IIT’s Visual Computing Lab

Note: this description needs to be updated. For a more up to date description see more recent publications


The Visual Computing Lab is a research group within the Computer Science department which is directed by Dr. Gady Agam. The mission of the lab is to conduct research in advanced computational techniques concerning Visual Computing. Visual Computing is a broad term that encompasses several areas in computer science including: computer vision, computer graphics, image and video processing, geometric modeling, and pattern recognition.

In general, we target the development and application of image analysis techniques to medical, scientific, industrial, and military problems in which automatic or semi-manual processing of images and volumetric data is required to inspect, recognize, enhance, or complement other sensory information. Applications of image based rendering to augmented and virtualized reality and other synthetic environments. Image based perceptual user interfaces, multimedia databases and multimedia computing, image based biometrics, document analysis and recognition.

Research funding to projects involving the Visual Computing Lab in recent years totaled roughly $1.5M. The majority of the funding was provided by the National Science Foundation (NSF), the National Institute of Health (NIH), the National Security Agency (NSA), and the Pritzker Institute for Biomedical Engineering.

keywords: 3D Modeling, Artificial Intelligence, Bioinformatics, Biomedical Engineering, Classification Theory, Computer Graphics, Computer Storage and Retrieval, Computer Vision, Diagnostic Imaging, Digital Imaging, Digital Systems Processing, Geometry, Image Analysis, Image Processing, Image Reconstruction and Restoration, Image Science, Machine Vision, Medical Imaging, Medical Informatics, Multimedia or Interactive Communications Technology, Pattern Recognition, Robotics, Signal Analysis, Signal Processing, Surveillance Systems, Target Recognition, Virtual Reality

Research activities

Current research activities in the lab focus on the following areas of applications: medical imaging, document imaging, geometric modeling and 3D biometrics. A short description of recent and ongoing activities in the lab is provided below. Research on medical imaging is done in conjunction with researchers in the radiology department at the university of Chicago and the biomedical engineering department at IIT. Research on document imaging is done in conjunction with researchers in the University of Maryland, the university at Buffalo, and the IR group at IIT. Some of the research on geometric modeling is done in conjunction with researchers in the mechanical engineering department at IIT. Below is a short review of examples application of current and past projects in the lab.

Medical imaging

Diffusion tensor warping. Visual analysis of 3D neuroimaging modalities such as fMRI, MRI and DTI brain imaging is a significant component in research and clinical practice. Using imaging techniques it is possible to assess brain activity in humans and study the reorganization of brain activity after disabling conditions. This can then be used to asses conditions, plan interventions, and monitor recovery. We are developing computational techniques for automated analysis, interpretation, and visualization of brain structure and function in fMRI, MRI and DTI brain imaging data. We are particularly interested in developing computational tools to facilitate the study of functional and morphometric variability in human brains by registering brain data sets of different subjects with and without abnormalities.

Automated lesion detection. Lesion segmentation in MRI scans is used for lesion quantification as pertaining to various medical conditions. We developed a novel technique for chronic stroke lesion segmentation based on multiple modalities including T1-weighted and T2-weighted images as well as diffusion tensor-based modalities. The approach is based on a mixture-parametric probabilistic model whereas the model parameters are optimized by maximizing the incomplete-data log-likelihood function through expectation maximization. The mixture components are selected to have Cauchy distributions. A probabilistic prior is computed by evaluating the feature vectors for a set of registered brain scans in a control set.

Vasculature extraction in Thoracic CT scans. Vessel tree reconstruction in volumetric data is a necessary prerequisite in various medical imaging applications. We are developing a novel approach to vessel tree reconstruction and its application to nodule detection in thoracic CT scans using correlation-based and probabilistic enhancement filters. The correlation-based enhancement filters we are developing depend on first-order partial derivatives and so are less sensitive to noise compared with Hessian-based filters. Additionally, multiple sets of eigenvalues are used so that a distinction between nodules and vessel junctions becomes possible.

Vessel enhancement. Vessel enhancement in volumetric data is a necessary prerequisite in various medical imaging applications. Ideally, vessel enhancement filters should enhance vessels and vessel junctions while suppressing nodules and other non-vessel elements. We developed probabilistic vessel models from which novel vessel enhancement filters capable of enhancing junctions while suppressing nodules are derived. The filters we developed are based on eigenvalue analysis of the structure tensor which is a first order differential quantity and so are less sensitive to noise.

Document imaging

Complex document information processing (CDIP). We investigate the problem of content-based image retrieval in the context of complex document images. Complex documents typically start out on paper and are then electronically scanned. These documents have rich internal structure and might only be available in image form. Additionally, they may have been produced by a combination of printing technologies (or by handwriting); and include diagrams, graphics, tables and other non-textual elements. Large collections of such complex documents are commonly found in legal and security investigations. We have developed a test collection of several million documents and a prototype system to ingest it.

Degraded document enhancement. There are numerous collections of scanned documents. Examples of such collections include historical collections, legal depositories, medical archives, and business archives. Moreover, in many situations such as legal litigation and security investigations scanned collections are being used to facilitate systematic exploration of the data. It is almost always the case that scanned documents suffer from some form of degradation. Large degradations make documents hard to read and substantially deteriorate the performance of automated document processing systems. We are developing statistical techniques for degraded document image enhancement in this context.

Writer identification. Writer identification in offline handwritten documents is a difficult task with multiple applications such as authentication, identification, and clustering in document collections. For example, in the context of content-based document image retrieval, given a document with handwritten annotations it is possible to determine whether the comments were added by a specific individual and find other documents annotated by the same person. We are investigating techniques for deriving canonical stroke frequency descriptors from handwritten text to identify writers. We demonstrated that a relatively small set of canonical strokes can be successfully employed for generating discriminative frequency descriptors.

