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Scale-variant traffic sign detection 变尺度交通标志检测
... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging Pub Date : 2019-07-31 DOI: 10.1117/12.2540462
Sijuan Wang, Zhiqiang You
{"title":"Scale-variant traffic sign detection","authors":"Sijuan Wang, Zhiqiang You","doi":"10.1117/12.2540462","DOIUrl":"https://doi.org/10.1117/12.2540462","url":null,"abstract":"Accurate detection of traffic signs is vital in many applications, such as driving assistance systems and autonomous vehicles. However, since urban scenes are often cluttered with confusing objects, the signs may appear scale-variant (from large to small sizes) in a single image or image sequences of traffic scenes, when autonomous vehicles are moving fast; traffic signs therefore need to be detected early and accurately when they appear small in the images, and tracked for timely recognition and decision.","PeriodicalId":90079,"journal":{"name":"... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging","volume":"33 1","pages":"111980O - 111980O-5"},"PeriodicalIF":0.0,"publicationDate":"2019-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89084793","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
Multi-scale binary geometric feature description and matching for accurate registration of point clouds 点云精确配准的多尺度二元几何特征描述与匹配
... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging Pub Date : 2019-07-31 DOI: 10.1117/12.2540407
Siwen Quan, Jie Ma, Fan Feng, Kun Yu
{"title":"Multi-scale binary geometric feature description and matching for accurate registration of point clouds","authors":"Siwen Quan, Jie Ma, Fan Feng, Kun Yu","doi":"10.1117/12.2540407","DOIUrl":"https://doi.org/10.1117/12.2540407","url":null,"abstract":"Point cloud registration in military scenarios is pivotal to automatic object reconstruction and recognition. This paper proposes 1) a multi-scale binary feature representation called mLoVS (multi-scale local voxelized structure) and 2) a “min-pooling” based feature matching technique for accurate registration of tank point clouds. The key insight of our method is that traditional fixed-scale feature matching methods either suffer from limited shape information or data missing caused by occlusion, while the multi-scale way provides a flexible matching choice. In addition, the binary nature of our feature representation can alleviate the increased time budget required by multi-scale feature matching. Experiments on several sets of tank point clouds confirm the effectiveness and overall superiority of our method.","PeriodicalId":90079,"journal":{"name":"... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging","volume":"34 1","pages":"111980L - 111980L-5"},"PeriodicalIF":0.0,"publicationDate":"2019-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73540510","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
Research on the influence of node deployment in cluster for modeling efficiency 研究集群中节点部署对建模效率的影响
... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging Pub Date : 2019-07-31 DOI: 10.1117/12.2540983
Yuxiang Liu, Yang Peng, Xin Long, Maojun Zhang
{"title":"Research on the influence of node deployment in cluster for modeling efficiency","authors":"Yuxiang Liu, Yang Peng, Xin Long, Maojun Zhang","doi":"10.1117/12.2540983","DOIUrl":"https://doi.org/10.1117/12.2540983","url":null,"abstract":"With the rapid development of oblique photography (OP) in recent years, the accuracy of reality modeling has increased, which has led to a surge in computational complexity. To solve the problem, a lot of reality modeling software adopts the strategy of cluster parallel computing for modeling. In this paper, the regression analysis method is used to study the influence of the configuration of the compute nodes in the cluster, which aims at improving the computational efficiency of the cluster for the 3D reconstruction task. Furthermore, the M/M/S queuing model in queuing theory is used to model the multi-task assignment of the cluster, and the mathematical model between compute nodes and performance of the cluster is established, which achieves the effective quantitative evaluation of the cluster computing efficiency. Experiments show that the CPU performance of the compute nodes is the most critical hardware factor affecting the efficiency of the cluster.","PeriodicalId":90079,"journal":{"name":"... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging","volume":"25 1","pages":"111980U - 111980U-6"},"PeriodicalIF":0.0,"publicationDate":"2019-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74226137","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
Second-order convolutional network for crowd counting 用于人群计数的二阶卷积网络
... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging Pub Date : 2019-07-31 DOI: 10.1117/12.2540362
Luyang Wang, Qiang Zhai, B. Yin, Hazrat Bilal
{"title":"Second-order convolutional network for crowd counting","authors":"Luyang Wang, Qiang Zhai, B. Yin, Hazrat Bilal","doi":"10.1117/12.2540362","DOIUrl":"https://doi.org/10.1117/12.2540362","url":null,"abstract":"Single image crowd counting remains challenging primarily due to various issues, such as large scale variations, perspective and non-uniform crowd distribution. In this paper, we propose a novel architecture referred to Second-Order Convolutional Network (SOCN) to deal with this task from the perspective of improving the feature transformation capability of the network. The proposed SOCN applies a convolutional neural network as the backbone. We introduce three cascaded second-order blocks located behind the backbone to augment the family of transformation operations and increase the nonlinearity of the network, which can extract multi-scale and discriminative features. Furthermore, we design a context attention module (CAM) including dilated convolutions to assign weights to the score map of each second-order block for the purpose that the features which contribute to counting can be highlighted. We conduct various experiments on ShanghaiTeach1 and UCF_CC_502 datasets, and the results demonstrate the effectiveness of our method.","PeriodicalId":90079,"journal":{"name":"... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging","volume":"65 1","pages":"111980T - 111980T-6"},"PeriodicalIF":0.0,"publicationDate":"2019-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75312820","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: 78
