College of Wushu and Dance, Shenyang Sport University, Liaoning, Shenyang 110102, China.
Comput Intell Neurosci. 2022 Jun 1;2022:2440263. doi: 10.1155/2022/2440263. eCollection 2022.
With the vigorous development of higher education in China, many universities have made great progress in various indicators in recent years. As the number of college students increases year by year, the effect of instruction in the classroom is especially important. The high quality of teaching directly affects the efficiency of students' listening to lectures, and more and more universities are receiving attention. However, the traditional dance classroom education and the one-to-many education model cannot adapt to the development trend of higher art education under the changes of the times and cannot effectively guarantee the quality of classroom education. The development of wireless sensor networks provides practical and feasible technical solutions for the development of dance education systems. Compared with general detection methods, image sensors can provide more real-time and more intuitive on-site information and wirelessly send image information to user terminals. This article describes the classic feature extraction algorithm and proposes a new feature extraction algorithm based on chart filling. The effectiveness of each algorithm is verified through several data sets. Image recognition is carried out by computer, including from computer to image processing, through the computer to recognize objects and various different modes of the target technology. The identification process usually includes several steps. First, the preprocessing of the image is required, then the segmentation of the image is performed, and then the feature extraction and matching are performed. In layman's terms, image recognition hopes to imitate the human heart to read photos. By applying the image recognition technology to the dance education system, changes in the methods and forms of dance education can be stimulated.
随着中国高等教育的蓬勃发展，近年来许多大学在各项指标上都取得了长足的进步。随着大学生人数的逐年增加，课堂教学的效果尤为重要。教学质量的高低直接影响学生听课的效率，越来越多的高校受到关注。然而，传统的舞蹈课堂教育和一对多的教育模式已经不能适应时代变化下高等艺术教育的发展趋势，不能有效保证课堂教育的质量。无线传感器网络的发展为舞蹈教育系统的发展提供了实用可行的技术解决方案。与一般的检测方法相比，图像传感器可以提供更实时、更直观的现场信息，并将图像信息无线发送到用户终端。本文描述了经典的特征提取算法，并提出了一种基于图表填充的新特征提取算法。通过几个数据集验证了每种算法的有效性。图像识别是由计算机完成的，包括从计算机到图像处理，通过计算机识别物体和各种不同模式的目标技术。识别过程通常包括几个步骤。首先需要对图像进行预处理，然后对图像进行分割，再进行特征提取和匹配。通俗地说，图像识别希望模仿人类的心脏来读取照片。将图像识别技术应用于舞蹈教育系统，可以激发舞蹈教育方法和形式的变化。
