scholarly journals Study on the Intelligent Recognition Method for the Maturity Grade of Fresh Corn Ear based on Computer Vision

2013 ◽  
Author(s):  
Huihui Wang ◽  
Yonghai Sun ◽  
Yan Lv ◽  
Xueheng Tao ◽  
Xuejun Wang ◽  
...  
Keyword(s):  
Computer Vision ◽  
Recognition Method ◽  
Intelligent Recognition ◽  
Maturity Grade

Study on the Intelligent Recognition Method for the Maturity Grade of Fresh Corn Ear Based on Computer Vision

2013 ◽  
Vol 347-350 ◽  
pp. 3681-3684
Author(s):  
Hui Hui Wang ◽  
Yong Hai Sun ◽  
Yan Lv ◽  
Xue Heng Tao ◽  
Xue Jun Wang ◽  
...  
Keyword(s):  
Computer Vision ◽  
Fourier Transformation ◽  
Probabilistic Neural Network ◽  
Discrete Fourier Transformation ◽  
Inspection System ◽  
Recognition Method ◽  
Accuracy Rate ◽  
Energy Chain ◽  
Intelligent Recognition ◽  
Maturity Grade

In order to realize the intelligent recognition for the maturity grade of fresh corn ear, intelligent inspection system was studied based on computer vision, which could automatically complete the collection and handling of ear graphic and the recognition of maturity of the corn ear. Based on the study, a kind of intelligent recognition method was put forward under the graphic of certain frequency domain. An energy chain was established, and the characters of energy spectrum was extracted through the two-dimensional inverse discrete Fourier transformation on the graphics collected. With the above characters, a probabilistic neural network was developed, the accuracy rate of the recognition method could be 96.7%.

scholarly journals Intelligent Recognition Method of Low-Altitude Squint Optical Ship Target Fused with Simulation Samples

2021 ◽  
Vol 13 (14) ◽  
pp. 2697
Author(s):  
Bo Liu ◽  
Qi Xiao ◽  
Yuhao Zhang ◽  
Wei Ni ◽  
Zhen Yang ◽  
...  
Keyword(s):  
Feature Recognition ◽  
Spectral Characteristics ◽  
Transmission Model ◽  
Sample Type ◽  
Recognition Method ◽  
Atmospheric Transmission ◽  
K Nearest Neighbors ◽  
Imaging Simulation ◽  
Low Altitude ◽  
Intelligent Recognition

To address the problem of intelligent recognition of optical ship targets under low-altitude squint detection, we propose an intelligent recognition method based on simulation samples. This method comprehensively considers geometric and spectral characteristics of ship targets and ocean background and performs full link modeling combined with the squint detection atmospheric transmission model. It also generates and expands squint multi-angle imaging simulation samples of ship targets in the visible light band using the expanded sample type to perform feature analysis and modification on SqueezeNet. Shallow and deeper features are combined to improve the accuracy of feature recognition. The experimental results demonstrate that using simulation samples to expand the training set can improve the performance of the traditional k-nearest neighbors algorithm and modified SqueezeNet. For the classification of specific ship target types, a mixed-scene dataset expanded with simulation samples was used for training. The classification accuracy of the modified SqueezeNet was 91.85%. These results verify the effectiveness of the proposed method.

Intelligent recognition method of cervical cell cluster based on YOLO model

2018 ◽  
Vol 33 (11) ◽  
pp. 965-971
Author(s):  
郑欣 ZHENG Xin ◽  
田博 TIAN Bo ◽  
李晶晶 LI Jing-jing
Keyword(s):  
Cell Cluster ◽  
Recognition Method ◽  
Cervical Cell ◽  
Intelligent Recognition

scholarly journals Deep Learning-Based Classification of Weld Surface Defects

2019 ◽  
Vol 9 (16) ◽  
pp. 3312 ◽  
Author(s):  
Zhu ◽  
Ge ◽  
Liu
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Random Forest ◽  
Convolutional Neural Network ◽  
Surface Defects ◽  
Recognition Method ◽  
High Level ◽  
Intelligent Recognition ◽  
Non Destructive

In order to realize the non-destructive intelligent identification of weld surface defects, an intelligent recognition method based on deep learning is proposed, which is mainly formed by convolutional neural network (CNN) and forest random. First, the high-level features are automatically learned through the CNN. Random forest is trained with extracted high-level features to predict the classification results. Secondly, the weld surface defects images are collected and preprocessed by image enhancement and threshold segmentation. A database of weld surface defects is established using pre-processed images. Finally, comparative experiments are performed on the weld surface defects database. The results show that the accuracy of the method combined with CNN and random forest can reach 0.9875, and it also demonstrates the method is effective and practical.

