MS Candidate

Sarthak Agarwal

Visiting Scholar

Kun Xia

Roberto Cesar Jr., professor/department head of Computer Science at the University of Sao Paolo and director of eScience Research Center presents his research in computer vision and image processing for urban visualization. Dr. Cesar&rsquo; invited talk will be held: 
EVL&rsquo;s Continuum 
Engineering Research Facility, Room 2068 
Friday, September 29th, noon 
&ldquo;Computer vision and physical models for urban informatics&rdquo; 
Computer vision techniques have played an important role in urban data analysis. Such techniques are useful for capturing and analyzing visual information about cities&rsquo; infrastructure, social manifestations and mobility, to name just a few. However, the adoption of computer vision techniques in urban environments faces important challenges, such as the restricted heterogeneous hardware typically involved, outdoor image variations, and other uncontrolled aspects. In this talk, we present some recent results to overcome these limitations. Specifically, we discuss computer vision techniques for rain scenes, pedestrian mobility analysis, and physical models for urban green regions and graffiti distributions. 
Bio: 
Roberto is a professor at the University of Sao Paulo (USP) since 1998 (BSc in Computer Science - UNESP - 1991; MSc in Electrical Engineering - UNICAMP - 1993; Ph.D. in Physics - USP/Brazil, IPT-UCL/Belgium - 1997). He is currently a full professor in the Department of Computer Science - IME - USP working in the Data Science Research Group. He is currently a special advisor for Physical Sciences and Engineering at the Sao Paulo Research Foundation - FAPESP. He served as the Director of the eScience Research Center at USP and as the head of the Computer Science Department. He was a member of the Image and Vision Computing and the Signal, Image and Video Processing editorial boards, chair and invited speaker of conferences and workshops (Sibgrapi 2003, CIARP 2010, Sibgrapi 2011; SHAPES 2.0 - 2012, eSon - IEEE eScience 2013, IEEE eScience 2014). He has experience in computer science, with an emphasis on computer vision, machine learning, and artificial intelligence. 
<a href="https://www.ime.usp.br/~cesar/">https://www.ime.usp.br/~cesar/</a>

Roberto Cesar Jr. Invited to Present &ldquo;Computer vision and physical models for urban informatics&rdquo;

Developing sensing circuits and systems capable of perceiving the physical environment is an essential step toward next-generation cyber-physical systems that leverage computation, communication, and sensing integrated with the physical domain. The recent introduction of a class of artificial intelligence (AI) methods known as deep learning has drastically advanced vision-based environment perception. However, the applicability of these methods is significantly limited by their reliance on vast amounts of 3D annotations for training, which are expensive, unnatural, and often unobtainable. Acquiring 3D annotations necessitates specialized data captured by 3D sensors rather than utilizing abundant and readily available images or videos. Moreover, it requires a controlled environment or compromises with inaccurate subjective labeling. This project aims to address real-time environment perception in a very challenging, largely unexplored, yet highly practical setting, where only 2D annotations are available during training, by modeling and learning rich space-time structures of the environment. The outcomes of this project will significantly impact cyber-physical systems and facilitate a wide range of applications, from robots in manufacturing and personal services to autonomous vehicles that enhance people’s mobility and safety. Furthermore, this project will tightly integrate research and education at the University of Illinois Chicago (UIC), which is a Minority Serving Institution (MSI), through curriculum development, research training for high school, undergraduate, and graduate students, broadening the participation of female and minority students, and community outreach. 
This project will address real-time environment perception when only 2D annotations are available during training by systematically pursuing a novel approach that decomposes the complex physical environment into geometry and motion substructures in 3D space and models their rich space-time interactions. From streaming video, the trained system will not only recognize objects and scene layouts in the environment but also estimate their 3D geometry and 3D motion in real-time. This project is focused on (1) establishing a self-supervised framework for lifting 2D objects to 3D, by bridging geometry and motion, brightness constancy, and differentiable depth rendering, (2) scaling 2D-to-3D lifting of individual objects to perceive the entire environment, by modeling and learning short-term and long-term dynamics of the environment, and (3) achieving flexible and generalizable environment perception by handling articulated motion and out-of-distribution environments. The new approach is expected to be efficient in terms of the amount of annotations required for training, scalable to a wide range of objects, and robust to the complexity and diversity of real-world environments. This project will advance and enrich the fundamental research of visual sensing, perception, and learning. Moreover, this project will demonstrate the usability and robustness of the proposed approach in real cyber-physical systems.

