Deep Learning Approaches to Bed-Exit Monitoring of Patients, Factory Inspection, and 3D Reconstruction
In this dissertation, we dedicate ourselves to applying deep-learning-based computer vision algorithms to industrial applications in 2D and 3D image processing. More specifically, we present the application of deep-learning-based image processing algorithms to the following three topics: RGB-image-based shipping box defect detection, RGB-image-based patients' bed-side status monitoring, and an RGBD-image-based 3D surface video conferencing system. These projects cover 2D image detection of static objects in industrial scenarios, 2D detection of dynamic human images in bedroom environments, and accurate 3D reconstruction of dynamic humanoid objects in video conferencing. In each of these projects, we proposed ready-to-deploy pipelines combining deep learning and traditional computer vision algorithms to improve the overall performance of industrial products. In each chapter, we describe in detail how we utilize, modify, and enhance the architecture of convolutional neural networks, including the training techniques using data acquisition, image annotation, synthetic datasets, and other schemes. In the relevant sections, we also present how post-processing steps with image processing algorithms can improve the overall effectiveness of the algorithm. We hope that our work demonstrates the versatility and advantages of deep neural networks in both 2D and 3D computer vision applications.
- Doctor of Philosophy
- Electrical and Computer Engineering
- West Lafayette