RAP – VISION AND IMAGE PROCESSING (VIP) RESEARCH GROUP

Yuhao Chen

Yuhao Chen — Tue, 25 Apr 2023 17:01:31 +0000

Yuhao Chen is a Research Assistant Professor at the Vision and Image Processing Lab (VIP) in systems design engineering at university of Waterloo. He was a postdoctoral fellow at the VIP lab from 2020-2023. He obtained his B.A.Sc. and Ph.D. degrees in Electrical and Computer Engineering from Purdue University in 2015 and 2019, respectively, while being a member of the Video and Image Processing (VIPER) laboratory. With a strong passion for exploring the cutting-edge technologies, Yuhao’s research has been focused on developing computer vision and artificial intelligence solutions for industrial applications, specifically in the fields of precision health and precision manufacturing. He currently leads projects in food image analysis, medical imaging for covid, efficient Neural Radience Field, data augmentation using diffusion models for food and cancer images, 3d human pose and motion for hockey games, human avatar and motion from 2d images and videos, implicit representation for 3D and 2D geometries, multi-task learning for robotic vision systems.

Yuhao is a chair for IAAI-24. He also co-chaired IAAI-23, CVIS2021, CVIS2022.

Besides research, Yuhao builds and manages GPU-servers for the VIP lab. He enjoys playing board games and ping pong with students.

Personal website: https://yhc-1.github.io/yuhao-chen/

Research interests

Computer vision, machine learning, deep learning, precision agriculture, precision health, precision manufacturing

Publications

Journal Articles / Conference Papers

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M. Javad Shafiee

Mohammad Shafiee — Fri, 31 Mar 2023 17:15:19 +0000

I am a Research Assistant Professor in the Department of Systems Design Engineering at University of Waterloo under the supervision of Prof. Alexander Wong. I received my Ph.D. under supervision of Prof. Alexander Wong and Prof. Paul Fieguth. My Bachelor’s degree is in Computer Science and Engineering from Shiraz University in Iran in 2008 and I received my my master degree in Artificial Intelligent in 2011 from same university.

Currently, I am working on efficient deep learning methods for embedded systems with application on autonomous driving cars, traffic monitoring and healthcare.

My PhD thesis topic was about introducing a “Randomly-connected Non-Local Conditional Random Fields” to address long-range spatial connectivity while maintaining computational complexity.

My master thesis was focused on A Novel Conditional Random Field Framework For Object Tracking supervised by Prof. Zohreh Azimifar.

Supervisors

Alexander Wong, Paul Fieguth

Research interests

My research interests include Computer Vision, Machine Learning and Biomedical Image Processing which my main focus is on Graphical Models especially Deep Learning, Conditional Random Fields and Markov Random Fields.

Research topics

Biomedical Imaging
Computer Vision
Discovery Radiomics
Evolutionary Deep Intelligence
Image Segmentation/Classification
Stochastic Models
Video Analysis

Publications

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Linlin Xu

Linlin Xu — Wed, 29 Mar 2023 19:13:55 +0000

I am a Research Assistant Professor in the Department of Systems Design Engineering, working on AI, machine learning, Earth Observation and Environmental Remote Sensing projects.

In my key research areas, I have demonstrated excellence, leadership and recognition. I, as principal investigator, have successfully secured many competitive grants, including NSERC Discovery Grant. I have published one book chapter, 66 journal papers on high-rank remote sensing journals and 41 conference articles on high-impact conferences.

I have been involved in many invited talks and presentations to different audiences, and I frequently receive reviewer invitations from high-impact remote sensing journals and conferences. I serve as the Associate Editor of Frontiers in Environmental Science (Section: Big Data, AI and the Environment).

LinkedIn: linkedin.com/linlinxu.

Google Scholar: https://scholar.google.com/linlinxu

Email: l44xu@uwaterloo.ca

Research interests

With the explosion of Earth observation (EO) data, fast EO market growth is bottlenecked by the lack of intelligent analytics that can automatically transfer large-volume, ever-increasing, noisy, heterogeneous raw EO data into compact, real-time, value-added information products. I tailor-design cutting-edge AI solutions to enable automatic generation of scalable EO information products in a high-precision, fast and cost-effective manner to better support various key applications in environmental monitoring, resource exploration and climate change studies. I have strong expertise on AI and machine learning (ML), geospatial data science, different remote sensing techniques (i.e., Hyperspectral, Lidar and SAR remote sensing), and their environmental monitoring applications, e.g., land cover/use classification, hyperspectral mapping of biophysical, biochemical, and geochemical parameters, oil spill detection, and high-resolution Pan-Arctic sea/lake ice mapping.

Students

Supervision – Current

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Ph.D

M.A.Sc

Supervision – Completed

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Ph.D

M.A.Sc

Research topics

Computer Vision
Image Segmentation/Classification
Remote Sensing
Stochastic Models

Research demos

Compressed Sensing
Disparate Scene Registration
Grid Seams: A fast superpixel algorithm for real-time applications
MAGIC System
SAR Sea Ice Image Synthesis
Satellite SAR Sea Ice Classification
Texture Classification

Publications

Journal Articles / Conference Papers

Journal Articles

Conference Papers

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