Computer Vision and Image Processing Laboratory

In a state-of-the-art research lab, students of all types – undergraduate, graduate, and doctoral – make breakthrough discoveries in image analysis and computer vision. Welcome to Speed School's Computer Vision and Image Processing Laboratory(CVIP).

Computer Vision and Image Processing Laboratory


This website provides overview of our research projects, publications and other technical activities at the Computer Vision and Image Processing (CVIP) Laboratory . It also provides information about the CVIP Laboratory members, affiliates, and its Director, Prof. Aly Farag.

A Bit of History

The CVIP Lab was founded by Dr. Aly Farag after he joined the University of Louisville (UofL) in August 1990. The Lab major research focus is on four areas: Biomedical Imaging (Computer-aided diagnosis for early detection of colorectal and lung cancers, and image-guided interventions); Computer Vision (Smart Systems and Autonomous Robotics); Biometrics (Facial information modeling, face recognition at a distance, and modeling human engagement); and Dental Imaging (3D Modeling and Reconstruction of the Human Jaw and Image-guided dental interventions). Under the umbrella of these four research areas, the Lab has very well stablished long-term projects, namely:

Human Jaw Modeling (1992-present):

its aim is to develop portable, non-ionizing, non-radiating and inexpensive technologies for 3D modeling and reconstruction of the human jaw.

Smart systems (1994-now):

its aim is to utilize information from multiple sensors (multi-sensor fusion) for autonomous mobility. It is a direct application for computer vision in robotics (e.g., autonomous navigation, auto-refueling, self-driving cars and social robotics)

Biomedical Image Analysis (1998-now):

its aim is to develop robust methods for segmentation, registration, and visualization of biomedical images obtained using Magnetic Resonance Imaging (MRI) or Computed Tomography (CT). Our most important contribution in this field has been robust methodologies for early colorectal cancer detection using virtual colonoscopy.

Facial Biometrics (2006-now):

its aim is to develop non-obtrusive and non-invasive methods for modeling facial information. Our main contributions have been designing a robust system for Face Recognition at a Distance (FRAD), Face Recognition in the wild and an end-to-end System for Real-time Monitoring of Student Engagement in STEM Classes. The later project has started in 2016 and is one of the most promising projects in the CVIP Lab.

Through years the CVIP Lab has succeeded to secure both federal and industrial fundings and without these fundings the CVIP Lab will never exist. It is worth noting that over the last 23 years the CVIP Lab has graduated and trained over 70 PhD and MS/MENG students, published over 290 conference papers and 56 journal papers, and conducted 5 US patents.



Virtual Colonscopy

Multi View-Based 3D Reconstruction of the Human Teeth

Measuring Student Engagement In Stem Classes

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Publications have been produced by the CVIP

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Graduated PhD and Masters Students


Patents have resulted from research conducted in the CVIP