2017 2nd International Conference on Image, Vision and Computing (ICIVC)最新文献

Efficient HD video and image salient object detection with hierarchical boolean map approach 基于层次布尔映射方法的高清视频和图像显著目标检测

2017 2nd International Conference on Image, Vision and Computing (ICIVC) Pub Date : 2017-06-02 DOI: 10.1109/ICIVC.2017.7984448

Bo Xiao, Bin Wang

引用次数: 2

Natural scene text detection based on SWT, MSER and candidate classification 基于SWT、MSER和候选分类的自然场景文本检测

2017 2nd International Conference on Image, Vision and Computing (ICIVC) Pub Date : 2017-06-02 DOI: 10.1109/ICIVC.2017.7984452

L. Guan, Jizheng Chu

引用次数: 15

Tracking skeletal fusion feature for one shot learning gesture recognition 跟踪骨骼融合特征，一次性学习手势识别

2017 2nd International Conference on Image, Vision and Computing (ICIVC) Pub Date : 2017-06-02 DOI: 10.1109/ICIVC.2017.7984545

Li Xuejiao, S. Yongqing

引用次数: 2

Grid clustering analysis the big data of spectrum by deep learning 网格聚类通过深度学习对频谱大数据进行分析

2017 2nd International Conference on Image, Vision and Computing (ICIVC) Pub Date : 2017-06-02 DOI: 10.1109/ICIVC.2017.7984705

Chen Shuxin, Sun Weimin

{"title":"Grid clustering analysis the big data of spectrum by deep learning","authors":"Chen Shuxin, Sun Weimin","doi":"10.1109/ICIVC.2017.7984705","DOIUrl":"https://doi.org/10.1109/ICIVC.2017.7984705","url":null,"abstract":"With the Internet plus big data science information era increasing rapidly. To seek special and unknown objects is the human exploration of the mystery of the universe to pursue the goal in the universe. The spectrum by the big data mining are the fairly complex data, the dimension is high, and the correlation between the dimensions is not strong, but it is easy to introduce noise or the missing data. So it is much more difficult to deal with metering data. This article investigates the LAMOST data release star spectrum based on the high resolution spectral parameters. The RFITSIO software package of R language is used to graphically analyze the big data of the spectrum. The deep learning analysis extracts the information from the large data with finding the new knowledge and the unknown outlier data. Now the FITS format spectral large data information rise to 107 levels of data. Since the big data is imported with a large amount of redundant information, the full spectrum signal of the star spectrum making the full use of Multivariable Statistical Analysis to cluster clustering data characterized by line index. Using the Lick line index as the spectral feature, the spectral data are clustered by the K-means mean algorithm of deep learning. Experiments show that the data with strong physical correlation are valid and fast, the clustering outlier analysis of the big data feature in the spectral survey are completed with the characteristics of the data.","PeriodicalId":181522,"journal":{"name":"2017 2nd International Conference on Image, Vision and Computing (ICIVC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129908218","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

Algorithm of fingerprint extraction and implementation based on OpenCV 基于OpenCV的指纹提取算法及实现

2017 2nd International Conference on Image, Vision and Computing (ICIVC) Pub Date : 2017-06-02 DOI: 10.1109/ICIVC.2017.7984539

Yue Yaru, Zhu Jialin

引用次数: 5

An accelerated matching algorithm for SIFT-like features 类sift特征的加速匹配算法

2017 2nd International Conference on Image, Vision and Computing (ICIVC) Pub Date : 2017-06-02 DOI: 10.1109/ICIVC.2017.7984527

Hongyan Zhang, J. Luo, Zihao Wang, Long Ma, Yi-Fan Niu

引用次数: 0

Integrating saliency and ResNet for airport detection in large-size remote sensing images 基于显著性和ResNet的大尺寸遥感图像机场检测

2017 2nd International Conference on Image, Vision and Computing (ICIVC) Pub Date : 2017-06-02 DOI: 10.1109/ICIVC.2017.7984451

