Analyzing the Impact of Scene Transitions on Indoor Camera Localization through Scene Change Detection in Real-Time

Real-time indoor camera localization is a significant problem in indoor robot navigation and surveillance systems.The scene can change during the image sequence and plays a vital role in the localization performance of robotic applications in terms of accuracy and speed.This research proposed a real-time indoor camera localization system based on a recurrent neural network that detects scene change during the image sequence.An annotated image dataset trains the proposed system and predicts the camera pose in real-time.The system mainly improved the localization performance of indoor cameras by more accurately predicting the camera pose.It also recognizes the scene changes during the sequence and evaluates the effects of these changes.This system achieved high accuracy and real-time performance.The scene change detection process was performed using visual rhythm and the proposed recurrent deep architecture,which performed camera pose prediction and scene change impact evaluation.Overall,this study proposed a novel real-time localization system for indoor cameras that detects scene changes and shows how they affect localization performance.

实验室用数字图像处理进行三维表面轮廓诊断

表面质量和轮廓精度的测量难是许多工业行业中存在的主要问题,半成品的表面质量直接影响到后续的生产步骤。虽然有许多方法可以获得所需的数据,但根据问题的复杂性所需要的测量硬件,比如二维或三维扫描仪,都比较昂贵。因此,本文提出一种新颖的算法,并在简单的三维扫描仪模型上,以0.1 mm的分辨率的图像处理进行了验证。扫描表面轮廓的方法有很多,但图像处理是目前工业自动化中最热门的课题。最重要的是,为了获得表面图像,使用标准的高分辨率反射相机,以MatLab为软件环境实现后处理。因此,这种解决方案可以替代昂贵的扫描仪,利用单一用途设备的额外功能扩展来实现。