机构

• 心理研究所 [1]
• 自动化研究所 [1]
• 沈阳自动化研究所 [1]
• 合肥物质科学研究院 [1]

采集方式

• OAI收割 [4]

内容类型

• 期刊论文 [3]
• 会议论文 [1]

发表日期

• 2023 [1]
• 2018 [1]
• 2016 [1]
• 2013 [1]

学科主题

筛选

浏览/检索结果: 共4条，第1-4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 Class-Oriented Self-Learning Graph Embedding for Image Compact Representation 期刊论文  OAI收割 IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2023, 卷号: 33, 期号: 1, 页码: 74-87 作者:  Hu, Liangchen;  Dai, Zhenlei;  Tian, Lei;  Zhang, Wensheng   |  收藏  |  浏览/下载：21/0  |  提交时间：2023/03/20 Sparse matrices  Manifolds  Machine learning algorithms  Laplace equations  Heuristic algorithms  Data models  Data mining  Adaptive graph learning  separability examination  marginal information preserving  L-2,L-p-norm sparsity  compact representation   Boosting water conservation by improving campaign: Evidence from a field study in China 期刊论文  OAI收割 URBAN WATER JOURNAL, 2018, 卷号: 15, 期号: 10, 页码: 966-973 作者:  Sun, Yan;  Li, Pengna;  She, Shengxiang;  Eimontaite, Iveta;  Yang, Bo   |  收藏  |  浏览/下载：56/0  |  提交时间：2019/04/22 Water conservation  campaign  field study  quantitative information  norm information   Adaptive modelling of gene regulatory network using Bayesian information criterion-guided sparse regression approach 期刊论文  OAI收割 IET SYSTEMS BIOLOGY, 2016, 卷号: 10, 期号: 6, 页码: 252-259 作者:  Shi, Ming;  Shen, Weiming;  Wang, Hong-Qiang;  Chong, Yanwen 收藏  |  浏览/下载：24/0  |  提交时间：2017/12/18 Genetics  Bayes Methods  Genomics  Regression Analysis  Inference Mechanisms  Bioinformatics  Adaptive Modelling  Gene Regulatory Network  Bayesian Information Criterion-guided Sparse Regression Approach  Grn  Microarray Expression Data  Systems Biology  Grn Reconstruction  Optimisation  l(1)-norm Regularisation   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：36/0  |  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.