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	Learning to Transform Service Instructions into Actions with Reinforcement Learning and Knowledge Base 期刊论文 OAI收割 International Journal of Automation and Computing, 2018, 卷号: 15, 期号: 5, 页码: 582-592 作者: Meng-Yang Zhang; Guo-Hui Tian; Ci-Ci Li; Jing Gong \| 收藏 \| 浏览/下载：6/0 \| 提交时间：2021/02/23 Natural language robot knowledge base reinforcement learning object state.
	Spatial structure of neuronal receptive field in awake monkey secondary visual cortex (V2) 期刊论文 OAI收割 PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2016, 卷号: 113, 期号: 7, 页码: 1913-1918 作者: Liu, L; She, L; Chen, M; Liu, TY; Lu, HDD 收藏 \| 浏览/下载：46/0 \| 提交时间：2016/09/14 AREA V2 FUNCTIONAL-ORGANIZATION STRIATE CORTEX MACAQUE MONKEY NATURAL IMAGES INDEPENDENT COMPONENTS OBJECT RECOGNITION PRIMATE AREA-18 SIMPLE CELLS FILTERS
	自然场景图像语义识别及标注算法研究学位论文 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, 2010 作者: 江爱文收藏 \| 浏览/下载：72/0 \| 提交时间：2015/09/02 自然场景图像语义分类视觉目标类识别图像多标签标注基于内容的图像检索图像理解 Natural scene image semantic classification visual object classes categorization multi-label image annotation content based image retrieval image understanding
	Types, structures and theories in NKI 期刊论文 OAI收割 Frontiers of Computer Science in China, 2008, 卷号: 2, 期号: 4, 页码: 451 作者: Xiaoru Zhang; Zaiyue Zhang; Yuefei Sui \| 收藏 \| 浏览/下载：2/0 \| 提交时间：2023/12/04 type ontology concept individual natural object
	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.