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	Dynamic evolution of recent droughts in Central Asia based on microwave remote sensing satellite products 期刊论文 OAI收割 JOURNAL OF HYDROLOGY, 2023, 卷号: 620, 页码: 129497 作者: Yang, Peng; Xia, Jun; Chen, Yaning \| 收藏 \| 浏览/下载：98/0 \| 提交时间：2023/06/10 Dynamic evolution Centroids Central Asia Advanced Microwave Scanning Radiometer
	Variability in Crop Response to Spatiotemporal Variation in Climate in China, 1980-2014 期刊论文 OAI收割 LAND, 2022, 卷号: 11, 期号: 8, 页码: 13 作者: Cao, Junjun; Leng, Guoyong; Yang, Peng; Zhou, Qingbo; Wu, Wenbin \| 收藏 \| 浏览/下载：25/0 \| 提交时间：2022/09/26 crop climate variability spatiotemporal centroids spatial heterogeneity
	Making sense of multivariate community responses in global change experiments 期刊论文 OAI收割 ECOSPHERE, 2022, 卷号: 13, 期号: 10 作者: Avolio, Meghan L.; Komatsu, Kimberly J.; Koerner, Sally E.; Grman, Emily; Isbell, Forest \| 收藏 \| 浏览/下载：27/0 \| 提交时间：2024/03/07 centroids data synthesis dispersion dissimilarity metrics rank abundance curves richness
	Subspace clustering guided convex nonnegative matrix factorization 期刊论文 OAI收割 NEUROCOMPUTING, 2018, 卷号: 292, 页码: 38-48 作者: Cui, Guosheng; Li, Xuelong; Dong, Yongsheng; Dong, YS (reprint author), Chinese Acad Sci, Xian Inst Opt & Precis Mech, Ctr OPT IMagery Anal & Learning OPTIMAL, Xian 710119, Shaanxi, Peoples R China. \| 收藏 \| 浏览/下载：29/0 \| 提交时间：2018/04/26 Convex Nonnegative Matrix Factorization Subspace Clustering Multiple Centroids Geometry Structure Image Clustering
	A shape context based Hausdorff similarity measure in image matching 会议论文 OAI收割 5th International Symposium on Photoelectronic Detection and Imaging (ISPDI) - Infrared Imaging and Applications, Beijing, June 25-27, 2013 作者: Ma TL(马天磊); Liu YP(刘云鹏); Shi ZL(史泽林); Yin J(尹健) 收藏 \| 浏览/下载：37/0 \| 提交时间：2013/12/26 The traditional Hausdorff measure, which uses Euclidean distance metric (L2 norm) to define the distance between coordinates of any two points, has poor performance in the presence of the rotation and scale change although it is robust to the noise and occlusion. To address the problem, we define a novel similarity function including two parts in this paper. The first part is Hausdorff distance between shapes which is calculated by exploiting shape context that is rotation and scale invariant as the distance metric. The second part is the cost of matching between centroids. Unlike the traditional method, we use the centroid as reference point to obtain its shape context that embodies global information of the shape. Experiment results demonstrate that the function value between shapes is rotation and scale invariant and the matching accuracy of our algorithm is higher than that of previously proposed algorithm on the MEPG-7 database.
	Target characteristic extraction algorithm based on block structure variables (EI CONFERENCE) 会议论文 OAI收割 2010 3rd International Conference on Advanced Computer Theory and Engineering, ICACTE 2010, August 20, 2010 - August 22, 2010, Chengdu, China 作者: Zhou Y.; Yang H.; Yan F.; Yan F. 收藏 \| 浏览/下载：28/0 \| 提交时间：2013/03/25 In image processing system the target recognition is very crucial. An optimized algorithm based on the definition of block is presented. In this algorithm two structure variables are self-defined to calculate the areas and centroids of targets. The labeling conflicts are resolved by tracking and correcting which means that if there is a conflict trace the neighbored blocks and correct the labels of them. Finally the characteristics of blocks which have the same label are accumulated. This method has outstanding advantages in saving memory compared with Pixel labeling algorithm and Run-length code algorithm. The new algorithm is simple and the results of target characteristics are easy to be analyzed and handled in the following processes. 2010 IEEE.