Tinghe Zhu, Yuhui Li, Qiankun Ye, H. Huo, T. Fang

{"title":"Integrating saliency and ResNet for airport detection in large-size remote sensing images","authors":"Tinghe Zhu, Yuhui Li, Qiankun Ye, H. Huo, T. Fang","doi":"10.1109/ICIVC.2017.7984451","DOIUrl":"https://doi.org/10.1109/ICIVC.2017.7984451","url":null,"abstract":"Automatic airport detection has received great attention due to the importance of airports in both military and civilian uses. This paper focuses on automatic airport detection in large-size remote sensing images under a two-step object detection framework. In the first step, both geometrical saliency and local entropy saliency are improved to find more accurate ROIs for detecting airports in large-size remote sensing images. The geometrical saliency is based on line features of airports, and line segment detector (LSD) is used to detect line segments. Then, line group weighted saliency map is generated after line connection, and local entropy saliency map is created by further considering the entropy difference between neighbor pixels. Finally, ROIs can be obtained by combining these two saliency maps. The improved saliencies could make airports prominent as a whole instead of many separated parts, and it could find more accurate ROIs than traditional saliency methods. In the second step, deep residual learning network (ResNet) is used to determine whether a ROI should be labeled as an airport. The unobvious features of airports could be further extracted by ResNet, which greatly promotes the robustness of the proposed two-step object detection method. The experiments on large-size remote sensing images have shown that the proposed method could reduce false alarms greatly compared with both the traditional two-way saliency (TWS) airport detection method and the state-of-the-art object detection method of single shot multi-box detector (SSD).","PeriodicalId":181522,"journal":{"name":"2017 2nd International Conference on Image, Vision and Computing (ICIVC)","volume":"46 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121975560","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: 15

Unconstrained face detection based on cascaded Convolutional Neural Networks in surveillance video 基于级联卷积神经网络的监控视频无约束人脸检测

2017 2nd International Conference on Image, Vision and Computing (ICIVC) Pub Date : 2017-06-02 DOI: 10.1109/ICIVC.2017.7984516

Junjie Li, Saleem Karmoshi, Ming Zhu

{"title":"Unconstrained face detection based on cascaded Convolutional Neural Networks in surveillance video","authors":"Junjie Li, Saleem Karmoshi, Ming Zhu","doi":"10.1109/ICIVC.2017.7984516","DOIUrl":"https://doi.org/10.1109/ICIVC.2017.7984516","url":null,"abstract":"With the popularity of surveillance video, face detection in surveillance video has become a popular and important topic. Face detection in surveillance video plays an important role in many popular applications such as: personal identification, crowd analysis, database establishment, and abnormal event detection. This paper proposes an unconstrained face detection method for surveillance video, which is not influenced by factors such as face location, expression, posture, scale, and lighting conditions. First, the detection area is initially extracted from the video frame using the improved foreground extraction and skin color detection. Next, we then use the multi-scale sliding window and the cascaded Convolutional Neural Network (CNN) designed in this paper to detect faces. This cascaded network consists of two CNN networks: the first network filters out most of the background area while ensuring the running speed of the whole system and the recall rate of the face, while the second network guarantees the accuracy of the overall system. Finally, we set up a database for the experiment which contained samples from the actual surveillance video. The results of our experiment suggest that the proposed method can obtain good results on unconstrained face detection in surveillance video and can also achieve satisfactory detection speed.","PeriodicalId":181522,"journal":{"name":"2017 2nd International Conference on Image, Vision and Computing (ICIVC)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122181173","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

Application of data mining methods in diabetes prediction 数据挖掘方法在糖尿病预测中的应用

2017 2nd International Conference on Image, Vision and Computing (ICIVC) Pub Date : 2017-06-02 DOI: 10.1109/ICIVC.2017.7984706

Messan Komi, Jun Li, Yongxin Zhai, Xianguo Zhang

引用次数: 77

Enhance deep learning performance in face recognition 增强深度学习在人脸识别方面的性能

2017 2nd International Conference on Image, Vision and Computing (ICIVC) Pub Date : 2017-06-02 DOI: 10.1109/ICIVC.2017.7984554

Ze Lu, Xudong Jiang, A. Kot

引用次数: 21