor-patch-based discriminative marginalized least squares regression for membranous nephropathy hyperspectral data classification","authors":"Tianhong Chen, Meng Lv, Yue Yang, Tianqi Tu, Wei Li, Wenge Li","doi":"10.1117/12.2604862","DOIUrl":"https://doi.org/10.1117/12.2604862","url":null,"abstract":"Least squares regression (LSR)-based classifiers are effective in multi-classification tasks. For hyperspectral image (HSI) classification, the spatial structure information usually helps to improve the performance, however, most existing LSRbased methods use the spectral-vector as input which ignore the important correlations in the spatial domain. To solve the drawback, a tensor-patch-based discriminative marginalized least squares regression (TPDMLSR) is proposed to modify discriminative marginalized least squares regression (DMLSR) with consideration of inter-class separability by employing the region covariance matrix (RCM). RCM is adopted to exploit a region of interest around each hyperspectral pixel to characterize the intrinsic spatial geometric structure of HSI. Specifically, TPDMLSR not only maintains the ascendancy of DMLSR, but also preserves the spatial-spectral structure and enhances the ability of class discrimination for regression by learning the tensor-patch manifold term with a new region covariance descriptor and measuring the inter-class similarity more accurately. The experimental results on membranous nephropathy (MN) dataset validate that TPDMLSR significantly outperforms LSR-based methods reflected in sensitivity, overall accuracy (OA), average accuracy (AA) and Kappa coefficient (Kappa).","PeriodicalId":90079,"journal":{"name":"... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging","volume":"31 1","pages":"119130A - 119130A-8"},"PeriodicalIF":0.0,"publicationDate":"2021-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77077226","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

RP-Unet: a Unet-based network with RNNPool enables computation-efficient polyp segmentation RP-Unet:基于unet的网络，通过RNNPool实现计算效率高的息肉分割

... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging Pub Date : 2021-08-06 DOI: 10.1117/12.2604803

Yue Chen, Zhiwen Liu, Yonggang Shi

{"title":"RP-Unet: a Unet-based network with RNNPool enables computation-efficient polyp segmentation","authors":"Yue Chen, Zhiwen Liu, Yonggang Shi","doi":"10.1117/12.2604803","DOIUrl":"https://doi.org/10.1117/12.2604803","url":null,"abstract":"The incidence of colon cancer has shown an upward trend in recent years, and the appearance of colon polyps is one of the signs of colon cancer. The detection and segmentation of colon polyps are one of the doctors' auxiliary diagnostic methods. However, the increasing number of model parameters and inference memory requirements make the engineering of polyp segmentation models a challenging task. In this paper, an efficient polyp segmentation model based on Unet and RNNPool named RP-Unet is proposed. The first two blocks consisted of two convolutional and max pooling layers in Unet are replaced with the proposed RNNPool Down and Fuse (RDF) modules to rapidly downsample and fuse the input feature maps, and they also provide feature maps for skip connection. The last two blocks in the encoder are replaced with the proposed Double Convolution with Residual connection and RNNPool (DCRR) modules, in which the convolution layers are residually connected, and the max pooling layer is replaced directly with RNNPool. In the two proposed modules, up mapping and channel mapping are used to strengthen feature propagation by mapping activation maps logically instead of allocating unnecessary memory. The proposed RP-Unet is evaluated on two polyp segmentation datasets, and experiments show that the peak inference memory is reduced by almost 22%, while the segmentation accuracy is not significantly reduced.","PeriodicalId":90079,"journal":{"name":"... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging","volume":"35 1","pages":"1191302 - 1191302-7"},"PeriodicalIF":0.0,"publicationDate":"2021-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77761260","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

A new way of deep learning combined with street view images for air pollutant concentration prediction 结合街景图像的深度学习空气污染物浓度预测新方法

... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging Pub Date : 2021-08-06 DOI: 10.1117/12.2605032

Jialiang Zhang, Xiaohai Qin, Ying Liu, Yubo Fan

{"title":"A new way of deep learning combined with street view images for air pollutant concentration prediction","authors":"Jialiang Zhang, Xiaohai Qin, Ying Liu, Yubo Fan","doi":"10.1117/12.2605032","DOIUrl":"https://doi.org/10.1117/12.2605032","url":null,"abstract":"Given the complex spatial structure of urban streets, we use two deep semantic segmentation methods with highprecision to model with street view image data. Through segmentation and quantization, we obtain depth semantic segmentation prediction maps and realize pixel-level classification of multi-objects in the image in a global sense. To accurately and effectively evaluate the urban environmental air quality which is closely related to residents' health, the category target objects related to the predicted pollutant concentration in the image are established as eight categories. The segmentation results are combined with the gas quality data collected by the mobile machine to predict, which can give a set of air pollutant concentration prediction scheme for city management personnel for reference. In this study, a semantic segmentation network is adopted to extract the main environmental factors from street view images as feature vectors of gas prediction models. All the image data used in the experiment were collected in Augsburg, Germany. The sampling tool was a pinhole camera installed on a mobile trolley and set to capture an image every ten seconds. The experiment produced various environmental factors, then input them into the prediction model by combining with the air measurement data of the street view for pollutant prediction. This method can be used as a reference path for evaluating urban environmental quality, air indicators, and air pollutant concentrations.","PeriodicalId":90079,"journal":{"name":"... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging","volume":"27 21 1","pages":"119130L - 119130L-6"},"PeriodicalIF":0.0,"publicationDate":"2021-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79956056","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

