Auto-focusing technique for moving object based on image definition criterion

2010 ◽  
Author(s):  
An Tao ◽  
Jin Gang ◽  
Wang Jian
Keyword(s):  
Moving Object ◽  
Object Based ◽  
Auto Focusing ◽  
Image Definition

An Auto-Focus Algorithm of Fast Search Based on Combining Rough and Fine Adjustment

2012 ◽  
Vol 468-471 ◽  
pp. 534-537
Author(s):  
Zhao Hua Lin ◽  
Xin Liu ◽  
Yu Liang Zhang ◽  
Shu Mei Zhang
Keyword(s):  
Focal Plane ◽  
Narrow Range ◽  
Evaluation Function ◽  
Small Step ◽  
Uniform Sampling ◽  
Fast Search ◽  
Optical Measure ◽  
Fine Adjustment ◽  
Auto Focusing ◽  
Image Definition

A coarse and fine combined fast search and auto-focusing algorithm was suggested in this paper. This method can automatically search and find the focal plane by evaluating the image definition. The Krisch operator based edge energy function was used as the big-step coarse focusing, and then the wavelet transform based image definition evaluation function, which is sensitivity to the variation in image definition, was used to realize the small-step fine focusing in a narrow range. The un-uniform sampling function of the focusing area selection used in this method greatly reduces the workload and the required time for the data processing. The experimental results indicate that this algorithm can satisfy the requirement of the optical measure equipment for the image focusing.

scholarly journals Collision Avoidance with a Moving Object Based on Q-Learning.

2003 ◽  
Vol 69 (680) ◽  
pp. 1051-1057 ◽  
Author(s):  
Masashi FURUKAWA ◽  
Michiko WATANABE ◽  
Masaharu IKEDA ◽  
Masahiro KINOSHITA ◽  
Yukinori KAKAZU
Keyword(s):  
Collision Avoidance ◽  
Moving Object ◽  
Q Learning ◽  
Object Based

scholarly journals Compressive Imaging of Moving Object Based on Linear Array Sensor

2016 ◽  
Vol 2016 ◽  
pp. 1-6 ◽  
Author(s):  
Changjun Zha ◽  
Yao Li ◽  
Jinyao Gui ◽  
Huimin Duan ◽  
Tailong Xu
Keyword(s):  
Moving Objects ◽  
Linear Array ◽  
Matching Pursuit ◽  
Moving Object ◽  
Imaging Method ◽  
Compressive Imaging ◽  
Array Sensor ◽  
Object Based ◽  
Direct Separation ◽  
Matching Pursuit Algorithm

Using the characteristics of a moving object, this paper presents a compressive imaging method for moving objects based on a linear array sensor. The method uses a higher sampling frequency and a traditional algorithm to recover the image through a column-by-column process. During the compressive sampling stage, the output values of the linear array sensor are multiplied by a coefficient that is a measurement matrix element, and then the measurement value can be acquired by adding all the multiplication values together. During the reconstruction stage, the orthogonal matching pursuit algorithm is used to recover the original image when all the measurement values are obtained. Numerical simulations and experimental results show that the proposed compressive imaging method not only effectively captures the information required from the moving object for image reconstruction but also achieves direct separation of the moving object from a static scene.

Distance measurement from a moving object based on speckle velocity detection

1981 ◽  
Vol 20 (5) ◽  
pp. 721 ◽  
Author(s):  
M. Giglio ◽  
S. Musazzi ◽  
U. Perini
Keyword(s):  
Distance Measurement ◽  
Moving Object ◽  
Object Based

Prediction of a Moving Object Based on Monocular Vision

2016 ◽  
Vol 13 (7) ◽  
pp. 4373-4378
Author(s):  
Yang Song ◽  
Juliang Xiao ◽  
Yang Zhao ◽  
Gang Wang
Keyword(s):  
Monocular Vision ◽  
Moving Object ◽  
Object Based
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