International Symposium on Multispectral Image Processing and Pattern Recognition最新文献_第2页

Object detection for rotated and densely arranged objects in aerial images using path aggregated feature pyramid networks 基于路径聚合特征金字塔网络的航空图像旋转密集目标检测

International Symposium on Multispectral Image Processing and Pattern Recognition Pub Date : 2020-02-14 DOI: 10.1117/12.2538090

Xiangyu Liu, Hong Pan, Xinde Li

引用次数: 2

Multiscale DEM generation on basis of singular value decomposition 基于奇异值分解的多尺度DEM生成

International Symposium on Multispectral Image Processing and Pattern Recognition Pub Date : 2020-02-14 DOI: 10.1117/12.2537903

Caixian Zhang, Jun He, Wenguang Hou

引用次数: 1

Comparative study on colorimetric characterization of LCD based on polynomial 基于多项式的LCD比色特性比较研究

International Symposium on Multispectral Image Processing and Pattern Recognition Pub Date : 2020-02-14 DOI: 10.1117/12.2539459

Tong Li, K. Xie, Hongtai Guo, Zixuan Li, Qi Zheng

引用次数: 1

Smoke detection in infrared images based on superpixel segmentation 基于超像素分割的红外图像烟雾检测

International Symposium on Multispectral Image Processing and Pattern Recognition Pub Date : 2020-02-14 DOI: 10.1117/12.2538195

Min Dai, Peng Gao, Mozhou Sha, J. Tian

引用次数: 2

A fast multi-target detection method based on improved YOLO 基于改进YOLO的快速多目标检测方法

International Symposium on Multispectral Image Processing and Pattern Recognition Pub Date : 2020-02-14 DOI: 10.1117/12.2539386

Xiechang Sun, Hao Jiang, Tongtong Huo, Weidong Yang

{"title":"A fast multi-target detection method based on improved YOLO","authors":"Xiechang Sun, Hao Jiang, Tongtong Huo, Weidong Yang","doi":"10.1117/12.2539386","DOIUrl":"https://doi.org/10.1117/12.2539386","url":null,"abstract":"Detection of sea surface targets in large-scale remote sensing images is one of the important research topics of ocean remote sensing technology. Ocean remote sensing images have the characteristics of wide format, strong interference and small target. This paper adopts the spinning target detection method, and proposes a ship detection model based on YOLO to output the real length, width and axial information. The model can accurately output the position, length and width and axial information of a ship target by predicting the minimum external rectangular area of the ship target, so as to realize multi-target detection and improve the detection performance significantly. To improve the recall rate of the target detection algorithm, this paper adopts the spinning target detection method, and proposes a ship detection model based on YOLO. Through redefining the representation of the rotation matrix and redesigning a new network loss function and the rotated IOU computing method, this model accurately outputs the real length, width and axial information, increases the output feature dimensions, and effectively raises the recall rate and speed of multi-target detection. Lastly, to improve the practicability of the algorithm on mobile devices, the model is processed in a lightweight way. Its parameters are significantly reduced while the detection accuracy is ensured.","PeriodicalId":384253,"journal":{"name":"International Symposium on Multispectral Image Processing and Pattern Recognition","volume":"31 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":"127389826","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: 5

A novel exponential loss function for pathological lymph node image classification 一种新的指数损失函数用于病理淋巴结图像分类

International Symposium on Multispectral Image Processing and Pattern Recognition Pub Date : 2020-02-14 DOI: 10.1117/12.2537004

