Welcome to CCVCL

The City College Visual Computing Research Laboratory (CCVCL) is directed by Professor Zhigang Zhu in the Computer Science Department at CCNY. It serves as an experimental environment for both research and education in advanced visual and other media computing. The research activities in the CCVCL primarily focus on the understanding of 3D natural scenes and the events in the scenes from multiple sensor modalities, including visible cameras, thermal sensors, and acoustic sensors. Currently, there are two main research themes in the Lab. The first aspect of the research is three-dimensional scene modeling and rendering from images and videos. The second aspect of the research is human and other subject tracking and human signature extraction from multiple cameras and multimodal sensors. Potential applications of the advanced visual (and other media) computing range across assistive technology, Human-Computer Interaction (HCI), virtual reality, robot/human navigation, aerial/ground surveillance, content-based video coding, surveillance, security and transportation.

News and Announcements

September 14, 2021, CCNY Places 3rd In International Overhead Imagery Hackathon [CUNY Newswire] [CCNY News]

August 06, 2021. Professor Hao Tang received an NSF CISE-MSI Grant (PI: Hao Tang; Co-PIs: Zhigang Zhu, William H Seiple) [NSF Summary] [CUNY Wire]

June 03, 2021. Professor Zhigang Zhu and DSE master student Jin Chen were featured on Futurum, a magazine and online platform aimed at inspiring young people to follow a career in the sciences, research and technology.

Starting May 2021. AI4IA: Artificial Intelligence for Intelligence Analysis Colloquium Series, part of the IC CAE Center for Critical Intelligence Studies at Rutgers University. 

May 13, 2021. Mr. Xingye (Alex) Li. SnapshotNet: Self-supervised Feature Learning for Point Cloud Data Segmentation Using Minimal Labeled Data. Master Thesis, Data Science and Engineering, The City College of New York. Congratulations!

May 1, 2021. AFOSR STTR Phase I (subaward via Intelligent Fusion Technology, Inc.), Robust Multi-View Target Attitude Determination Using Models, Multi-Cues and Machine Learning. Duration: 05/01/2021-04/31/2022

January 13, 2021. AFOSR Grant (Award #FA9550-21-1-0082) on Dynamic data driven applications systems with multimodal sensing, collaborative perception and deep computing [CUNY Newswire] [CCNY News] [GSOE LinkedIn] [CCNY Tweet]

Previous Activities

November 18, 2020. Check this out: Guide to Research. September 2, 2020. Two students in Professor Zhu’s Computer Vision and Image Processing Class at
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