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	Multicore Parallelized Spatial Overlay Analysis Algorithm Using Vector Polygon Shape Complexity Index Optimization 期刊论文 OAI收割 APPLIED SCIENCES-BASEL, 2024, 卷号: 14, 期号: 5, 页码: 14 作者: Fan, Junfu; Zuo, Jiwei; Sun, Guangwei; Shi, Zongwen; Gao, Yu \| 收藏 \| 浏览/下载：12/0 \| 提交时间：2024/04/29 overlay analysis shape complexity data partitioning parallel computing acceleration ratio load balancing OpenMP
	Comparative Study on the Effect of Shape Complexity on the Efficiency of Different Overlay Analysis Algorithms 期刊论文 OAI收割 IEEE ACCESS, 2021, 卷号: 9, 页码: 144179-144194 作者: Zhang, Peng; Fan, Junfu; Zhang, Panpan; Zhang, Zhikun; Chen, Zheng \| 收藏 \| 浏览/下载：18/0 \| 提交时间：2022/09/21 Shape Complexity theory Shape measurement Linear regression Licenses Computational modeling Analytical models Shape complexity overlay analysis multiple linear regression
	Comparative Study on the Effect of Shape Complexity on the Efficiency of Different Overlay Analysis Algorithms 期刊论文 OAI收割 IEEE ACCESS, 2021, 卷号: 9, 页码: 144179-144194 作者: Zhang, Peng; Fan, Junfu; Zhang, Panpan; Zhang, Zhikun; Chen, Zheng \| 收藏 \| 浏览/下载：23/0 \| 提交时间：2022/09/21 Shape Complexity theory Shape measurement Linear regression Licenses Computational modeling Analytical models Shape complexity overlay analysis multiple linear regression
	Accuracy Assessment of the Planar Morphology of Valley Bank Gullies Extracted with High Resolution Remote Sensing Imagery on the Loess Plateau, China 期刊论文 OAI收割 INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH, 2019, 卷号: 16, 期号: 3, 页码: 19 作者: Chen, Yixian; Jiao, Juying; Wei, Yanhong; Zhao, Hengkang; Yu, Weijie \| 收藏 \| 浏览/下载：70/0 \| 提交时间：2019/05/22 valley bank gully Pleiades imagery visual interpretation accuracy assessing standard gully detection limit gully dimensions vegetation cover gully shape complexity broken topography
	Integrated intensity, orientation code and spatial information for robust tracking (EI CONFERENCE) 会议论文 OAI收割 2007 2nd IEEE Conference on Industrial Electronics and Applications, ICIEA 2007, May 23, 2007 - May 25, 2007, Harbin, China 作者: Wang Y.; Wang Y.; Wang Y.; Wang Y.; Wang Y. 收藏 \| 浏览/下载：28/0 \| 提交时间：2013/03/25 real-time tracking is an important topic in computer vision. Conventional single cue algorithms typically fail outside limited tracking conditions. Integration of multimodal visual cues with complementary failure modes allows tracking to continue despite losing individual cues. In this paper we combine intensity orientation codes and special information to form a new intensity-orientation codes-special (IOS) feature to represent the target. The intensity feature is not affected by the shape variance of object and has good stability. Orientation codes matching is robust for searching object in cluttered environments even in the cases of illumination fluctuations resulting from shadowing or highlighting etc The spatial locations of the pixels are used which allow us to take into account the spatial information which is lost in traditional histogram. Histograms of intensity orientation codes and spatial information are employed for represent the target Mean shift algorithm is a nonparametric density estimation method. The fast and optimal mode matching can be achieved by this method. In order to reduce the compute time we use the mean shift procedure to reach the target localization. Experiment results show that the new method can successfully cope with clutter partial occlusions illumination change and target variations such as scale and rotation. The computational complexity is very low. If the size of the target is 3628 pixels it only needs 12ms to complete the method. 2007 IEEE.