中国科学院机构知识库网格系统: 检索

Vision Based Hand Gesture Recognition Using 3D Shape Context 期刊论文 OAI收割
IEEE/CAA Journal of Automatica Sinica, 2021, 卷号: 8, 期号: 9, 页码: 1600-1613
作者: Chen Zhu; Jianyu Yang; Zhanpeng Shao; Chunping Liu

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收藏 | 浏览/下载：36/0 | 提交时间：2021/09/03

3D shape context depth map hand shape segmentation hand gesture recognition human-computer interaction

Real-time segmentation of various insulators using generative adversarial networks 期刊论文 OAI收割
IET COMPUTER VISION, 2018, 卷号: 12, 期号: 5, 页码: 596-602
作者: Chang, Wenkai; Yang, Guodong; Yu, Junzhi; Liang, Zize

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收藏 | 浏览/下载：61/0 | 提交时间：2019/12/16

image segmentation insulators neural nets power engineering computing real-time pixel-level segmentation generative adversarial networks insulator segmentation algorithm cluttered background artificial thresholds compact end-to-end neural network visual saliency map proposed two-stage training segmentation quality

Large-scale 3D Semantic Mapping Using Stereo Vision 期刊论文 OAI收割
International Journal of Automation and Computing, 2018, 卷号: 15, 期号: 2, 页码: 194-206
作者: Yi Yang; Fan Qiu1 Hao Li; Lu Zhang; Mei-Ling Wang; Meng-Yin Fu

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收藏 | 浏览/下载：23/0 | 提交时间：2021/02/23

Semantic map stereo vision motion segmentation visual odometry simultaneous localization and mapping (SLAM).

Map segmentation for simultaneous localization and mapping in ruins 期刊论文 OAI收割
Advanced Robotics, 2016, 卷号: 30, 期号: 1, 页码: 1-14
作者:

收藏 | 浏览/下载：42/0 | 提交时间：2016/01/30

mapping SLAM map segmentation mobile robot

Low-rank representation for 3D hyperspectral images analysis from map perspective 期刊论文 OAI收割
signal processing, 2015, 卷号: 112, 页码: 27-33
作者: Yuan, Yuan; Fu, Min; Lu, Xiaoqiang

收藏 | 浏览/下载：33/0 | 提交时间：2015/03/18

3D hyperspectral data Semantic analysis Segmentation Markov random field (MRF) Low-rank representation (LRR) Maximum a posteriori (MAP) Remote sensing

AUTOMATIC SEGMENTATION OF URBAN POINT CLOUDS BASED ON THE GAUSSIAN MAP 期刊论文 OAI收割
PHOTOGRAMMETRIC RECORD, 2013, 卷号: 28, 期号: 144, 页码: 342-361
作者: Wang, Yinghui; Hao, Wen; Ning, Xiaojuan; Zhao, Minghua; Zhang, Jiulong

收藏 | 浏览/下载：30/0 | 提交时间：2015/08/12

Gaussian map mean shift clustering point cloud segmentation urban scene

Multi-focus image fusion algorithm based on adaptive PCNN and wavelet transform (EI CONFERENCE) 会议论文 OAI收割
International Symposium on Photoelectronic Detection and Imaging 2011: Advances in Imaging Detectors and Applications, May 24, 2011 - May 26, 2011, Beijing, China
Wu Z.-G.; Wang M.-J.; Han G.-L.

收藏 | 浏览/下载：88/0 | 提交时间：2013/03/25

Being an efficient method of information fusion image fusion has been used in many fields such as machine vision medical diagnosis military applications and remote sensing.In this paper Pulse Coupled Neural Network (PCNN) is introduced in this research field for its interesting properties in image processing including segmentation target recognition et al. and a novel algorithm based on PCNN and Wavelet Transform for Multi-focus image fusion is proposed. First the two original images are decomposed by wavelet transform. Then based on the PCNN a fusion rule in the Wavelet domain is given. This algorithm uses the wavelet coefficient in each frequency domain as the linking strength so that its value can be chosen adaptively. Wavelet coefficients map to the range of image gray-scale. The output threshold function attenuates to minimum gray over time. Then all pixels of image get the ignition. So the output of PCNN in each iteration time is ignition wavelet coefficients of threshold strength in different time. At this moment the sequences of ignition of wavelet coefficients represent ignition timing of each neuron. The ignition timing of PCNN in each neuron is mapped to corresponding image gray-scale range which is a picture of ignition timing mapping. Then it can judge the targets in the neuron are obvious features or not obvious. The fusion coefficients are decided by the compare-selection operator with the firing time gradient maps and the fusion image is reconstructed by wavelet inverse transform. Furthermore by this algorithm the threshold adjusting constant is estimated by appointed iteration number. Furthermore In order to sufficient reflect order of the firing time the threshold adjusting constant is estimated by appointed iteration number. So after the iteration achieved each of the wavelet coefficient is activated. In order to verify the effectiveness of proposed rules the experiments upon Multi-focus image are done. Moreover comparative results of evaluating fusion quality are listed. The experimental results show that the method can effectively enhance the edge details and improve the spatial resolution of the image. 2011 SPIE.

Salient Region Detection in High Resolution Remote Sensing Images 会议论文 OAI收割
Shanghai, China, 14-15 May 2010
作者: Junge Sun; Yunhong Wang; Zhaoxiang Zhang; Yiding Wang

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收藏 | 浏览/下载：15/0 | 提交时间：2017/02/09

Pre-segmentation Remote Sensing Saliency Map

行为序列分割与聚类方法研究 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, 2008
作者: 吴晓婕

收藏 | 浏览/下载：98/0 | 提交时间：2015/09/02

行为分析行为序列分割无监督行为聚类时空显著特征 MAP-MRF behavior analysis behavior sequence segmentation unsupervised behavior clustering spatio-temporal salient feature MAP-MRF

集成电路图像有源区分割方法 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, 2006
作者: 刘佳璐

收藏 | 浏览/下载：54/0 | 提交时间：2015/09/02

集成电路图像图像分割分水线变换 MAP算法图像分解纹理分割 IC image image segmentation object extraction watershed transform MAP algorithm image decomposition