Flight trajectory prediction of point-conditioned time and altitudes 点条件时间和高度的飞行轨迹预测

... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging Pub Date : 2021-08-06 DOI: 10.1117/12.2604850

Yingchao Xiao, Yuanyuan Ma, Hui Ding

引用次数: 0

Grading and profiling for export quality coffee beans using red green blue analysis, blob analysis, Hu’s Moments, and back-propagation neural network 使用红绿蓝分析、斑点分析、胡氏矩和反向传播神经网络对出口优质咖啡豆进行分级和分析

... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging Pub Date : 2021-08-06 DOI: 10.1117/12.2605057

Jessie R. Balbin, C. D. Del Valle, Van Julius Leander G. Lopez, Rogelito F. Quiambao

{"title":"Grading and profiling for export quality coffee beans using red green blue analysis, blob analysis, Hu’s Moments, and back-propagation neural network","authors":"Jessie R. Balbin, C. D. Del Valle, Van Julius Leander G. Lopez, Rogelito F. Quiambao","doi":"10.1117/12.2605057","DOIUrl":"https://doi.org/10.1117/12.2605057","url":null,"abstract":"The Philippines used to be one of the prime exporters of coffee from different parts of the world. However due to lack of technology and the absence of standard the production and exportation of coffee diminish through the years. Until now, the coffee farmers are relying on manual operations of classifying and profiling coffee beans intended to level and match the global standard. Hence, the researchers created a system that will automatically classify and profile coffee beans without human intervention based on the different features of coffee beans using integrated image processing algorithms. The focus of this research is to create a device that can evaluate the size, quality, and roast level of a batch of the coffee beans through the use of image processing techniques and Back Propagation Neural Network. To determine these features, BPNN would serve as the method to develop the brain of the device. The integrated processing algorithms used in this research include K-mean shift, Blob, and Canny Edge to extract the features of the coffee beans and Red Green Blue Analysis, Hu's Moment, and Blob Analysis to make use of these features and feed it into the BPNN. Based on the standard set by the Philippine Coffee Board Inc., the prototype in this research was able to classify and profile different coffee beans with up to 100% accuracy.","PeriodicalId":90079,"journal":{"name":"... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging","volume":"76 1","pages":"119130F - 119130F-5"},"PeriodicalIF":0.0,"publicationDate":"2021-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82574606","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

Infrared and visible image fusion algorithm for substation equipment based on NSCT and Siamese network 基于NSCT和Siamese网络的变电站设备红外与可见光图像融合算法

... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging Pub Date : 2021-08-06 DOI: 10.1117/12.2605018

Yang Yang, Yuzhen Yin, Ning Yang, Lihua Li

{"title":"Infrared and visible image fusion algorithm for substation equipment based on NSCT and Siamese network","authors":"Yang Yang, Yuzhen Yin, Ning Yang, Lihua Li","doi":"10.1117/12.2605018","DOIUrl":"https://doi.org/10.1117/12.2605018","url":null,"abstract":"In order to accurately obtain the status information of substation equipment, a large number of infrared and visible images will be used in the process of equipment maintenance. Traditional image fusion methods often lose the temperature information of the image, resulting in low brightness and contrast in the fusion image; while deep learning fusion algorithm will lose some details. Therefore, this paper proposes an infrared and visible light fusion algorithm based on NSCT and Siamese network to improve the quality of fusion image. Firstly, the infrared and visible images are decomposed by NSCT; the high-frequency part and low-frequency part are fused by the fusion rule of guided filtering, and the new high-frequency subband coefficient FH and the new low-frequency subband FL are obtained; then the first fusion image is obtained by NSCT reconstruction of FH and FL; after that, the weight mapping image of the first fusion image and the infrared image is obtained by convolution network, and at the same time Laplacian pyramid is used to decompose the primary fusion image and infrared image, and Gaussian pyramid is used to decompose the weight map; finally, the primary fusion image subband, infrared image subband and weight map image subband are fused according to the local window energy fusion method, and the final image is reconstructed by Laplacian pyramid. Experiments show that the subjective and objective indicators of the fusion picture are all improved.","PeriodicalId":90079,"journal":{"name":"... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging","volume":"12 1","pages":"1191304 - 1191304-7"},"PeriodicalIF":0.0,"publicationDate":"2021-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77784577","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

The sentiment analysis model with multi-head self-attention and Tree-LSTM 基于树- lstm的多头自注意情感分析模型

... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging Pub Date : 2021-08-06 DOI: 10.1117/12.2604779

Lei Li, Yijian Pei, Chenyang Jin

引用次数: 0