Radio frequency sensing based environmental monitoring technology 基于射频传感的环境监测技术
... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging Pub Date : 2019-07-31 DOI: 10.1117/12.2541098
Xianxun Zhu, Yang Zhang, Zhiyang Zhao, Jiancun Zuo
{"title":"Radio frequency sensing based environmental monitoring technology","authors":"Xianxun Zhu, Yang Zhang, Zhiyang Zhao, Jiancun Zuo","doi":"10.1117/12.2541098","DOIUrl":"https://doi.org/10.1117/12.2541098","url":null,"abstract":"In recent years, the research and development of environmental action monitoring at home and abroad are in full swing. RF sensing has achieved a series of breakthrough research results based on its advantages in the application of behavioral identification, positioning and target monitoring. This paper first introduces the application scenarios of RF-awared environment monitoring and compares them with traditional environmental monitoring technologies. Then it analyzes the basic principles of RF sensing technology and the applications of signal acquisition method, feature extraction method, fingerprint database establishment method and machine recognition method in behavior recognition, positioning and target monitoring. Finally, the limitations of the current research and the future development directions are pointed out.","PeriodicalId":90079,"journal":{"name":"... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging","volume":"73 1","pages":"111980Y - 111980Y-5"},"PeriodicalIF":0.0,"publicationDate":"2019-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85741481","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
Flight trajectory clustering based on a novel distance from a point to a segment set 基于点到段集新距离的飞行轨迹聚类
... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging Pub Date : 2019-07-31 DOI: 10.1117/12.2540415
Yingchao Xiao, Yuanyuan Ma, Hui Ding, Qiucheng Xu
{"title":"Flight trajectory clustering based on a novel distance from a point to a segment set","authors":"Yingchao Xiao, Yuanyuan Ma, Hui Ding, Qiucheng Xu","doi":"10.1117/12.2540415","DOIUrl":"https://doi.org/10.1117/12.2540415","url":null,"abstract":"The measurement of trajectory distance is the base of trajectory clustering. To deal with the flight trajectory clustering in air traffic, a novel method is proposed in this paper to measure the flight trajectory distance. This method views the trajectory as a set of segments, whose end points are trajectory points, and it measures the distance from a trajectory point to another trajectory, and thus presents the distance definition of trajectories. Based on the calculated distance matrix, spectral clustering algorithm is adopted to cluster flight trajectories. The experiment on actual flight trajectory data verifies the effectiveness of the proposed method.","PeriodicalId":90079,"journal":{"name":"... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging","volume":"1 1","pages":"111980E - 111980E-6"},"PeriodicalIF":0.0,"publicationDate":"2019-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89406901","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: 3
SE-dual path networks combined with a navigator for fine-grained classification se -双路径网络与用于细粒度分类的导航器相结合
... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging Pub Date : 2019-07-31 DOI: 10.1117/12.2540751
Liu Yang, Jin Zhong
{"title":"SE-dual path networks combined with a navigator for fine-grained classification","authors":"Liu Yang, Jin Zhong","doi":"10.1117/12.2540751","DOIUrl":"https://doi.org/10.1117/12.2540751","url":null,"abstract":"Recognizing fine-grained categories is difficult due to the challenges of discriminative region localization and fine-grained feature learning. To handle this circumstance, we propose a novel model termed SDN-Net for SE-DPN-Navigator Networks, which consists of DPN (Dual Path Networks), SE-blocks (Squeeze-and-Excitation Blocks) and a Navigator. DPN shares common features while maintaining the flexibility to explore new features. Moreover, we add SE-blocks into DPN to make up the SE-DPN which acts as a feature extractor of the proposed model, SE-blocks helps the model learn to use global information to selectively emphasize informative features and suppress less useful ones. We also use a Navigator to help the model to detect most informative regions without extra bounding box/part annotations. Our model can be trained end-to-end. With the great cooperation between these three components, we achieve state-of-the-art performance on two publicly available fine-grained recognition datasets (CUB-200-2001 and Stanford Cars). Besides, We have done ablation studies and confirmed the effectiveness of each components in the proposed model.","PeriodicalId":90079,"journal":{"name":"... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging","volume":"23 1","pages":"1119809 - 1119809-6"},"PeriodicalIF":0.0,"publicationDate":"2019-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82056492","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
