Topology 147-3-1, tangent-sigmoid transfer function at hidden layer and purelin transfer function at output layer Showed that the best desirable network for classification based on freshness is a one-hidden layer network with The results of the evaluation of the neural networks Also,Īccording to the regression diagram of fat content obtained from the destructive method (fat content obtainedįrom Soxhlet device) with fat content obtained from non-destructive method (machine vision), the coefficient ofĭetermination and accuracy between them achieved 0.841. With one and two hidden layers, a various number of neurons, and threshold functions were used. Moreover, to predict the freshness and quality of meat, feed-forward back propagation artificial neural networks In the image processing section,ġ08 textual features and 39 color features were extracted in the RGB, HSV, HIS, and CIElab color spaces. Using image processing as a non-destructive method and Soxhlet device as a destructive method, the amount ofįat content was predicted, and also the freshness was classified for camel meat. This research aims to investigate and evaluate the fatĬontent and freshness of camel meat using machine vision technology as a non-destructive method. This study shows that the image processing technology can effectively process the players’ static images, which gives the direction for physical education (PE) under artificial intelligence (AI).Ĭamel meat can be a suitable alternative for other red meat types in human nutrition, due to its low cholesterolĪnd low-fat content and the appropriate protein content. The badminton teaching action recognition model based on Haar-like and AdaBoost can recognize and classify badminton actions and improve the quality of badminton teaching. The proposed method has a recognition rate of more than 90% for each action, the average recognition accuracy of actions reaches 95%, the average recognition rate of the same person’s actions is 96.5%, and the average recognition rate of different people’s actions is 94.8%. The results show that action images improved by machine vision can process the captured actions effectively, making the computer better identify different badminton teaching actions. Finally, the badminton players’ action recognition algorithm is tested and compared with the traditional hidden Markov model (HMM) and support vector machine (SVM). Furthermore, a new algorithm model is implemented and trained by using Haar-like and Adaptive Boosting (AdaBoost). Subsequently, badminton players’ actions are recognized and preprocessed, and a dataset is constructed. Second, the images and videos about the action decomposition of badminton teaching are collected, and the image data are extracted by Haar-like. First, the principle and advantages of machine vision sensing are introduced. The teaching process is segmented into many independent actions to help learners standardize their movements in badminton play, improving the national physical quality. The study was aimed at realizing the identification of athletes’ actions in badminton teaching. This research can provide theoretical support for the application of machine vision model in other fields. Finally, the accuracy of the optimization model is verified by analyzing and predicting the data. It can be seen from the analysis that both language and vocal music have obvious stability in the calculation process, which is an inherent attribute of communication platform. By using machine vision model to design and analyze the communication platform of art education, the changing trend of different indicators can be obtained. Through analysis, it can be seen that M3 has the greatest influence on coordinate data. The curve corresponding to parameter M3 also shows a linear trend of change. The curve corresponding to parameter M1 shows a gradual decline, and the curve of parameter M2 has little influence on coordinate data. The parameter γ also promotes and inhibits coordinate values. Relevant studies show that, according to the coordinate parameter curve, the parameter α plays a promoting role on the coordinate value, while the parameter β plays a promoting role on the coordinate value first and then plays an inhibiting role. This model can be used to analyze the design and application of art education platform. In order to further analyze the relevant content of the art education exchange, based on machine vision theory, model calibration, and coordinate change are adopted to modify the original model, so as to obtain the optimized model. There are some problems in the design and application of the art education exchange platform.
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