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• 自动化研究所 [7]
• 长春光学精密机械与物... [3]
• 遥感与数字地球研究所 [3]
• 地理科学与资源研究所 [1]
• 计算技术研究所 [1]
• 中国科学院大学 [1]
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• OAI收割 [18]
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• 2019 [1]
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浏览/检索结果: 共19条，第1-10条 帮助 条数/页： 5101520253035404550556065707580859095100 排序方式： 请选择题名升序题名降序提交时间升序提交时间降序作者升序作者降序发表日期升序发表日期降序 Infrared LSS-Target Detection Via Adaptive TCAIE-LGM Smoothing and Pixel-Based Background Subtraction 期刊论文  OAI收割 Photonic Sensors, 2019, 卷号: 9, 期号: 2, 页码: 179-188 作者:  Y.F.Wu;  Y.J.Wang;  P.X.Liu;  H.Y.Luo;  B.Y.Cheng   |  收藏  |  浏览/下载：34/0  |  提交时间：2020/08/24 Small target detection,L0 smoothing,texture complexity,information,entropy,pixel-based adaptive segmentation,search,Instruments & Instrumentation,Optics   Ground-Based Cloud Detection Using Graph Model Built Upon Superpixels 期刊论文  OAI收割 IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2017, 卷号: 14, 期号: 5, 页码: 719-723 作者:  Shi, Cunzhao;  Wang, Yu;  Wang, Chunheng;  Xiao, Baihua   |  收藏  |  浏览/下载：23/0  |  提交时间：2017/09/12 Cloud Detection  Color  Graph Model (Gm)  Segmentation  Superpixel  Texture   Reliable Fusion of Stereo Matching and Depth Sensor for High Quality Dense Depth Maps 期刊论文  OAI收割 SENSORS, 2015, 卷号: 15, 期号: 8, 页码: 20894-20924 作者:  Liu, Jing;  Li, Chunpeng;  Fan, Xuefeng;  Wang, Zhaoqi   |  收藏  |  浏览/下载：24/0  |  提交时间：2019/12/13 stereo matching  depth sensor  multiscale pseudo-two-layer model  segmentation  texture constraint  fusion move   Staging of cervical cancer based on tumor heterogeneity characterized by texture features on F-18-FDG PET images 期刊论文  OAI收割 PHYSICS IN MEDICINE AND BIOLOGY, 2015, 卷号: 60, 期号: 13, 页码: 5123-5139 作者:  Mu, Wei;  Chen, Zhe;  Liang, Ying;  Shen, Wei;  Yang, Feng 收藏  |  浏览/下载：40/0  |  提交时间：2015/09/17 cervical cancer  PET/CT images  tumor segmentation  texture analysis  cancer staging   MCA aided geodesic active contours for image segmentation with textures 期刊论文  OAI收割 PATTERN RECOGNITION LETTERS, 2014, 卷号: 45, 页码: 235-243 作者:  Shan, Hao;  He, Changtao;  Wang, Na 收藏  |  浏览/下载：43/0  |  提交时间：2015/06/09 Morphological component analysis\diversity  Image segmentation  Sparse representation  Curvelets  Geodesic active contours  Texture separation   LS-SVM-based image segmentation using pixel color-texture descriptors 期刊论文  OAI收割 PATTERN ANALYSIS AND APPLICATIONS, 2014, 卷号: 17, 期号: 2, 页码: 341-359 Yang, Hong-Ying; Zhang, Xian-Jin; Wang, Xiang-Yang   |  收藏  |  浏览/下载：23/0  |  提交时间：2014/12/16 Image segmentation  Least squares support vector machine  Human visual attention  Local texture content  Arimoto entropy thresholding   Object-oriented segmentation of remote sensing image based on texture analysis 会议论文  OAI收割 Proceedings of the 2013 the International Conference on Remote Sensing, Environment and Transportation Engineering (Rsete 2013) Wang, Yanhong; Cheng, Bo; Wang, Guizhou; You, Shucheng 收藏  |  浏览/下载：25/0  |  提交时间：2014/12/07 Texture feature  High-resolution  Object-oriented  Image segmentation  STATISTICS   FUSING SPECTRAL AND TEXTURE INFORMATION FOR COLLAPSED BUILDINGS DETECTION IN AIRBORNE IMAGE 会议论文  OAI收割 2012 Ieee International Geoscience and Remote Sensing Symposium, New York Li, L.; Zhang, B.; Wu, Y. 收藏  |  浏览/下载：21/0  |  提交时间：2014/12/07 Collapsed Buildings  Airborne Images  Fusion  Texture  Spectral  DAMAGED BUILDINGS  SEGMENTATION  EARTHQUAKE   Aurora image segmentation by combining patch and texture thresholding 期刊论文  OAI收割 computer vision and image understanding, 2011, 卷号: 115, 期号: 3, 页码: 390-402 作者:  Gao, Xinbo;  Fu, Rong;  Li, Xuelong;  Tao, Dacheng;  Zhang, Beichen 收藏  |  浏览/下载：24/0  |  提交时间：2015/10/20 Image segmentation  Feature extraction  LBP  Aurora  Texture segmentation  Otsu  Patch segmentation   TextureGrow: Object recognition and segmentation with limit prior knowledge (EI CONFERENCE) 会议论文  OAI收割 2011 International Conference on Network Computing and Information Security, NCIS 2011, May 14, 2011 - May 15, 2011, Guilin, Guangxi, China Yao Z.; Han Q. 收藏  |  浏览/下载：29/0  |  提交时间：2013/03/25 In this paper we present a new method for automatically visual recognition and semantic segmentation of photographs. Our automatically and rapidly approach based on Cellular Automation. Most of the analysis and description of recognition and segmentation are based on statistical or structural properties of this attribute  most of them need plenty of samples and prior Knowledge. In this paper  within a few evident samples (not too many)  we can first get the texture feature of each component and the structures  then select the approximately location randomly of the objects or patches of them  then we use the Cellular Automata algorithm to "grow" based on texture of different objects. The grow progress will stop When texture grow to the boundary. By this steps a new method is found which allow us use very few samples and low lever experience and get a rapidly and accuracy way to recognize and segment objects. We found that this new propose gives competitive results with limited experience and samples. 2011 IEEE.