Details

Autor(en) / Beteiligte

• Sun, Nannan
• Sun, Ruiyang
• Li, Shihong
• Wu, Xiaolong

Titel

Martial Arts Routine Difficulty Action Technology VR Image Target Real-Time Extraction Simulation

Ist Teil von

• IEEE access, 2020-01, Vol.8, p.1-1

Ort / Verlag

Piscataway: IEEE

Erscheinungsjahr

2020

Quelle

Free E-Journal (出版社公開部分のみ）

Beschreibungen/Notizen

• With the gradual increase in the difficulty of competitive martial arts, athletes must complete fine, stable, high-quality and difficult movements in order to achieve excellent performance. The real-time extraction of martial arts movements is a topic that many martial arts enthusiasts care about. This research mainly discusses the real-time extraction and simulation of VR image targets of martial arts routine difficulty action technology. Considering the complex characteristics of martial arts movements, this article will analyze the preprocessing content of existing images. This includes image enhancement and image filtering, and uses median filtering methods to enhance the characteristics of the collected images. In this way, the visual effect of the original image can be improved, and the processed image will contribute to the subsequent segmentation. A new image segmentation method is proposed for the color model of the image. According to the H component of the HSV model representing the characteristics of chromaticity, the color image is transformed into the HSV model, and the H component is extracted. The histogram concept applies to H components. Based on the histogram of the H component, the segmentation threshold is determined, and the cropping target in the image is detected. Because the model space is very sensitive to color, VR technology is used to automatically determine the segmentation target. Combined with the above division methods, the automatic extraction of objects in the image is completed. The method of using VR technology for image extraction processing has high precision, and the error value is 3.92%<5%. The research results show that the VR segmentation results are good, suitable for image segmentation in complex backgrounds and automatic image extraction in complex backgrounds.