In order to improve the detection and identification ability of sports injury ultrasound medicine, a segmentation method of sports injury ultrasound medical image based on local features is proposed, and the research on the sports injury ultrasound medical detection and identification ability is carried out. Methods of the sports injury ultrasound medical image segmentation model are established; the sports injury ultrasound medical image information is enhanced by using the sports skeletal muscle block matching technology; the image features are extracted; and the characteristics of sports injury ultrasound medical images are analyzed by CT bright spot feature transmission. In detail, combined with the deep convolutional neural network training method, the extracted sports injury points are automatically detected for sports injury ultrasound medical images, and the sports injury ultrasound medical image segmentation is realized. The simulation results show that this method has high accuracy for sports injury ultrasound medical image segmentation, the error value can be controlled below 0.103, and finally, the effect of zero error is achieved. It is confirmed that the method proposed in this paper has high resolution and accuracy for sports injury point detection and has strong practical application ability.
The estimation time and estimation precision of motion pose samples are problematic for the pose estimation algorithm of sports movements. This paper proposes a multifeature fusion-based algorithm for accurate posture estimation. The human rod model is constructed after analyzing the human pose estimation technology. Using the Kalman filter method, the degree of freedom and range of motion of the major joints of the human body were determined. The eight-star model was used to extract the sports posture features, and the weighted average method was used to process the grayscale images of sports. Using the multifeature fusion method, the extracted multisource feature vector information is thoroughly analyzed and processed, and a new group of fusion feature vectors is created. Using a mixture Gaussian distribution model, the posture estimation of an athlete’s body is accomplished. Experimental results indicate that when the amount of sports pose sample data is 900 GB, the accurate estimation time of the proposed method is 5.3 s, and its accuracy is 100 percent. Improve the estimation accuracy of samples of sports posture.
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