This project aims to translate streams of data from individual sensors into a shared manifold-space for joint understanding and processing. This work includes an investigation of computational topology and contrastive learning for manifold learning.
The goal of this project is to advance the point-cloud post-processing using deep learning method to understand the global and local manifolds of a 3D object.
The goal of this project is to develop novel deep learning algorithms for video segment hashing and identification to support efficient and accurate duplicates identification and removal from phones and cloud storages.
The goal of the project is to secure authentication of a template, especially a biometric query, without compromising the template, the database, or the query; in case of database attack or a corrupted communication channel.
We propose comprehensive resources and models for understanding automatically
transcribed videos. In particular, in this project, we pursue a deep learning model for identifying the
important points and questions mentioned in a video transcript. To achieve this objective, we employ two specific deep learning models.
The goal of this project is to advance the point-cloud post-processing using deep learning method to understand the global and local manifolds of a 3D object.
The goal of this project is to develop novel deep learning algorithms for video segment hashing and identification to support efficient and accurate duplicates identification and removal from phones and cloud storages.
The goal of the project is to secure authentication of a template, especially a biometric query, without compromising the template, the database, or the query; in case of database attack or a corrupted communication channel.
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.
We propose to tackle denoising problem using deep learning into two folds: outlier removal and denoising. We used two stage deep learning pipeline where first stage acts as a binary classifier that classifies 3d points either as outlier or non-outlier. The second stage receives non-outlier and noisy points from the first stage and learns the underlying manifold to produce residual noise from the reference(true) surface.