International Symposium on Multispectral Image Processing and Pattern Recognition最新文献_第3页

False positive reduction in lymph node detection by using convolutional neural network with multi-view input 基于多视图输入的卷积神经网络减少淋巴结检测中的假阳性

International Symposium on Multispectral Image Processing and Pattern Recognition Pub Date : 2020-02-14 DOI: 10.1117/12.2535551

Jiaqi Wang, Li Xu

引用次数: 0

Tiny object detection using multi-feature fusion 基于多特征融合的微小目标检测

International Symposium on Multispectral Image Processing and Pattern Recognition Pub Date : 2020-02-14 DOI: 10.1117/12.2541898

Peng Yang, Yuejin Zhao, Ming Liu, Liquan Dong, Xiaohua Liu, Mei Hui

{"title":"Tiny object detection using multi-feature fusion","authors":"Peng Yang, Yuejin Zhao, Ming Liu, Liquan Dong, Xiaohua Liu, Mei Hui","doi":"10.1117/12.2541898","DOIUrl":"https://doi.org/10.1117/12.2541898","url":null,"abstract":"Vehicle identification is widely used in route planning, safety supervision and military reconnaissance. It is one of the research hotspots of space-based remote sensing applications. Traditional HOG, Gabor features and Hough transform and other manual design features are not suitable for modern city satellite data analysis. With the rapid development of CNN, object detection has made remarkable progress in accuracy and speed. However, in satellite map analysis, many targets are usually small and dense, which results in the accuracy of target detection often being half or even lower than the big target. Small targets have lower resolution, blurred images, and very rare information. After multi-layer convolution, it is difficult to extract effective information. In the satellite map data set we produced, the target vehicles are not only small but also very dense, and it is impossible to achieve high detection accuracy when using YOLO for training directly. In order to solve this problem, we propose a multi-feature fusion target detection method, which combines satellite image and electronic image to achieve the fusion of target vehicle and surrounding semantic information. We conducted a comparative experiment to demonstrate the applicability of multi-feature fusion methods in different detection models such as YOLO and R-CNN. By comparing with the traditional target detection model, the results show that the proposed method has higher detection accuracy.","PeriodicalId":384253,"journal":{"name":"International Symposium on Multispectral Image Processing and Pattern Recognition","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129675703","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

Three-dimensional measurement method for thickness of LED tape coating based on linear array spectral confocal 基于线阵光谱共聚焦的LED带涂层厚度三维测量方法

International Symposium on Multispectral Image Processing and Pattern Recognition Pub Date : 2020-02-14 DOI: 10.1117/12.2538022

Hanyu Hong, Jiaowei Shi, Xiuhua Zhang, Qingsong Zhao

{"title":"Three-dimensional measurement method for thickness of LED tape coating based on linear array spectral confocal","authors":"Hanyu Hong, Jiaowei Shi, Xiuhua Zhang, Qingsong Zhao","doi":"10.1117/12.2538022","DOIUrl":"https://doi.org/10.1117/12.2538022","url":null,"abstract":"Traditional measuring equipments and methods cannot satisfy the requirements of micrometer-level accuracy and realtime measurement of LED tape coating, the paper proposes a three-dimensional measurement method to compute the thickness of LED tape coating based on linear array spectral confocal. Firstly, the distance data is collected by linear array spectral confocal scanning and converted into 3D point cloud data, then the point cloud is materialized and smoothed to make the 3D object more realistic. Finally, the 3D entity is interacted in the Point Cloud Library to perform manual measurement of the tiny parts of the object. The subsequent automatic measurements are used to control the grating ruler for the specified position moving of measurement based on the previous manual measurement processes and the procedure file. The experimental results indicate that the accuracy of the proposed measurement method is less than 3um, and automatic measurement costs the processing time within 2.5s. In addition, the measurement accuracy is as high as 99.9%, which indicates that the proposed method performs a competitive result.","PeriodicalId":384253,"journal":{"name":"International Symposium on Multispectral Image Processing and Pattern Recognition","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129077563","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

Coupled discriminant mappings for heterogeneous face recognition 异构人脸识别的耦合判别映射

International Symposium on Multispectral Image Processing and Pattern Recognition Pub Date : 2020-02-14 DOI: 10.1117/12.2538163

