A new method ,which is called image manipulation, is introduced to analyze the cavitation of flow field for the first time. As the complexity of the cavitation development must be considering, only the method of ima...A new method ,which is called image manipulation, is introduced to analyze the cavitation of flow field for the first time. As the complexity of the cavitation development must be considering, only the method of image manipulation can calculate the strength of the cavitation more accurately. This method based on wavelet transform is used to eliminate the noise. The area of the cavitations is deduced to serve as the strength of cavitation. The method is applied in an example of inducer's rotating cavitation. The results show that using image manipulation can get the accurate date of cavitation with ease,and the reason of the inducer shaft's vibration is uncovered clearly.展开更多
A galvanometric scanner with a dynamic focus was designed using a PC to realize the model transform of the image, calculate the interpolation points of the image, and implement the focus compensation of the dynamic fo...A galvanometric scanner with a dynamic focus was designed using a PC to realize the model transform of the image, calculate the interpolation points of the image, and implement the focus compensation of the dynamic focus system. The interrupt of PC was used for the real-time control. It was confirmed that the PC-based galvanometric scanner with dynamic focus could run more than 72 h stably, with an accuracy of 100 ±0.1 mm, and the period of real-time control was less than 20μs.展开更多
The computer graphics and computer vision communities have been working closely together in recent years and a variety of algorithms and applications have been developed to analyze and manipulate the visual media arou...The computer graphics and computer vision communities have been working closely together in recent years and a variety of algorithms and applications have been developed to analyze and manipulate the visual media around us. There are three major driving forces behind this phenomenon： 1） the availability of big data from the Internet has created a demand for dealing with the ever-increasing, vast amount of resources; 2） powerful processing tools, such as deep neural networks, provide effective ways for learning how to deal with heterogeneous visual data; 3） new data capture devices, such as the Kilxect, the bridge betweea algorithms for 2D image understanding and 3D model analysis. These driving forces have emerged only recently, and we believe that the computer graphics and computer vision communities are still in the beginning of their honeymoon phase. In this work we survey recent research on how computer vision techniques benefit computer graphics techniques and vice versa, and cover research on analysis, manipulation, synthesis, and interaction. We also discuss existing problems and suggest possible further research directions.展开更多
文摘A new method ,which is called image manipulation, is introduced to analyze the cavitation of flow field for the first time. As the complexity of the cavitation development must be considering, only the method of image manipulation can calculate the strength of the cavitation more accurately. This method based on wavelet transform is used to eliminate the noise. The area of the cavitations is deduced to serve as the strength of cavitation. The method is applied in an example of inducer's rotating cavitation. The results show that using image manipulation can get the accurate date of cavitation with ease,and the reason of the inducer shaft's vibration is uncovered clearly.
文摘A galvanometric scanner with a dynamic focus was designed using a PC to realize the model transform of the image, calculate the interpolation points of the image, and implement the focus compensation of the dynamic focus system. The interrupt of PC was used for the real-time control. It was confirmed that the PC-based galvanometric scanner with dynamic focus could run more than 72 h stably, with an accuracy of 100 ±0.1 mm, and the period of real-time control was less than 20μs.
基金This research was sponsored by the National Natural Science Foundation of China under Grant Nos. 61572264 and 61373069, the National Key Research and Development Plan of China under Grant No. 2016YFB1001402, Huawei Innovation Research Program （HIRP）, China Association for Science and Technology （CAST） Young Talents Plan, and Tianjin Short-Term Recruitment Program of Foreign Experts.
文摘The computer graphics and computer vision communities have been working closely together in recent years and a variety of algorithms and applications have been developed to analyze and manipulate the visual media around us. There are three major driving forces behind this phenomenon： 1） the availability of big data from the Internet has created a demand for dealing with the ever-increasing, vast amount of resources; 2） powerful processing tools, such as deep neural networks, provide effective ways for learning how to deal with heterogeneous visual data; 3） new data capture devices, such as the Kilxect, the bridge betweea algorithms for 2D image understanding and 3D model analysis. These driving forces have emerged only recently, and we believe that the computer graphics and computer vision communities are still in the beginning of their honeymoon phase. In this work we survey recent research on how computer vision techniques benefit computer graphics techniques and vice versa, and cover research on analysis, manipulation, synthesis, and interaction. We also discuss existing problems and suggest possible further research directions.