Guoping Xu, Hanqiang Cao, J. Udupa, Chunyi Yue, Youli Dong, Li Cao, D. Torigian

{"title":"A novel exponential loss function for pathological lymph node image classification","authors":"Guoping Xu, Hanqiang Cao, J. Udupa, Chunyi Yue, Youli Dong, Li Cao, D. Torigian","doi":"10.1117/12.2537004","DOIUrl":"https://doi.org/10.1117/12.2537004","url":null,"abstract":"Recent progress in deep learning, especially deep convolutional neural networks (DCNNs), has led to significant improvement in natural image classification. However, research is still ongoing in the domain of medical image analysis in part due to the shortage of annotated data sets for training DCNNs, the imbalanced number of positive and negative samples, and the difference between medical images and natural images. In this paper, two strategies are proposed to train a DCNN for pathological lymph node image classification. Firstly, the transfer learning strategy is used to deal with the shortage of training samples. Second, a novel exponential loss function is presented for the imbalance in training samples. Four state-of-the-art DCNNs (GoogleNet, ResNet101, Xception, and MobileNetv2) are tested. The experiments demonstrate that the two strategies are effective to improve the performance of pathological lymph node image classification in terms of accuracy and sensitivity with a mean of 0.13% and 1.50%, respectively, for the four DCNNs. In particular, the proposed exponential loss function improved the sensitivity by 3.9% and 4.0% for Xception and ResNet101, respectively.","PeriodicalId":384253,"journal":{"name":"International Symposium on Multispectral Image Processing and Pattern Recognition","volume":"2016 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":"127426256","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: 7

ATSGPN: adaptive threshold instance segmentation network in 3D point cloud 三维点云的自适应阈值实例分割网络

International Symposium on Multispectral Image Processing and Pattern Recognition Pub Date : 2020-02-14 DOI: 10.1117/12.2541582

Yu Sun, Zhicheng Wang, Jingjing Fei, Ling Chen, Gang Wei

引用次数: 0

Study on sophisticated vegetation classification for AHSI/GF-5 remote sensing data AHSI/GF-5遥感数据的复杂植被分类研究

International Symposium on Multispectral Image Processing and Pattern Recognition Pub Date : 2020-02-14 DOI: 10.1117/12.2539369

K. Shang, Yisong Xie, Hongyan Wei

引用次数: 3

Outdoor cycle three-dimensional intelligent parking lot system 户外自行车立体智能停车场系统

International Symposium on Multispectral Image Processing and Pattern Recognition Pub Date : 2020-02-14 DOI: 10.1117/12.2557558

Shihong Qin, Ruixing Wang, Hongcheng Zhou, Zhao Xiong, Jun Jian, Ziyi Mei

引用次数: 0

A novel self-relearning approach for Landsat image change detection 一种新的陆地卫星图像变化检测自学习方法

International Symposium on Multispectral Image Processing and Pattern Recognition Pub Date : 2020-02-14 DOI: 10.1117/12.2538192

Xiaoyu Chang, Xin Huang, Jiayi Li

{"title":"A novel self-relearning approach for Landsat image change detection","authors":"Xiaoyu Chang, Xin Huang, Jiayi Li","doi":"10.1117/12.2538192","DOIUrl":"https://doi.org/10.1117/12.2538192","url":null,"abstract":"Land cover composition and change are important aspects for many scientific research and socioeconomic assessments. The multi-date land cover change detection is generally more difficult and time-consuming to select enough training samples when considering multi-date image labels at the same location. To improve the accuracy for multi-date change detection, this study proposed a new algorithm framework, combining self-learning and relearning algorithm. Wuhan was selected as the experimental area, and Landsat images in 2005 and 2016 were used to extract six main types of change classes. Firstly, PCM (primitive co-occurrence matrix) and the minimum class certainty are used to ensure the high confidence of selected candidate set samples, and then the most informative samples are identified for classification from the candidate samples. To save computing costs, we adopt clustering method to reduce the self-relearning samples. Based on our experimental results, the self-relearning algorithm increases the final classification accuracy by approximately 2.5% (from 92.64% to 95.09%) in the case of using few initial training samples, providing a feasible solution for the multi-date change detection.","PeriodicalId":384253,"journal":{"name":"International Symposium on Multispectral Image Processing and Pattern Recognition","volume":"107 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":"124788623","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