Pattern Theory and Applications

This is the main page of a graduate-level course in pattern theory, machine learning, pattern formation, pattern recognition and computer vision being taught in 2013/1 at the Polytechnic Institute IPRJ/UERJ. It is generally useful for computer scientists, statisticians, and applied mathematicians wishing to automatically analyze sets of images and other signals. Think of this course as a special 'flavor' or school of machine learning and pattern recognition which has been largely developed at the instructor's alma mater, Brown University.

General Info

• Instructor: prof. Ricardo Fabbri, Ph.D. Brown University
• Meeting times: Tues 6pm-7:40pm, Weds 5pm - 6:40pm
• Forum for file exchange and discussion: uerj.tk

Course Format

• Evaluation criteria: Final grade = projects (60%), class participation (20%) and reading summaries (20%)
• Each student will have a main project to develop throughout the semester
  • There will be mid and final project presentations, each worth 50% of the project grade
• There will be assigned reading almost every class (papers, book chapters, etc)
  • Readings must be summarized with personal opinions and reflexions and a summary must be typed and handed in
  • Discussion in class will be graded as "class participation"

Pre-requisites

• Undergraduate-level mathematics and probability (will review as needed)

Approximate Content

We will be reading sections of interest from Mumford's book together with complements from the others. Focus may shift based on research demand and demand from student's individual projects. We plan to focus on the following topics.

1. Overview of Pattern Theory, Machine Learning, Pattern Recognition, Computer Vision and Image Understanding. Motivation. Basic concepts.
2. Character Recognition and Syntactic Grouping. Image Understanding. (chapter 3)
3. Image Texture, Image Segmentation and Gibbs Models (ch. 4)
4. Faces and Flexible Templates (ch. 5): --> Focus of course <--
5. Natural Scenes and their Multiscale Analysis (ch. 6)
6. Catastrophe Theory - readings from Rene Thom's book. Qualitative pattern theory?

Main Resources

Textbooks

• Main book: Pattern Theory: The Stochastic Analysis of Real-World Signals, David Mumford and Agnes Desolneux (see uerj.tk)
• Pattern Theory: From Representation to Inference, Ulf Grenader
• Structural Stability and Morphogenesis, Rene Thom. We'll be complementing the course with ideas from this book, looking into this for investigating pattern formation

Lectures

Partial listing & Tentative Outline

1. Overview of pattern theory and classic pattern recognition

Keywords

Portuguese: Teoria dos Padrões, Reconhecimento de Padrões, Visão Computacional, Inteligência Artificial, Formação de Padrões