Alphabet Recognition Based on Computer Vision

2014 ◽  
Vol 543-547 ◽  
pp. 2354-2357
Author(s):  
Hui Zhou
Keyword(s):  
Computer Vision ◽  
Experimental Results ◽  
Correct Match ◽  
Test Image ◽  
Recognition Method

In order to realize rapid alphabet recognition, the paper proposes an alphabet recognition method based on computer vision optimization technical which can also extract the classification features. Experimental results show that the obtained variance value of the test image and the standard image obtained by the proposed method is the minimum which indicating the method can achieve correct match, effective classification, and provide a great method of identification.

scholarly journals Construction and Simulation of Athlete’s Wrong Action Recognition Model in Sports Training Based on Embedded Wireless Communication and Computer Vision

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Naichun Gao
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Action Recognition ◽  
Center Of Gravity ◽  
Knee Valgus ◽  
Sports Training ◽  
Recognition Method ◽  
Total Frequency ◽  
Emg Signal ◽  
Computer Vision Technology

Embedded networking has a broad prospect. Because of the Internet and the rapid development of PC skills, computer vision technology has a wide range of applications in many fields, especially the importance of identifying wrong movements in sports training. To study the computer vision technology to identify the wrong movement of athletes in sports training, in this paper, a hidden Markov model based on computer vision technology is constructed to collect video and identify the landing and take-off movements and badminton serving movements of a team of athletes under the condition of sports training, Bayesian classification algorithm to analyze the acquired sports training action data, obtain the error frequency, and the number of errors of the landing jump action, and the three characteristic data of the displacement, velocity, and acceleration of the body’s center of gravity of the athlete in the two cases of successful and incorrect badminton serve actions and compared and analyzed the accuracy of the action recognition method used in this article, the action recognition method based on deep learning and the action recognition method based on EMG signal under 30 experiments. The training process of deep learning is specifically split into two stages: 1st, a monolayer neuron is built layer by layer so that the network is trained one layer at a time; when all layers are fully trained, a tuning is performed using a wake-sleep operation. The final result shows that the frequency of the wrong actions of the athletes on the landing jump is concentrated in the knee valgus, the total frequency of error has reached 58%, and the frequency of personal error has reached 45%; the problem of the landing distance of the two feet of the team athletes also appeared more frequently, the total frequency reached 50%, and the personal frequency reached 30%. Therefore, athletes should pay more attention to the problems of knee valgus and the distance between feet when performing landing jumps; the difference in the displacement, speed, and acceleration of the body’s center of gravity during the badminton serve will affect the error of the action. And the action recognition method used in this study has certain advantages compared with the other two action recognition methods, and the accuracy of action recognition is higher.

scholarly journals Research on Recognition Method of Basketball Goals Based on Image Analysis of Computer Vision

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Weini Zhang
Keyword(s):  
Computer Vision ◽  
Image Analysis ◽  
Interface Design ◽  
Research Process ◽  
Frame Rate ◽  
Recognition Method ◽  
Mixed Programming ◽  
Time Requirements ◽  
Stable Performance ◽  
Goal Recognition

Moving target detection is involved in many engineering applications, but basketball has some difficulties because of the time-varying speed and uncertain path. The purpose of this paper is to use computer vision image analysis to identify the path and speed of a basketball goal, so as to meet the needs of recognition and achieve trajectory prediction. This research mainly discusses the basketball goal recognition method based on computer vision. In the research process, Kalman filter is used to improve the KCF tracking algorithm to track the basketball path. The algorithm of this research is based on MATLAB, so it can avoid the mixed programming of MATLAB and other languages and reduce the difficulty of interface design software. In the aspect of data acquisition, the extended EPROM is used to store user programs, and parallel interface chips (such as 8255A) can be configured in the system to output switch control signals and display and print operations. The automatic basketball bowling counter based on 8031 microprocessor is used as the host computer. After the level conversion by MAX232, it is connected with the RS232C serial port of PC, and the collected data is sent to the workstation recording the results. In order to consider the convenience of user operation, the GUI design of MATLAB is used to facilitate the exchange of information between users and computers so that users can see the competition results intuitively. The processing frame rate of the tested video image can reach 60 frames/second, more than 25 frames/second, which meet the real-time requirements of the system. The results show that the basketball goal recognition method used in this study has strong anti-interference ability and stable performance.

scholarly journals Design and Image Research of Tennis Line Examination Based on Machine Vision Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Liu Yan ◽  
Sun Xin
Keyword(s):  
Machine Vision ◽  
Camera Calibration ◽  
Recognition Accuracy ◽  
Calibration Method ◽  
Recognition Method ◽  
Systematic Analysis ◽  
High Recognition Accuracy ◽  
Intelligent Recognition ◽  
Region Growth ◽  
Design Experiments

In view of the intelligent demand of tennis line examination, this paper performs a systematic analysis on the intelligent recognition of tennis line examination. Then, a tennis line recognition method based on machine vision is proposed. In this paper, the color region of the image recognition region is divided based on the region growth, and the rough estimation of the court boundary is realized. In order to achieve the effect of camera calibration, a fast camera calibration method which can be used for a variety of court types is proposed. On the basis of camera calibration, a tennis line examination and segmentation system based on machine vision analysis is constructed, and the experimental results are verified by design experiments. The results show that the machine vision analysis-based intelligent segmentation system of tennis line examination has high recognition accuracy and can meet the actual needs of tennis line examination.

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