Xinli Cao, Ke Wen, Likun Huang, Bing Tang, Wei Zhang

{"title":"Coupled discriminant mappings for heterogeneous face recognition","authors":"Xinli Cao, Ke Wen, Likun Huang, Bing Tang, Wei Zhang","doi":"10.1117/12.2538163","DOIUrl":"https://doi.org/10.1117/12.2538163","url":null,"abstract":"Previous efforts on heterogeneous face recognition typically assume each subject has multiple training samples. However, this assumption may not hold in some special cases such as law-enforcement where only a Single Sample Per Person (SSPP) exists in the training set. For face recognition in SSPP scenario, it often suffers from overfitting and singular matrix problems. To solve this problem, we propose a novel learning-based algorithm called Coupled Discriminant Mapping (CDM) for heterogeneous face recognition. The CDM method finds a common space and learns a couple of discriminant projections for two different modalities without depending on the intra-class scatters. In the common space ,images of the same person are pulled into close proximity even if they come through different modalities meanwhile all the image under the same modality are pushed apart since each image belongs to a distinct class. The performance of CDM method is evaluated in two tasks: visual face image vs. near infrared face image and conventional face recognition. Experiments are conducted on two widely studied databases to show the effectiveness and consistence of the proposed CDM method.","PeriodicalId":384253,"journal":{"name":"International Symposium on Multispectral Image Processing and Pattern Recognition","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122364962","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 new robust image feature point detector 一种新的鲁棒图像特征点检测器

International Symposium on Multispectral Image Processing and Pattern Recognition Pub Date : 2020-02-14 DOI: 10.1117/12.2535793

Yi Zhao

引用次数: 0

A cascaded method for transmission tower number recognition in large scenes 大场景下发射塔号码识别的级联方法

International Symposium on Multispectral Image Processing and Pattern Recognition Pub Date : 2020-02-14 DOI: 10.1117/12.2539402

Yuanchun Xia, Guoyou Wang, Ran Wang, F. Zhou

引用次数: 0

Multi-scale oriented object detection in aerial images based on convolutional neural networks with global attention 基于全局关注卷积神经网络的航空图像多尺度定向目标检测

International Symposium on Multispectral Image Processing and Pattern Recognition Pub Date : 2020-02-14 DOI: 10.1117/12.2541855

Jingjing Fei, Zhicheng Wang, Zhaohui Yu, Xi Gu, Gang Wei

{"title":"Multi-scale oriented object detection in aerial images based on convolutional neural networks with global attention","authors":"Jingjing Fei, Zhicheng Wang, Zhaohui Yu, Xi Gu, Gang Wei","doi":"10.1117/12.2541855","DOIUrl":"https://doi.org/10.1117/12.2541855","url":null,"abstract":"Object detection is a fundamental yet challenging problem in natural scenes and aerial scenes. Although region based deep convolutional neural networks (CNNs) have brought impressive improvements for object detection in natural scenes, detecting oriented objects in aerial images still remains challenging, due to the complexity of the aerial image backgrounds and the large degree of freedom in scale, orientation, and density. To tackle these problems, we propose a novel network, composed of backbone structure with global attention module, multi-scale object proposal network and final oriented object detector, which can efficiently detect small objects, arbitrary direction objects, and dense objects in aerial images. We utilize pyramid pooling blocks as a global attention module on the top of the backbone structure to generate discriminative feature representations, which provide diverse context information and complementary receptive field for the detector. The global attention module can help the model reduce false alarms and incorrect classifications in the complex aerial image backgrounds. The multi-scale object proposal network aims to generate object-like regions at different scales through several intermediate layers. After that, these regions are sent to the detector for refined classification and regression, which can alleviate the problem of variant scales in aerial images. The oriented object detector is designed to generate predictions for inclined box. The quantitative comparison results on the challenging DOTA dataset show that our proposed method is more accurate than baseline algorithms and is effective for objection detection in aerial images. The results demonstrate that the proposed method significantly improves the performance.","PeriodicalId":384253,"journal":{"name":"International Symposium on Multispectral Image Processing and Pattern Recognition","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115292463","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

Semantic segmentation of very high resolution remote sensing images with residual logic deep fully convolutional networks 基于残差逻辑深度全卷积网络的高分辨率遥感图像语义分割

International Symposium on Multispectral Image Processing and Pattern Recognition Pub Date : 2020-02-14 DOI: 10.1117/12.2541818

Sheng-Fang He, Jin Liu

引用次数: 1

Turnover and shape filter based feature matching for image stitching 基于周转率和形状滤波器的图像拼接特征匹配

International Symposium on Multispectral Image Processing and Pattern Recognition Pub Date : 2020-02-14 DOI: 10.1117/12.2539406

Shuang Song, Xinguo He, Lin He

引用次数: 0

Multi-scales feature integration single shot multi-box detector on small object detection 多尺度特征集成单镜头多盒探测器对小目标的检测

International Symposium on Multispectral Image Processing and Pattern Recognition Pub Date : 2020-02-14 DOI: 10.1117/12.2538020

Jianbang Zhou, Bo Chen, Jiahao Zhang, Zhong Chen, Jian Yang

引用次数: 1