Tag: Representation Learning

F6-M Adaptive Manifold Learning for Multi-Sensor Translation and Fusion given Missing Data

June 29, 2022 in 2019-2020, Project

The goal of this work is to translate streams of data from individual sensors into a shared manifold-space for joint understanding and processing. This work includes investigation of computational topology for manifold learning, data summarization, and intrinsic dimensionality estimation. In practice, for a given application, processing chains are generally developed for a particular sensor or set of sensors. 

M2-T Deep Learning for Point Cloud Denoising (renamed: 3D Point Cloud Denoising and Compression using Deep Learning)

June 29, 2022 in 2019-2020, Project

Enable efficient 3D sensing and information sharing for auto-driving and smart city with 3D point cloud denoising and end-to-end compression using deep learning architectures. We would explore deep learning architecture for point cloud processing. We formulate a multi-scale CNN based 3d point cloud feature extraction technique.

F4-M Adaptive Manifold Learning for Multi-Sensor Translation and Fusion given Missing Data

June 29, 2022 in 2018-2019, Project

The goal of this work is to translate streams of data from individual sensors into a shared manifold-space for joint understanding and processing. This work includes investigation of computational topology for manifold learning, data summarization, and intrinsic dimensionality estimation. In practice, for a given application, processing chains are generally developed for a particular sensor or set of sensors. 

F6-V Machine-Learning-Enabled Video Coding Strategy for Object Detection

June 29, 2022 in 2018-2019, Project

The goal of this project is to develop a machine-learning-enabled video coding strategy for object detection.  Most existing video encoders minimizes distortion under a rate constraint. However, for surveillance video, it is desired for a video encoder to maximize detection probability under a rate constraint. To address this, we will design a new video coding strategy that maximizes object detection probability under a rate constraint. We will locate the information important to object detector, develop Rate-Detection-Optimized framework for mode selection, and design optimized bit rate allocation method.