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.
January 13, 2021. AFOSR Grant (Award#19RT0416) on Dynamic data driven applications systems with multimodal sensing, collaborative perception and deep computing [CUNY Newswire] [CCNY News] [GSOE LinkedIn] [CCNY Tweet]
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 the CUNY Graduate Center in Spring 2020 are the winners of this year’s student poster competition (with a $1000 scholarship each) of the GC CS and DS programs, based on their course projects. “Social Distancing Using Deep Learning and 3D Computer Vision” By Bilal AbdulRahman and “Structure from Motion with Space Carving and MaskRCNN Segmentation” by Steven Alsheimer. Congratulations!
May, 2020. Mr. Yaohua Chang. Multimodal Data Integration for Real-Time Indoor Navigation Using a Smartphone. Master Thesis, Data Science and Engineering, The City College of New York. Congratulations!
August 19, 2019. Mr. GREGORY OLMSCHENK successfully defended his PhD thesis: SEMI-SUPERVISED REGRESSION WITH GENERATIVE ADVERSARIAL NETWORKS USING MINIMAL LABELED DATA – CRITICAL ISSUES, NETWORKS BEHAVIORS AND APPLICATIONS. Here are a few summary slides. Congratulations!
May 2019. Ms. Ling Zhang. Unsupervised Feature Learning for Point Cloud by Contrasting and Clustering with Graph Convolutional Neural Network, Master Thesis, Computer Engineering, The City College of New York. Congratulations!