Faculty of Education, Northeast Normal University, Changchun 130021, China.
High School Attached to Northeast Normal University, Changchun 130021, China.
J Healthc Eng. 2022 Feb 18;2022:2129168. doi: 10.1155/2022/2129168. eCollection 2022.
The final loss function in the deep learning neural network is composed of other functions in the network. Due to the existence of a large number of non-linear functions such as activation functions in the network, the entire deep learning model presents the nature of a nonconvex function. As optimizing the nonconvex model is more difficult, the solution of the nonconvex function can only represent the local but not the global. The BP algorithm is an algorithm for updating parameters and is mainly applied to deep neural networks. In this article, we will study the volume holographic image library technology, design the basic optical storage path, realize single-point multistorage in the medium, and multiplex technology with simple structure to increase the information storage capacity of volume holography. We have studied a method to read out the holographic image library with the same diffraction efficiency. The test part of the system is to test the entire facial image pattern recognition system. The reliability and stability of the system have been tested for performance and function. Successful testing is the key to the quality and availability of the system. Therefore, this article first analyzes the rules of deep learning, combines the characteristics of image segmentation algorithms and pattern recognition models, designs the overall flow chart of the pattern recognition system, and then conducts a comprehensive inspection of the test mode to ensure that all important connections in the system pass through high-quality testing is guaranteed. Then in the systematic research of this paper, based on the composite threshold segmentation method of histogram polynomial fitting and the deep learning method of the U-NET model, the actual terahertz image is cut, and the two methods are organically combined to form terahertz. The coaxial hologram reconstructs the image for segmentation and finally completes the test of the system. After evaluation, the performance of the system can meet the needs of practical applications.
深度学习神经网络中的最终损失函数由网络中的其他函数组成。由于网络中存在大量的非线性函数，如激活函数，整个深度学习模型呈现出非凸函数的性质。由于优化非凸模型更加困难，因此非凸函数的解只能表示局部而非全局。BP 算法是一种用于更新参数的算法，主要应用于深度神经网络。在本文中，我们将研究体全息图像库技术，设计基本的光学存储路径，在介质中实现单点多存储，以及结构简单的复用技术，以增加体全息术的信息存储容量。我们研究了一种具有相同衍射效率的读出体全息图像库的方法。系统的测试部分是测试整个面部图像模式识别系统。系统的性能和功能已经过可靠性和稳定性测试。成功测试是系统质量和可用性的关键。因此，本文首先分析了深度学习的规则，结合图像分割算法和模式识别模型的特点，设计了模式识别系统的总体流程图，然后对测试模式进行了全面检查，以确保系统中所有重要的连接都通过了高质量的测试。然后，在本文的系统研究中，基于直方图多项式拟合的复合阈值分割方法和 U-NET 模型的深度学习方法，对实际太赫兹图像进行了分割，将两种方法有机地结合起来，形成太赫兹同轴全息图重构图像，最终完成了系统的测试。经过评估，系统的性能可以满足实际应用的需求。
