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	Multibranch Feature Difference Learning Network for Cross-Spectral Image Patch Matching 期刊论文 OAI收割 IEEE Transactions on Geoscience and Remote Sensing, 2022, 卷号: 60, 页码: 1-15 作者: Yu C(余创) \| 收藏 \| 浏览/下载：49/0 \| 提交时间：2022/06/07 Combined metric network Cross-spectral image patch matching Feature difference multibranch feature difference learning network (MFD-Net)
	Features Combined Binary Descriptor Based on Voted Ring-Sampling Pattern 期刊论文 OAI收割 IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2020, 卷号: 30, 期号: 10, 页码: 3675-3687 作者: Liu, Hongmin; Zhang, Qianqian; Fan, Bin; Wang, Zhiheng; Han, Junwei \| 收藏 \| 浏览/下载：29/0 \| 提交时间：2021/01/07 Histograms Feature extraction Encoding Task analysis Fans Computational efficiency Memory management Binary descriptor combined features gradient feature intensity feature ring-sampling pattern voting strategy
	Multi-source Remote Sensing Image Registration Based on Contourlet Transform and Multiple Feature Fusion 期刊论文 OAI收割 International Journal of Automation and Computing, 2019, 卷号: 16, 期号: 5, 页码: 575-588 作者: Huan Liu; Gen-Fu Xiao; Yun-Lan Tan; Chun-Juan Ouyang \| 收藏 \| 浏览/下载：8/0 \| 提交时间：2021/02/22 Feature fusion multi-scale circle Gaussian combined invariant moment multi-direction gray level co-occurrence matrix multi-source remote sensing image registration contourlet transform.
	ESTIMATION OF EVAPOTRANSPIRATION IN HEIHE RIVER BASIN USING HJ-1AB DATA 会议论文 OAI收割 2012 Ieee International Geoscience and Remote Sensing Symposium (Igarss) Xu Hongwei; Sun Rui; Du Junping 收藏 \| 浏览/下载：21/0 \| 提交时间：2014/12/07 SEBS model Priestley-Taylor Equation combined with Ts-NDVI feature space HJ-1A/B Heihe River BALANCE SYSTEM SEBS HEAT-FLUX ENERGY
	The new approach for infrared target tracking based on the particle filter algorithm (EI CONFERENCE) 会议论文 OAI收割 International Symposium on Photoelectronic Detection and Imaging 2011: Advances in Infrared Imaging and Applications, May 24, 2011 - May 24, 2011, Beijing, China 作者: Sun H.; Han H.-X.; Sun H. 收藏 \| 浏览/下载：63/0 \| 提交时间：2013/03/25 Target tracking on the complex background in the infrared image sequence is hot research field. It provides the important basis in some fields such as video monitoring precision and video compression human-computer interaction. As a typical algorithms in the target tracking framework based on filtering and data connection the particle filter with non-parameter estimation characteristic have ability to deal with nonlinear and non-Gaussian problems so it were widely used. There are various forms of density in the particle filter algorithm to make it valid when target occlusion occurred or recover tracking back from failure in track procedure but in order to capture the change of the state space it need a certain amount of particles to ensure samples is enough and this number will increase in accompany with dimension and increase exponentially this led to the increased amount of calculation is presented. In this paper particle filter algorithm and the Mean shift will be combined. Aiming at deficiencies of the classic mean shift Tracking algorithm easily trapped into local minima and Unable to get global optimal under the complex background. From these two perspectives that "adaptive multiple information fusion" and "with particle filter framework combining" we expand the classic Mean Shift tracking framework.Based on the previous perspective we proposed an improved Mean Shift infrared target tracking algorithm based on multiple information fusion. In the analysis of the infrared characteristics of target basis Algorithm firstly extracted target gray and edge character and Proposed to guide the above two characteristics by the moving of the target information thus we can get new sports guide grayscale characteristics and motion guide border feature. Then proposes a new adaptive fusion mechanism used these two new information adaptive to integrate into the Mean Shift tracking framework. Finally we designed a kind of automatic target model updating strategy to further improve tracking performance. Experimental results show that this algorithm can compensate shortcoming of the particle filter has too much computation and can effectively overcome the fault that mean shift is easy to fall into local extreme value instead of global maximum value.Last because of the gray and fusion target motion information this approach also inhibit interference from the background ultimately improve the stability and the real-time of the target track. 2011 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE).
	People counting using combined feature 会议论文 OAI收割 Beijing, China, 2011 作者: Congwen Gao; Kaiqi Huang; Tieniu Tan \| 收藏 \| 浏览/下载：23/0 \| 提交时间：2016/12/30 Statistical Analysis video Surveillance combined Feature
	Layered fast correlation tracking algorithm combined with target feature (EI CONFERENCE) 会议论文 OAI收割 4th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optical Test and Measurement Technology and Equipment, November 19, 2008 - November 21, 2008, Chengdu, China 作者: Guo L.-H. 收藏 \| 浏览/下载：23/0 \| 提交时间：2013/03/25 A new correlation tracking algorithm layered fast correlation tracking algorithm combined with target feature is proposed for target tracking in image sequences. Based on traditional correlation tracking algorithm according to resolution of real-time image the proposed algorithm chooses the designated layer image. At the same time the proposed algorithm uses a new search method combined with the target features to predict matching position which can improve the matching precision and reduce computational complexity. In addition the experimental results indicate that the proposed algorithm can overcome the influence of gray mutation and satisfy the requirement of real-time. 2009 SPIE.