Scene Matching Method for Children’s Psychological Distress Based on Deep Learning Algorithm

In the process of children’s psychological development, various levels of psychological distress often occur, such as attention problems, emotional problems, adaptation problems, language problems, and motor coordination problems; these problems have seriously affected children’s healthy growth. Scene matching in the treatment of psychological distress can prompt children to change from a third-person perspective to a first-person perspective and shorten the distance between scene contents and child’s perceptual experience. As a part of machine learning, deep learning can perform mapping transformations in huge data, process huge data with the help of complex models, and extract multilayer features of scene information. Based on the summary and analysis of previous research works, this paper expounded the research status and significance of the scene matching method for children’s psychological distress, elaborated the development background, current status, and future challenges of deep learning algorithm, introduced the methods and principles of depth spatiotemporal feature extraction algorithm and dynamic scene understanding algorithm, constructed a scene matching model for children’s psychological distress based on deep learning algorithm, analyzed the scene feature extraction and matching function construction of children’s psychological distress, proposed a scene matching method for children’s psychological distress based on deep learning algorithm, performed scene feature matching and information processing of children’s psychological distress, and finally conduced a simulation experiment and analyzed its results. The results show that the deep learning algorithm can have a deep and abstract mining on the characteristics of children’s psychological distress scenes and obtain a large amount of more representative characteristic information through training on large-scale data, thereby improving the accuracy of classification and matching of children’s psychological distress scenes. The study results of this paper provide a reference for further researches on the scene matching method for children’s psychological distress based on deep learning algorithm.

Detection of Good Matching Areas Using Convolutional Neural Networks in Scene Matching-Based Navigation Systems

This paper presents a solution for false matching detection in scene matching-based aerial navigation systems. A navigation system that uses normalized cross-correlation to match a captured image with a reference image was designed. While traditional methods rely on statistical indicators to detect false matchings, this research relied on deep learning using Convolutional Neural Network (CNN). A CNN was trained to online predict the probability of a matching result to be true or false. The training dataset of images was constructed depending on the knowledge of where good matching areas are expected to be. The probability numbers were stored as an assistant map to be used again with the same reference map without classification. The system was implemented and tested in a 3D simulation environment using models for a drone, camera, and flight environment. The Robot Operating System (ROS) and the 3D dynamic simulator Gazebo were used for simulation. The results proved the efficiency of the proposed method in excluding the false matchings. Using the assistant map without classification resulted in an execution time of 41ms and RMS error of position less than 1.2m.