Research on the Tool Images Evaluation Based on Auto-Focus Technology

2011 ◽  
Vol 411 ◽  
pp. 478-482
Author(s):  
Yue Shen Lai ◽  
Meng Shi ◽  
Jun Wei Tian ◽  
Gang Cheng

Accurate and efficient image-clarity evaluation function which adopts digital image processing technology is the key to achieving automatic focusing. The different evaluation functions are adopted for different images. By converting color images to grayscale, and comparing them with the green component which extract from the color images, it is found that using the gray image can get a much better results and an evaluation function is built which is used for tool images. Experiments prove that the used algorithm has a good single peak, accuracy, stability, and fast speed.

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Jing Zhang ◽  
Tao Zhang

When performing digital image processing, the most critical technology that affects its use effect is the autofocus technology. With the advancement of science and the development of computer technology, autofocus technology has become more and more widely used in various fields. Autofocus technology is a key technology in robot vision and digital video systems. In order to allow digital image processing technology to better serve humans, it is necessary to further improve the focus evaluation function algorithm. This article focuses on the imaging principle of defocused images, using different evaluation functions to analyze and process the experimental images to observe the changes in image clarity. Through the introduction and analysis of the existing evaluation function, it can be known that the focus evaluation function will directly affect the quality of digital image processing. Therefore, it is best to choose unimodality, unbiasedness, low noise sensitivity, wide coverage, and a small amount of calculation. For the evaluation function, the Laplacian gradient function is an ideal choice. However, because the current digital image processing technology is not perfect enough, the focus function is still prone to multiextreme problems when the image is severely defocused and the high-frequency components in the image are missing; the balance between image processing speed and focus accuracy also still needs to be improved. Therefore, this paper studies the autocontrol microscope focus algorithm based on digital image processing, analyzes the principle of visual image imaging, and makes some improvements to the microscope focus algorithm. Through experiments, it can be seen that the real-time data of the original Laplace function in the edge-obvious target is 76.9, and it reaches 77.6 after improvement. The improved algorithm can better maintain the single-peak state during the focusing process, which improves the image processing efficiency while ensuring the measurement accuracy.


2021 ◽  
Author(s):  
Zhe Yin ◽  
Zhijie Shan

<p>Rock outcrops are common features of the karst ecosystem The bare rock rate is an important indicator for rocky desertification grades classification, and its accurate extraction can benefit for understanding the distribution characteristics of rock outcrops in desertification areas and the classification of rocky desertification grades. In order to explore the distribution pattern of surface bare rocks in the typical geomorphic environment of the Karst gabin basin, the Mengzi gabin basin was carried out as the research site. The combination of UAV shooting images and digital image processing technology were used, the characteristics of bare rock rate on the karst fault basin after vegetation restoration were shaped. Our results showed that digital image processing technology can be used for extraction of bare rock rate in Karst area, and the effective combination of UAV technology and digital image processing technology can quickly obtain bare rock rate data of typical landform in Karst gabin basins. After performing drone aerial photography on 26 typical landform information under different bare rock distribution conditions on the Mengzi gabin basin, the results of the image processing analysis showed that the bare rock rate is between 2.7%-28.9%. The research provide technical support for the assessment of the karst ecosystem degradation and the evaluation of the current status of rocky desertification in karst gabin basin</p>


Sign in / Sign up

Export Citation Format

Share Document