机构

• 长春光学精密机械与物... [4]
• 自动化研究所 [3]
• 西安光学精密机械研究... [2]
• 计算技术研究所 [1]

采集方式

• OAI收割 [10]

内容类型

• 期刊论文 [5]
• 会议论文 [4]
• 学位论文 [1]

发表日期

• 2023 [1]
• 2022 [1]
• 2017 [3]
• 2011 [3]
• 2008 [1]
• 2006 [1]
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浏览/检索结果: 共10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 Spectral-Spatial Attention Rotation-Invariant Classification Network for Airborne Hyperspectral Images 期刊论文  OAI收割 DRONES, 2023, 卷号: 7, 期号: 4 作者:  Shi, Yuetian;  Fu, Bin;  Wang, Nan;  Cheng, Yinzhu;  Fang, Jie   |  收藏  |  浏览/下载：41/0  |  提交时间：2023/05/29 airborne hyperspectral image  hyperspectral image classification  rotation-invariant  local spatial feature enhancement  convolutional neural network  attention mechanism  lightweight feature enhancement   Rotation-Invariant Attention Network for Hyperspectral Image Classification 期刊论文  OAI收割 IEEE Transactions on Image Processing, 2022, 卷号: 31, 页码: 4251-4265 作者:  Zheng, Xiangtao;  Sun, Hao;  Lu, Xiaoqiang;  Xie, Wei   |  收藏  |  浏览/下载：31/0  |  提交时间：2022/07/21 Hyperspectral image classification  convolutional neural network  rotation-invariant network  spectralspatial feature extraction  attention mechanism   Ordinal pyramid coding for rotation invariant feature extraction 期刊论文  OAI收割 NEUROCOMPUTING, 2017, 卷号: 242, 期号: 242, 页码: 150-160 作者:  Wang, Guoli;  Fan, Bin;  Zhou, Zhili;  Pan, Chunhong   |  收藏  |  浏览/下载：18/0  |  提交时间：2017/05/09 Rotation Invariant  Ordinal Pyramid Pooling  Fisher Vector  Feature Extraction   Feature Extraction by Rotation-Invariant Matrix Representation for Object Detection in Aerial Image 期刊论文  OAI收割 IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2017, 卷号: 14, 期号: 6, 页码: 851-855 作者:  Wang, Guoli;  Wang, Xinchao;  Fan, Bin;  Pan, Chunhong   |  收藏  |  浏览/下载：32/0  |  提交时间：2017/05/09 Feature Extraction  Fisher Vector  Object Detection  Ring Pyramid Pooling (Rpp)  Rotation-invariant Matrix (Rim)   A Two-Phase Improved Correlation Method for Automatic Particle Selection in Cryo-EM 期刊论文  OAI收割 IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS, 2017, 卷号: 14, 期号: 2, 页码: 316-325 作者:  Zhang, Fa;  Chen, Yu;  Ren, Fei;  Wang, Xuan;  Liu, Zhiyong   |  收藏  |  浏览/下载：36/0  |  提交时间：2019/12/12 Particle selection  feature-based  template-matching  rotation-invariant feature  correlation score functions   图像特征匹配研究 学位论文  OAI收割 工学博士, 中国科学院自动化研究所: 中国科学院研究生院, 2011 作者:  樊彬 收藏  |  浏览/下载：43/0  |  提交时间：2015/09/02 特征描述  特征匹配  重复性纹理  直线匹配  点线不变量  旋转不变性  光照不变性  feature description  feature matching  repetitive patterns  line matching  line-point invariant  rotation invariance  illumination invariance   Rotation and scaling invariant feature lines for image matching (EI CONFERENCE) 会议论文  OAI收割 2011 International Conference on Mechatronic Science, Electric Engineering and Computer, MEC 2011, August 19, 2011 - August 22, 2011, Jilin, China 作者:  Wang Y.;  Wang Y.;  Wang Y.;  Wang Y.;  Wang Y. 收藏  |  浏览/下载：25/0  |  提交时间：2013/03/25 Image matching has been one of the most fundamental issues computer vision over the decades. In this paper we propose a novel method based on making use of feature lines in order to achieve more robust image matching. The feature lines have the properties of rotation and scaling invariance  coined RIFLT(Rotation invariant feature line transform). Experimental results demonstrate the effectiveness and efficiency of the proposed method. Compare with the famous powerful algorithm Scale Invariant Feature Transform(SIFT)  the proposed method is more insensitive to noise. And for certain sequence of images  which contain clear lines  the proposed method is more efficiency. Using the feature lines obtained by our method  it is possible to matching two scene images with different rotation angles  scale and light distort. 