Heart beat classification and matching recognition based on hierarchical dynamic time warping 基于层次动态时间规整的心跳分类与匹配识别
... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging Pub Date : 2019-07-31 DOI: 10.1117/12.2540503
Si Liu, Enqi Zhan, Yang Wang, Jianbin Zheng
{"title":"Heart beat classification and matching recognition based on hierarchical dynamic time warping","authors":"Si Liu, Enqi Zhan, Yang Wang, Jianbin Zheng","doi":"10.1117/12.2540503","DOIUrl":"https://doi.org/10.1117/12.2540503","url":null,"abstract":"Automatic heartbeat classification is an important technique to assist doctors to identify ectopic heartbeats in long-term Holter recording. In this paper, the ECG signal in the MIT-BIH database is filtered first, and then the R-peak detection is performed by the classical method named Pan-Tompkin. The first 100 and the last 150 data points of the R-peak are as chosen as matching signals. Following the recommendation of the Advancement of Medical Instrumentation (AAMI), all the heartbeat samples of MIT-BIH could be grouped into four classes, such as normal or bundle branch block (i.e., class N), supraventricular ectopic (i.e., class S), ventricular ectopic (i.e., class V) and fusion of ventricular and normal (i.e., class F). The division of training and testing data complies with the inter-patient schema. The ECG signals are matched and recognized as specific cardiac diseases using curve fitting and the hierarchical dynamic time warping (DTW) algorithm.Experimental results show that the average classification accuracy of the proposed DTW algorithm is 92.51%, outperforming the other methods. The sensitivities for the classes N, S, V and F are 98.94%, 99.06%, 96.77% and 93.81% respectively, and the corresponding positive predictive values are 93.94%, 91.18%, 88.24% and 96.67%, respectively.","PeriodicalId":90079,"journal":{"name":"... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging","volume":"46 1","pages":"111980J - 111980J-6"},"PeriodicalIF":0.0,"publicationDate":"2019-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90713307","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
PolSAR image classification based on complex-valued convolutional neural network and Markov random field 基于复值卷积神经网络和马尔可夫随机场的PolSAR图像分类
... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging Pub Date : 2019-07-31 DOI: 10.1117/12.2540913
Xianxiang Qin, Wangsheng Yu, Peng Wang, Tianping Chen, H. Zou
{"title":"PolSAR image classification based on complex-valued convolutional neural network and Markov random field","authors":"Xianxiang Qin, Wangsheng Yu, Peng Wang, Tianping Chen, H. Zou","doi":"10.1117/12.2540913","DOIUrl":"https://doi.org/10.1117/12.2540913","url":null,"abstract":"Recently, a complex-valued convolutional neural network (CV-CNN) has been used for the classification of polarimetric synthetic aperture radar (PolSAR) images, and has shown superior performance to most traditional algorithms. However, it usually yields unreliable results for the pixels distributing within heterogeneous regions or the edge areas. To solve this problem, in this paper, an edge reassigning scheme based on Markov random field (MRF) is considered to combine with the CV-CNN. In this scheme,both the polarimetric statistical property and label context information are employed. The experiments performed on a benchmark PolSAR image of Flevoland has demonstrated the superior performance of the proposed algorithm.","PeriodicalId":90079,"journal":{"name":"... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging","volume":"4 1","pages":"111980B - 111980B-7"},"PeriodicalIF":0.0,"publicationDate":"2019-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87771204","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
An efficient approach combined with harmonic and shift invariance for piano music multi-pitch detection 一种结合谐波和移不变性的钢琴音乐多音高检测方法
... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging Pub Date : 2019-07-31 DOI: 10.1117/12.2540410
Kaiyuan Deng, Gang Liu, Yuzhi Huang
{"title":"An efficient approach combined with harmonic and shift invariance for piano music multi-pitch detection","authors":"Kaiyuan Deng, Gang Liu, Yuzhi Huang","doi":"10.1117/12.2540410","DOIUrl":"https://doi.org/10.1117/12.2540410","url":null,"abstract":"We propose an efficiently discriminative method that using AdaBoost as binary classifiers combined with musical signal properties for polyphonic piano music multi-pitch detection. As features, we use spectral components of multiples and divisions of notes’ fundamental frequency, which can reduce note’s feature redundancy compared with full spectrum. For the frame-level multi-pitch detection, the features of notes have adjacent pitches are similar (we called it shift invariance), which inspires us to use one binary classifier to detect those notes’ pitch. In a certain extent, those adjacent notes improves the classifier’s generalizability. In the post-processing stage, to combine with time property, we concatenate each notes’ several continuously frame-level predictions as their new features for final pitch detection. In conclusion, the proposed method with fewer classifiers achieves better performance compared with other methods.","PeriodicalId":90079,"journal":{"name":"... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging","volume":"114 1","pages":"111980P - 111980P-6"},"PeriodicalIF":0.0,"publicationDate":"2019-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76688212","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
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