Signature recognition. Offline signature recognition is an important form of biometric identification that can be used for various purposes. Similar to other biometric measures, signatures have inherent variability and so pose a difficult recognition problem. We are developing a novel approach for solving the curve correspondence problem that is not limited by the requirement of one dimensional parametrization. Our approach utilizes particle dynamics and minimizes a cost function through an iterative solution of a system of first order ordinary differential equations. The approach is, therefore, capable of handling complex curves for which a simple parametrization is not available.

Geometric modeling

Adaptive mesh subdivision. We are developing novel techniques to adaptively subdivide mesh surfaces using existing (e.g. \sqrt3, Loop, and Catmull-Clark) subdivision schemes. In modifying global adaptive subdivision schemes to perform local subdivision, it is necessary to guarantee smooth transition between subdivided regions and regions left at the original level to prevent the formation of surface artifacts at the boundaries between such regions. We are developing techniques for incremental adaptive subdivision that prevent and decouple cracks and degenerate faces by subdividing iteratively without forming artifacts. User selectable error measures enhanced by hysteresis thresholding, guide a dynamic selection process. Smoothness across different subdivision depths is obtained by the postponement of local atomic operations.

Accurate curvature estimation. Accurate curvature estimation in discrete surfaces is an important problem with numerous applications. Curvature is an indicator of ridges and can be used in applications such as shape analysis and recognition, object segmentation, adaptive smoothing, anisotropic fairing of irregular meshes, and anisotropic texture mapping. We are developing a new framework for accurate curvature estimation in discrete surfaces. The proposed framework is based on local directional curve sampling of the surface where the sampling frequency can be controlled. This local model has a large number of degrees of freedoms compared with known techniques and so can better represent the local geometry.

3D face recognition. Automated 3D object analysis and recognition is an essential component in numerous applications such as manufacturing automation, quality control, autonomous navigation, indexing and retrieval of object models, computer aided diagnosis in medical imaging, and biometrics. Biometrics in particular has become a focal point of research with significant importance. We are investigating a general framework that may be applied to a wide range of applications, and target specifically the application of 3D surface-based biometrics.

Novel view-synthesis. Synthesizing new views from existing ones has numerous applications. Approaches for view synthesis rely on dense or sparse matching between existing views. In all cases, some parts of the original images are inevitably unmatched and so the synthesis of new views of the corresponding regions need to rely upon sparse sets of matched points. Triangulation based upon sparsely matched points constitutes a possible solution for the handling of unmatched regions. However, such a triangulation should respect the underlying geometry of the corresponding scene. We developed techniques for physically valid scene triangulation which is then used for novel view synthesis.

Current and former lab members

Current PhD and MS (~) Students

  • Ying Chen

  • Xu Ouyang

  • Pramod Adithya Muntimadugu

  • Lawrence Amadi

  • Kaiyue Zhu

  • Haithum Elhadi

  • Grant Nikseresht

  • Thenappan Meyyappan (~)

  • Silviu Andrei (~)

  • Yihe Chen (~)

  • Rodrigo Aranguren Carmona (~)

Graduated PhD Students

  • Bingqing Xie. Gene Prioritization Based on Network and Feature Information
  • Di Ma. Automated Indexing and Annotation of Lecture Videos
  • Lin Gan. Piecewise Nonlinear Image Registration
  • Xi Zhang. Data Synthesis for Object Recognition
  • Shizhu Liu. User generated data analysis and utilization
  • Yuval Merhav. Exploiting knowledge in unsupervised open information extraction
  • Tayo Obafemi-Ajayi. Computational models for historical document image enhancement
  • Xiaojing Tang. A sampling framework for local surface geometry estimation and analysis
  • Changhua Wu. Computational techniques for vessel-based analysis of thoracic CT scans ​

Graduated MS Students (Thesis)

  • Guangyao Ma. Point cloud fusion of aerial and vehicle LIDAR
  • Xi Xhang. Learning-base mesh quality estimation
  • Di Ma. Efficient 3D-based subdivision interpolation
  • Ravinder Singh. Subdivision interpolation for medical image analysis
  • Gulsher Bal . Degraded document image enhancement
  • Mandar Soman. DT-MRI feature extraction
  • Suresh Suneel. Incremental adaptive subdivision of mesh surfaces

Graduated MS Students (Project)

  • Megha Lokanadham
  • Cristina Fernandez Garcia
  • Alena Hoxsey
  • Rachael Affenit
  • Petter Castro
  • Imane Rifki
  • Sylvain Cassiau
  • Souham Biswas
  • Zihan Niu
  • Gustavo Cali
  • Adam Muhs
  • Jerome Sebastie Boe
  • Come Genetet
  • Nathan Navarro
  • Andi Zang
  • Manishankar Janakaraj
  • Brian Long
  • Damien Wetterwald
  • Zoe Dubois
  • Julian Mulla
  • Dolega Bart
  • Hoo-Nyong Jang
  • Yves Vilbe
  • Eric Hamery

MuscleX Developers

  • Grant Nikseresht
  • Xintian Li
  • Jiranun Jiratrakanvong
  • Miguel Menendez Alvarez
  • Jiaqi Li
  • Jinjian Shao

BS Students (Project)

  • Tung Nguyen
  • Tuan Tran
  • Lucas Myers
  • Qian Peng
  • Jiang Lan
  • Daniel Weiss
  • Daniel Chong