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Your search keyword '"Meng, Caihong"' showing total 8 results
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8 results on '"Meng, Caihong"'

2. Moving object detection based on T-test combined with kirsch operator

Author

Zhao Hui, Meng Caihong, and Sun Xiangju

Subjects
Image fusion, Computer science, business.industry, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Pattern recognition, Video sequence, Image segmentation, Object (computer science), Object detection, Kirsch operator, Video tracking, Computer vision, Artificial intelligence, Video monitoring, business
Abstract

Moving object detection is a hot topic in computer vision. It is widely used in many areas, such as video monitoring, and intelligent traffic. This paper proposes a new method, which combines the T-test with the kirsch operator. First, the T-test method can detect the moving area between two frames of the video sequences while the kirsch operator can detect the contour of the objects. Second, the moving information fuses the contour information, so the experimental results can obtain both the moving information and the contour information. The morphologic operation is used to get the accurate information of the moving object. Finally, the moving objects are labeled in the video sequences. Experimental results prove that the proposed method is effective and robust.

Published
2011

8. A GIS-based urban landscape change analysis of Lanzhou City, China

Author

Meng, Caihong, Yang, Yongchun, Liu, Yanjun, and Hu, Haojie

Abstract

Well located at the geometric center of China, Lanzhou is one of the typical valley-basin cities in the western China. In this study, we aim to resolve two questions: (1) from the spatial analysis, we want to know what changes have happened on urban landscape characteristics from 1976 to 2004? (2) What is the main driving force behind the changes? This research is based on American quick bird remote sensing image of 2004 with a high resolution of 0.61m and the topographical map of Lanzhou city. In addition, the detailed land use data and some cadastral data in 1976 are collected as well as the current land use data. Based on the calculated result, we analyse the change characteristics of the classlevel metrics and landscape-level metrics of the Lanzhou city. Our results show that the change trend in landscape associated with urbanization of Lanzhou city can be effectively captured by spatial analysis using the remote sensing image and GIS data processing technique, and by calculating several landscape pattern metrics, such as patch density, edge density, Shannon diversity index, contagion, etc.

Published
2011
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