CCSS: Modeling and Learning Space-time Structures for Real-time Environment Perception

EVL affiliated faculty and assistant professor of Computer Science Wei Tang has been appointed by Science Direct as associate editor of the journal &ldquo;Pattern Recognition&rdquo;.  The journal publishes developments in the fields of computer vision, image processing, text and document analysis, and neural networks. Dr. Tang&rsquo;s expertise in these fields makes him the perfect candidate for this position.  We congratulate Dr. Tang on his appointment.

Wei Tang Appointed Associate Editor for &ldquo;Pattern Recognition&rdquo;

PhD Student

Hossein Fathollahian

Unsupervised video anomaly detection (UVAD) aims to detect abnormal events in videos without any annotations. It remains challenging because anomalies are rare, diverse, and usually not well-defined. Existing UVAD methods are purely data-driven and perform unsupervised learning by identifying various abnormal patterns in videos. Since these methods largely rely on the feature representation and data distribution, they can only learn salient anomalies that are substantially different from normal events but ignore the less distinct ones. To address this challenge, this paper pursues a different approach that leverages data-irrelevant prior knowledge about normal and abnormal events for UVAD. We first propose a new normality prior for UVAD, suggesting that the start and end of a video are predominantly normal. We then propose normality propagation, which propagates normal knowledge based on relationships between video snippets to estimate the normal magnitudes of unlabeled snippets. Finally, unsupervised learning of abnormal detection is performed based on the propagated labels and a new loss re-weighting method. These components are complementary to normality propagation and mitigate the negative impact of incorrectly propagated labels. Extensive experiments on the ShanghaiTech and UCF-Crime benchmarks demonstrate the superior performance of our method. 
Funding: NSF award ECCS-2400900

Learning Anomalies with Normality Prior for Unsupervised Video Anomaly Detection

This year, students at the Electronic Visualization Laboratory (EVL) at the University of Illinois Chicago College of Engineering participated in the <a href="https://ieeevis.org/year/2024/welcome">IEEE VIS 2024 Conference</a>. 
Prof. Liz Marai&rsquo;s students had several presentations. PhD Student Siyuan Zhao presented his work &ldquo;A Part-to-Whole Circular Cell Explorer&rdquo; at the Bio+Med+Vis Workshop on Oct 13. PhD Student Andrew Wentzel presented his paper <a href="https://www.evl.uic.edu/pubs/2827">&ldquo;DITTO: A Visual Digital Twin for Interventions and Temporal Treatment Outcomes in Head and Neck Cancer&rdquo;</a> on Oct 17. 
Prof. Fabio Miranda&rsquo;s PhD Student Gustavo Moreira presented his work <a href="https://www.evl.uic.edu/pubs/2843">&ldquo;Curio: A Dataflow-Based Framework for Collaborative Urban Visual Analytics&rdquo;</a> on Oct 16.
Dr. Saeed BoorBoor presented his two papers, &ldquo;VoxAR: Adaptive Visualization of Volume Rendered Objects in Optical See-Through Augmented Reality&rdquo; and &ldquo;Submerse: Visualizing Storm Surge Flooding Simulations in Immersive Display Ecologies&rdquo;. He will join as an EVL Faculty member at the University of Illinois Chicago next term. 
UIC&rsquo;s EVL faculty Liz Marai, PhD Students Md Nafiul Alam Nipu, Siyuan Zhao, and Hossein Fathollahian also participated at the VIS Chicago Satellite Event hosted by the University of Chicago Data Science Institute. After the watch party for the plenary session, we heard from faculty and PhD students at UChicago, UIC&rsquo;s EVL, Northwestern, and DePaul.

