Multiple Target Image Tracking Technology Based on FPGA

2013 ◽  
Vol 3 (4) ◽  
pp. 66-70
Author(s):  
Zhongxun Wang ◽  
Jietao Bi ◽  
Qiying Jin
Keyword(s):  
Multiple Target ◽  
Target Image ◽  
Image Tracking ◽  
Tracking Technology

Application of image tracking technology on microstructure research of lime solidify soil

2008 ◽  
Author(s):  
Wei Wang ◽  
Xiaoyuan He ◽  
Jianqiang Xiao ◽  
Yunmei Meng ◽  
Xin Hu ◽  
...  
Keyword(s):  
Image Tracking ◽  
Tracking Technology

Target Location of Industrial Robot Based on Vision

2014 ◽  
Vol 945-949 ◽  
pp. 1478-1481
Author(s):  
Gui Hong Jia
Keyword(s):  
Target Location ◽  
Vision System ◽  
Industrial Robot ◽  
Robot Vision ◽  
Contour Extraction ◽  
Multiple Target ◽  
Segmentation Method ◽  
Target Image ◽  
Black And White ◽  
Centroid Position

Vision is the most important way to obtain information from the word. This paper collected the target image using industrial robot vision system, and We get Black and white images using binary image segmentation method, then the contour of each object in the image can be obtained with edge detection and contour extraction, The centroid position was confirmed using minimum enclosing rectangle method after gaining the outline of target. The experimental results show that this method can quickly and accurately obtain multiple target centroid position.

The Research About Adaptive Active Recognition and Tracking Technology of Fast Target Image Strength

2020 ◽  
Vol 20 (20) ◽  
pp. 11795-11801
Author(s):  
Li Ning ◽  
Liu Chunxiang ◽  
Zhang Yunfeng ◽  
Cao Lihua ◽  
Zhaobing Chen
Keyword(s):  
Target Image ◽  
Tracking Technology

scholarly journals Multiple Auto-Adapting Color Balancing for Large Number of Images

2015 ◽  
Vol XL-7/W3 ◽  
pp. 735-742 ◽  
Author(s):  
X. Zhou
Keyword(s):  
Adaptive Methods ◽  
Least Square ◽  
Target Color ◽  
Multiple Target ◽  
Target Image ◽  
Local Color ◽  
Local Statistics ◽  
Least Square Fitting ◽  
Surface Models ◽  
And Performance

This paper presents a powerful technology of color balance between images. It does not only work for small number of images but also work for unlimited large number of images. Multiple adaptive methods are used. To obtain color seamless mosaic dataset, local color is adjusted adaptively towards the target color. Local statistics of the source images are computed based on the so-called adaptive dodging window. The adaptive target colors are statistically computed according to multiple target models. The gamma function is derived from the adaptive target and the adaptive source local stats. It is applied to the source images to obtain the color balanced output images. Five target color surface models are proposed. They are color point (or single color), color grid, 1st, 2nd and 3rd 2D polynomials. Least Square Fitting is used to obtain the polynomial target color surfaces. Target color surfaces are automatically computed based on all source images or based on an external target image. Some special objects such as water and snow are filtered by percentage cut or a given mask. Excellent results are achieved. The performance is extremely fast to support on-the-fly color balancing for large number of images (possible of hundreds of thousands images). Detailed algorithm and formulae are described. Rich examples including big mosaic datasets (e.g., contains 36,006 images) are given. Excellent results and performance are presented. The results show that this technology can be successfully used in various imagery to obtain color seamless mosaic. This algorithm has been successfully using in ESRI ArcGis.

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