Damon L. Woodard, Ph.D.

Professor

Dept. of Electrical and Computer Engineering
University of Florida (UF)

Director

Florida Institute for National Security (FINS)

Applied Artificial Intelligence (AAI) Group

Damon L. Woodard, Ph.D.

Professor,
Department of Electrical and Computer Engineering (ECE), University of Florida (UF)
Director,
Florida Institute for National Security (FINS) + Applied Artificial Intelligence (AAI) Group

COURSES

Course Pre-Requisites / Co-Requisites

• EEE-5502 Foundations of Signal Processing
• Undergraduate-level probability and statistics course • Undergraduate-level linear algebra course
• Exposure to MATLAB programming

Course Description

Pictorial data representation; feature encoding; spatial filtering; image enhancement; image segmentation; cluster seeking; two-dimensional z-transforms; scene analysis; picture description language; object recognition; pictorial database; interactive graphics; picture understanding machine.

Course Objectives

This course introduces students to the fundamental principles and methods used for image processing and computer vision. The goal of this course is to understand how to efficiently represent, process, and analyze image signals. The objective of the course is to provide students with the scientific foundations needed to implement and apply techniques used to address image analysis related problems. Topics to be covered include: image acquisition and display using digital devices, properties of human visual perception, sampling and quantization, image enhancement, two-dimensional Fourier transforms, linear and nonlinear filtering, morphological operations, noise removal, image deblurring, edge detection, geometric transformations, segmentation, and object recognition (classification). The course objective will be met by the completion of multiple homework assignments which require the implementation and application of image processing / computer vision methods discussed during lecture. Also, the students’ understanding of the main concepts will be assessed using multiple exams.

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Course Pre-Requisites / Co-Requisites

  • EEL 5840/EEE 4773 Fundamentals of Machine Learning

  • Recommended pre-req: EEL 4516 (Noise in Devices and Communication Systems) or EEL 3850 (Data Science for ECE)

  • Recommended pre-req: EEE 55502 (Foundations of DSP) or EEL 4750 (Foundations of DSP)

Course Description

Decision functions, optimum decision criteria, training algorithms, unsupervised learning, feature extraction and data reduction, potential functions, syntactic pattern description, recognition grammars, machine intelligence.

Course Objectives

 The objective of this course is to impart a working knowledge of several important and widely used pattern recognition topics to the students through a mixture of motivational applications and theory.

Upon completion of this course, the student will be able to:

    • Identify relevant real-world problems as instances of canonical pattern recognition problems.

    • Design and implement effective strategies for data preprocessing.

    • Derive, reason and solve pattern recognition problems using the basics of statistical learning theory.

    • Implement Python code to solve pattern recognition problems.

    • Explain and utilize concepts of pattern recognition for data science and electrical engineering.

 

Course Pre-Requisites / Co-Requisites

  • EEE-6512 Image Processing and Computer Vision is a prerequisite for the course or instructor approval.

Course Description

Methods and principles for the automatic identification/authentication of individuals. Technologies include fingerprint, face, and iris biometrics. Additional topics include biometric system design, performance evaluation, multi-modal biometric systems, and biometric system security.

Course Objectives

This course introduces students to the fundamental principles and methods used for biometric identification. The goals of this course are to understand the process of biometric recognition as well as the challenges it poses as means of establishing identity. The objective of the course is to provide students with the scientific foundations needed to design, implement, and evaluate large-scale biometric identification systems. This objective will be met by the completion of multiple homework assignments which require the application of methods of biometric identification and system evaluation discussed during lecture. Also, the students’ understanding of the main concepts will be assessed using multiple exams including a final.

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I am available to provide subject matter expertise to commercial and government entities.

CONTACT

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