IEEE VIS 2024: Visualization & Virtual Analytics Virtual Conference

EVL Affiliated Faculty Assistant Professor, Human-Centered Engineering Lab

Hyungil Kim

September 2023 marks the 50th anniversary of the Electronic Visualization Laboratory (EVL) at University of Illinois Chicago (UIC). EVL&rsquo;s introduction of the CAVE Automatic Virtual Environment in 1992, the first widely replicated, projection-based, walk-in, virtual-reality (VR) system in the world, put EVL at the forefront of collaborative, immersive data exploration and analytics. However, the journey did not begin then. Since its founding in 1973, EVL has been developing tools and techniques for real-time, interactive visualizations - pillars of VR. But EVL&rsquo;s culture is also relevant to its successes, as it has always been an interdisciplinary lab that fosters teamwork, where each person&rsquo;s expertise contributes to the development of the necessary tools, hardware, system software, applications, and human interface models to solve problems. Over the years, as multidisciplinary collaborations evolved and advanced scientific instruments and data resources were distributed globally, the need to access and share data and visualizations while working with colleagues, local and remote, synchronous and asynchronous, also became important fields of study. This paper is a retrospective of EVL&rsquo;s past 50 years that surveys the many networked, immersive, collaborative visualization and VR systems and applications it developed and deployed, as well as lessons learned and future plans. 
Article appears in the journal PRESENCE: Virtual and Augmented Reality, in the Special Issue on Immersive Visualization Laboratories - Past, Present and Future, edited by S. Su and W. Sherman, <a href="https://direct.mit.edu/pvar/issue/volume/33">https://direct.mit.edu/pvar/issue/volume/33</a>. 
Article copyrighted by Massachusetts Institute of Technology (MIT). 
DOI: <a href="https://doi.org/10.1162/pres_a_00421">https://doi.org/10.1162/pres_a_00421</a>

EVL faculty, students, and collaborators 1975.

Electronic Visualization Laboratory&rsquo;s 50th Anniversary Retrospective: Look to the Future, Build on the Past

September 2023 marked the 50th anniversary of the Electronic Visualization Laboratory (EVL). This paper summarizes EVL&rsquo;s efforts in Visual Data Science, with a focus on the many networked, immersive, collaborative visualization and virtual reality (VR) systems and applications the Lab has developed and deployed, as
well as lessons learned and future plans. 
Index terms: Visualization, Virtual Reality, Visual Data Science, Collaboration

An EVL CAD Rendering of the DOCC Space

Immersive Analytics at the Electronic Visualization Laboratory

The Data Observation and Computation Collaboratory (DOCC, &rsquo;dock&rsquo;) is a visual data science instrument for University of Illinois Chicago (UIC) science and engineering research and research training. Many UIC researchers are part of multi-disciplinary teams investigating and contributing to solutions for societal issues, in such fields as biology, cybersecurity, healthcare, manufacturing, urban sustainability, and cyber-physical systems (e.g., autonomous cars). DOCC is a digital &rsquo;Project Room&rsquo; that enables domain scientists and computer scientists to collaborate on real-world problems by providing them with high-performance computing, high-resolution displays, and sophisticated Artificial Intelligence (AI), data analytic, data simulation, image processing, visualization, and virtual-reality software and services. 
Societal challenges can only be addressed by research teams, with each contributing expertise in complementary areas. DOCC&rsquo;s Intellectual Merit is two-fold: it provides onsite computing with adequate support, and it provides collaboration software services. With respect to the former, DOCC is a configurable computing environment that prioritizes real-time, interactive, collaborative, data-intensive applications, but can be scheduled off-hours for batch-mode processing. Users can execute codes faster, apply more sophisticated analytics to large-scale problems, gain greater insights, and open new avenues of research. With respect to the latter, DOCC&rsquo;s collaboration software lets researchers either sit around one of DOCC&rsquo;s large tables and display walls or video-teleconference in while simultaneously sharing and interacting with their data on the large displays – which remote viewers can access online. 
To encourage adoption and ensure Broader Impact, DOCC has a visual front end, enabling users to easily access familiar collaboration tools, scientific notebooks, AI workflows, and popular visualization software. Alternatively, users can add new AI models or create them from scratch. And because DOCC is connected to the UIC research network, data and/or computer codes can be ported to remote computer and cloud resources located on campus, regionally, nationally, or internationally. DOCC personnel can assist new users make their applications more robust, dynamic and configurable. DOCC can transform scientific research, data exploration and discovery, recruit and retain new faculty, make cyberinfrastructure more accessible to students, and help educate the next-generation workforce. UIC is a Minority Serving Institution, a Hispanic Serving Institution and an Asian American and Native American Pacific Islander Serving Institution. 
DOCC and its associated data will remain active for at least three years after the completion of this project, as demand dictates. Software will be stored and archived via EVL&rsquo;s storage servers, as well as code repositories.

Image Processing

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