Sending image information to its Fourier phase and its use toward image recognition

1996 ◽  
Author(s):  
Hamid Fahimi
Keyword(s):  
Image Recognition ◽  
Image Information

scholarly journals Research on the Application of Artificial Intelligence Machine Learning Technology in Improving the Accuracy of Engineering Image Processing

2021 ◽  
Vol 2083 (4) ◽  
pp. 042007
Author(s):  
Xiaowen Liu ◽  
Juncheng Lei
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Deep Learning ◽  
Image Recognition ◽  
Recognition Performance ◽  
Color Space ◽  
Gaussian Model ◽  
Image Feature ◽  
Layer By Layer ◽  
Image Information

Abstract Image recognition technology mainly includes image feature extraction and classification recognition. Feature extraction is the key link, which determines whether the recognition performance is good or bad. Deep learning builds a model by building a hierarchical model structure like the human brain, extracting features layer by layer from the data. Applying deep learning to image recognition can further improve the accuracy of image recognition. Based on the idea of clustering, this article establishes a multi-mix Gaussian model for engineering image information in RGB color space through offline learning and expectation-maximization algorithms, to obtain a multi-mix cluster representation of engineering image information. Then use the sparse Gaussian machine learning model on the YCrCb color space to quickly learn the distribution of engineering images online, and design an engineering image recognizer based on multi-color space information.

scholarly journals Fuzzy Logic Using in Images Retrieval Depending on Their Features

2019 ◽  
Vol 54 (5) ◽  
Author(s):  
Faiez Musa Lahmood Alrufaye ◽  
Mohammed Muanis I. Al-Sagheer ◽  
Marwah Thamer Ali
Keyword(s):  
Artificial Intelligence ◽  
Image Processing ◽  
Fuzzy Logic ◽  
Computer Science ◽  
Image Recognition ◽  
Correct Result ◽  
Image Information ◽  
Fuzzy Logic Algorithm ◽  
Image Characteristics ◽  
Content Information

Image processing has become one of the most important branches of computer science, especially after entering into several areas of life such as medicine, engineering and various sciences. In our current research, we have developed a system of image recognition based on image characteristics and some content information using the most important artificial intelligence algorithms, a fuzzy logic algorithm, to obtain complete image information using small values ranging from 0 to 1. The program was executed on a set of standard database called the WANG database. It holds the contents of 1000 images from the Corel stock photo database, in JPEG format. The system was evaluated using the recall method. This method calculates the proportion of correct results identified by the system as correct results with correct result identified by the classic system.

Study on the Image Process of Wheels Based on Machine Vision

2011 ◽  
Vol 179-180 ◽  
pp. 914-919
Author(s):  
Shi Feng Wang ◽  
Chang Chun Li ◽  
Jing Yu ◽  
Tian Hou Zhang
Keyword(s):  
Feature Extraction ◽  
Machine Vision ◽  
Edge Detection ◽  
Image Recognition ◽  
Image Feature ◽  
Image Process ◽  
Image Feature Extraction ◽  
Image Information ◽  
Industrial Cameras

This paper introducted the machine vision technology into the field of wheel suspension coating, used the industrial cameras for the image information of the wheel center and the valve hole, did the edge detection of the valve hole and the wheel based on visual c++ platform ,making the image feature extraction and analysis, thus completing image recognition of the wheel center and the valve hole, realized hang the wheels to the conveyor of automatically.

Oil Leak Image Data Analysis Based on Maximum Variance Threshold Method

2014 ◽  
Vol 687-691 ◽  
pp. 3564-3568
Author(s):  
Xi Zhang ◽  
Lei Zhang ◽  
Yu Feng Zhang
Keyword(s):  
Data Analysis ◽  
Image Recognition ◽  
Computer Image ◽  
Image Data ◽  
Threshold Method ◽  
Threshold Segmentation ◽  
Image Information ◽  
Maximum Variance ◽  
Recognition Technique

The oil leak is detected by the computer image recognition technique and the oil leak images are processed by the threshold segmentation and semi-thresholding of the maximum variance threshold method. Through capture of fluorescent images of leakage, the oil leak image information is acquired. The maximum variance threshold method is adopted for image data analysis to compute the dilation area of oil images and achieve the detection of leakage.

scholarly journals Noise Data Removal and Image Restoration Based on Partial Differential Equation in Sports Image Recognition Technology

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Shi Junmei
Keyword(s):  
Differential Equation ◽  
Image Processing ◽  
Partial Differential Equation ◽  
Image Restoration ◽  
Image Recognition ◽  
Image Data ◽  
Order Partial Differential Equation ◽  
Partial Differential ◽  
Image Information ◽  
Noise Data

With the rapid development of image processing technology, the application range of image recognition technology is becoming more and more extensive. Processing, analyzing, and repairing graphics and images through computer and big data technology are the main methods to obtain image data and repair image data in complex environment. Facing the low quality of image information in the process of sports, this paper proposes to remove the noise data and repair the image based on the partial differential equation system in image recognition technology. Firstly, image recognition technology is used to track and obtain the image information in the process of sports, and the fourth-order partial differential equation is used to optimize and process the image. Finally, aiming at the problem of low image quality and blur in the transmission process, denoising is carried out, and image restoration is studied by using the adaptive diffusion function in partial differential equation. The results show that the research content of this paper greatly improves the problems of blurred image and poor quality in the process of sports and realizes the function of automatically tracking the target of sports image. In the image restoration link, it can achieve the standard repair effect and reduce the repair time. The research content of this paper is effective and applicable to image processing and restoration.

Smart teaching mode based on particle swarm image recognition and human-computer interaction deep learning

2020 ◽  
Vol 39 (4) ◽  
pp. 5699-5711
Author(s):  
Shirong Long ◽  
Xuekong Zhao
Keyword(s):  
Feature Extraction ◽  
Particle Swarm Optimization ◽  
Deep Learning ◽  
Real Time ◽  
Image Recognition ◽  
Particle Swarm ◽  
Learning Technology ◽  
Search Performance ◽  
Swarm Optimization ◽  
Teaching Mode

The smart teaching mode overcomes the shortcomings of traditional teaching online and offline, but there are certain deficiencies in the real-time feature extraction of teachers and students. In view of this, this study uses the particle swarm image recognition and deep learning technology to process the intelligent classroom video teaching image and extracts the classroom task features in real time and sends them to the teacher. In order to overcome the shortcomings of the premature convergence of the standard particle swarm optimization algorithm, an improved strategy for multiple particle swarm optimization algorithms is proposed. In order to improve the premature problem in the search performance algorithm of PSO algorithm, this paper combines the algorithm with the useful attributes of other algorithms to improve the particle diversity in the algorithm, enhance the global search ability of the particle, and achieve effective feature extraction. The research indicates that the method proposed in this paper has certain practical effects and can provide theoretical reference for subsequent related research.

AN ALGORITHM FOR IMAGE RECOGNITION AND ANALYSIS USING NEURAL INFORMATION TECHNOLOGIES

2012 ◽  
Vol 71 (17) ◽  
pp. 1565-1574 ◽  
Author(s):  
O. M. Gafurov ◽  
V. I. Syryamkin ◽  
A. O. Gafurov ◽  
S. S. Stolyarova
Keyword(s):  
Image Recognition ◽  
Information Technologies ◽  
Neural Information
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