Research on Laser Ultrasonic Visual Image Processing

2018 ◽  
Vol 45 (1) ◽  
pp. 0104004
Author(s):  
朱洪玲 Zhu Hongling ◽  
刘畅 Liu Chang ◽  
张博 Zhang Bo ◽  
蔡桂喜 Cai Guixi
Keyword(s):  
Image Processing ◽  
Visual Image ◽  
Laser Ultrasonic

scholarly journals Art Visual Image Transmission Method Based on Cartesian Genetic Programming

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Jing Zhao
Keyword(s):  
Image Processing ◽  
Genetic Programming ◽  
Success Rate ◽  
Visual Communication ◽  
Processing Time ◽  
Visual Image ◽  
Image Transmission ◽  
Cartesian Genetic Programming ◽  
Visual Effect ◽  
Image Communication

Because most of the traditional artistic visual image communication methods use the form of modeling and calculation, there are some problems such as long image processing time, low success rate of image visual communication, and poor visual effect. An artistic visual image communication method based on Cartesian genetic programming is proposed. The visual expression sensitivity difference method is introduced to process the image data, the neural network is used to identify the characteristics of the artistic visual image, the midpoint displacement method is used to remove the folds of the artistic visual image, and the processed image is formed under the above three links. The Cartesian genetic programming algorithm is used to encode the preprocessed image, improve the fitness function, select the algorithm to improve the operation, design the image rendering platform, input the processed image to the platform, and complete the artistic visual image transmission. The analysis of the experimental results shows that the image processing time of this method is short, the success rate of visual communication is high, and the image visual effect is good, which can obtain the image processing results satisfactory to users.

scholarly journals Imaging technologies to study the composition of live pigs: A review

2016 ◽  
Vol 14 (3) ◽  
pp. e06R01 ◽  
Author(s):  
Anna Carabús ◽  
Marina Gispert ◽  
Maria Font-i-Furnols
Keyword(s):  
Magnetic Resonance Imaging ◽  
Computed Tomography ◽  
Image Processing ◽  
Magnetic Resonance ◽  
Visual Image ◽  
Dual Energy ◽  
Resonance Imaging ◽  
Processing Technologies ◽  
X Ray ◽  
Advantages And Disadvantages

Image techniques are increasingly being applied to livestock animals. This paper overviews recent advances in image processing analysis for live pigs, including ultrasound, visual image analysis by monitoring, dual-energy X-ray absorptiometry, magnetic resonance imaging and computed tomography. The methodology for live pigs evaluation, advantages and disadvantages of different devices, the variables and measurements analysed, the predictions obtained using these measurements and their accuracy are discussed in the present paper. Utilities of these technologies for livestock purposes are also reviewed. Computed tomography and magnetic resonance imaging yield useful results for the estimation of the amount of fat and lean mass either in live pigs or in carcasses. Ultrasound is not sufficiently accurate when high precision in estimating pig body composition is necessary but can provide useful information in agriculture to classify pigs for breeding purposes or before slaughter. Improvements in factors, such as the speed of scanning, cost and image accuracy and processing, would advance the application of image processing technologies in livestock animals.

ARTIFICIAL VISION FOR THE BLIND: A BIO-INSPIRED ALGORITHM FOR OBJECTS AND OBSTACLES DETECTION

2010 ◽  
Vol 10 (04) ◽  
pp. 531-544 ◽  
Author(s):  
FLORIAN DRAMAS ◽  
SIMON J. THORPE ◽  
CHRISTOPHE JOUFFRAIS
Keyword(s):  
Image Processing ◽  
Sensory Modality ◽  
Three Dimensional ◽  
Processing System ◽  
Visual Image ◽  
Artificial Vision ◽  
Image Processing Algorithm ◽  
Essential Information ◽  
Time Calculation ◽  
Current Systems

Although artificial vision systems could potentially provide very useful input to assistive devices for blind people, such devices are rarely used outside of laboratory experiments. Many current systems attempt to reproduce the visual image via an alternative sensory modality (often auditory or somatosensory), but this dominant "scoreboard" approach, is often difficult to interpret for the user. Here, we propose to offload the recognition problem onto a separate image processing system that then provides the user with just the essential information about the location of objects in the surrounding environment. Specifically, we show that a bio-inspired image processing algorithm (SpikeNet) can not only robustly, precisely, and rapidly recognize and locate key objects in the image, but also in space if the objects are in a stereoscopic field of view. In addition, the bio-inspired algorithm allows real-time calculation of optic flow. We hence propose that this system, coupled with a restitution interface allowing localization in space (i.e. three-dimensional virtual sounds synthesis) can be used to restore essential visuomotor behaviors such as grasping desired objects and navigating (finding directions, avoiding obstacles) in unknown environments.

Image intelligent automatic processing analysis based on artificial intelligence vision generator

2020 ◽  
pp. 1-10
Author(s):  
Ruijuan Wang ◽  
Wei Zhuo
Keyword(s):  
Artificial Intelligence ◽  
Image Processing ◽  
Deep Learning ◽  
Human Error ◽  
Processing System ◽  
Image Data ◽  
Visual Image ◽  
Automated Analysis ◽  
Learning Method ◽  
Deep Learning Model

The image intelligent processing analysis technology uses a computer to imitate and execute some intellectual functions of the human brain, and realizes an image processing system with artificial intelligence, that is, an image processing analysis technology is an understanding of an image. The degree of intelligent automated analysis and processing is low, many operations need to be done manually, causing human error, inaccurate detection, and time-consuming and laborious. Deep learning method can extract features step by step in the original image from the bottom to the top. Therefore, based on feature analysis technology, this paper uses the deep learning method to intelligently and automatically analyse the visual image. This method only needs to send the image into the system, and then the manual analysis is not needed, and the analysis result of the final image can be obtained. The process is completely intelligent and automatically processed. First, improve the deep learning model and use massive image data to choose and optimize parameters. Results indicate that our method not only automatically derives the semantic information of the image, but also accurately understands the image accurately and improve the work efficiency.

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