2011 IEEE.   Approach for detecting crowd panic behavior based on fluid kinematic features and entropy (EI CONFERENCE) 会议论文  OAI收割 International Workshop on Advanced Computational Intelligence and Intelligent Informatics, IWACIII 2011, November 19, 2011 - November 23, 2011, Suzhou, China 作者:  Li Y. 收藏  |  浏览/下载：25/0  |  提交时间：2013/03/25 Crowd panic behavior detection is an important task in video analysis and event recognition  whose purpose is to detect when the panic behavior happened and alarming the abnormal event timely. In this paper  the crowd is regard as a fluid  and the crowd motion is described by four fluid kinematic features (divergence  vorticity  gradient tensor invariant and rotation tensor invariant). To discriminate the panic event from normal crowd behavior  an information entropy is calculated as a high level feature based on the fluid kinematic features. Experimental results show that the entropy raised dramatically once a panic event happened.   Mean shift tracking combining SIFT (EI CONFERENCE) 会议论文  OAI收割 2008 9th International Conference on Signal Processing, ICSP 2008, October 26, 2008 - October 29, 2008, Beijing, China 作者:  Xue C. 收藏  |  浏览/下载：65/0  |  提交时间：2013/03/25 A novel visual tracking algorithm to cope with occlusion and scale variation is proposed. This method combines mean shift and SIFT algorithm to track object. SIFT algorithm is invariant to rotation  translation and scale variation. But it is a timeconsuming algorithm. The wasting time is related to image size. So the proposed algorithm first adopts mean shift to initially locate object position  then SIFT operator is used to detect features in object area and model area  lastly  the proposed method matches features in these two areas and calculates the relationship between them using affine transform. According to affine transform parameters  the state of object can be adjusted in time. In order to reduce process time  an improved feature matching algorithm is proposed in this paper. Experiments show that the proposed algorithm deals with occlusion successfully and can adjust object size in time. 2008 IEEE.   A novel starting-point-independent wavelet coefficient shape matching (EI CONFERENCE) 会议论文  OAI收割 ICO20: Optical Information Processing, August 21, 2005 - August 26, 2005, Changchun, China Hu S.; Zhu M.; Wu C.; Song H.-J. 收藏  |  浏览/下载：17/0  |  提交时间：2013/03/25 In many computer vision tasks  in order to improve the accuracy and robustness to the noise  wavelet analysis is preferred for the natural multi-resolution property. However  the wavelet representation suffers from the dependency of the starting point of the sampled contour. For overcoming the problem that the wavelet representation depends on the starting point of the sampled contour  the Zernike moments are introduced  and a novel Starting-Point-lndependent wavelet coefficient shape matching algorithm is presented. The proposed matching algorithm firstly gains the object contours  and give the translation and scale invariant object shape representation. The object shape representation is converted to the dyadic wavelet representation by the wavelet transform. And then calculate the Zernike moments of wavelet representation in different scales. With respect to property of rotation invariant of Zernike moments  consider the Zernike moments as the feature vector to calculate the dissimilarity between the object and template image  which overcoming the problem of dependency of starting point. The experimental results have proved the proposed algorithm to be efficient  precise  and robust.