CISC 859 Pattern Recognition, Winter 2019

Instructor: Dorothea Blostein, 720 Goodwin Hall, 533-6537,

Lectures: Mondays and Wednesdays 8:30-9:45AM, Goodwin 521

Office hours: Mondays and Wednesdays 10:00-11:00AM, Goodwin 720


The exam is 1:00-4:00 on Friday April 12 in Goodwin 247.

A review session will be scheduled on April 8 or 9 or 10.

The exam tests your mastery of material covered in the assignments. It will include major questions on the following two topics:

The remainder of the exam will be shorter questions on various topics, similar to the following assignment problems. (Starred problems were mentioned above.) I wrote brackets around Assig 4 problem 1 because I do not expect you to memorize the sum-of-squared-error criterion or the answer to this question; you should be prepared to answer general questions about clustering. Omitted from the list above are problems that are too detailed or too time-consuming for an exam.

The exam is done without aids: closed-book and no calculators. Doing this allows me to ask simple questions such as "Describe what curse of dimensionality means" or "Give an example of training on the test data: describe a series of training&testing steps where this problem occurs". I will provide a copy of the Anderson Math Grammar with the exam, along with the derivation sequence from "expression" to "letter" (top left of course reader page 88).
I realize that being without a calculator might make you prone to simple math errors. I will not deduct marks for obvious errors like 2*3=5, provided that you are clearly showing your work so I can see that you are correct in how you are attempting to find the answer.

Textbook and Course Reader

Duda, Hart, and Stork, Pattern Classification, Second Edition, Wiley 2001.
This book is well established as the standard reference book in pattern recognition. Hardcopy is available from Queen’s bookstore and the publisher offers an e-book version.

Blostein, CISC859 Course Reader, 2019. For sale at Queen’s bookstore. The bookstore does not automatically print more copies if their stock runs out – you must make a request for them to print another copy for you.

Course description and prerequisites

As described in the course overview, this course covers statistical and structural pattern recognition. The course material is relevant to many areas of research, including data mining, artificial intelligence, computer vision and signal processing. CISC859 has been taken by graduate students from computing, electrical engineering, mathematics, mechanical engineering, geology, chemistry, and engineering physics.

Familiarity with the following subjects is expected. Students missing this background have successfully taken the course by doing extra reading.

Marking Scheme; Information about Assignments, Presentation, Project

The CISC859 marking scheme is described in detail below. As an overview, 84% of the course mark is based on evaluated work and 16% is based on participation. The evaluated work is marked using Queen's 0 to 100 scale with this conversion: exam 25%, oral presentation 15%, written report 15%, digit recognizer work 14%, digit recognizer report 15%. I don't mark the following participation components in detail, instead using A 87 as the default mark for good effort: assignments 8%, plan for oral presentation 3%, fill out feedback forms for student presentations 4%, digit recognizer part1 1%. The minimum passing mark for a graduate course is 70.

33% Assignments and exam

37% Study of a pattern recognition topic, with oral presentation and written report. Here is a list of suggested topics; or choose your own topic. 30% Digit recognizer project. Here is a project description and here are sample programs for doing image I/O in C and Java, as well as digit images for classifier training and testing.

Assignments and Schedule

This schedule may be adjusted slightly during the term.

Pattern Recognition Resources

International Association for Pattern Recognition

IAPR is the International Association for Pattern Recognition. IAPR maintains this list of internship positions in pattern recognition for undergraduate and graduate students.
The IAPR education committee provides researcher/student resources for three areas of core technology (symbolic PR; statistical PR; machine learning) and two broad families of application areas (1D signal analysis; 2D image analysis). For each area, they provide links to tutorials and surveys; explanations; online demos; datasets; books; code.
I also recommend taking at look at the information provided by the Technical Committees of the IAPR including:

Software Environments and Tools

Toolbox by Duda, Hart, Stork written in MATLAB. Read this Introduction to the DHS toolbox by graduate student Nawei Chen and me; it illustrates some of the basic pattern recognition ideas we discuss in class. You are not required to buy or use this toolbox. You can use other software tools such as the ones listed below.

Python scikit machine learning and data visualization

R is a free software environment for statistical computing and graphics. A past CISC 859 student writes: "R implements a lot of the concepts that you discuss in the course, and provides many built-in datasets (like the IRIS dataset and a car manufacturer dataset), so it's a great way to get some fast and easy experience with classifiers from a practical perspective. Coupled with the more theoretical perspective provided by the lectures, I found it to be great one-two punch."

Weka. This Weka tutorial is recommended by a former CISC859 student.


Classification in Matlab

OpenCV (Open Source Computer Vision Library) has C++, C, Python and Java interfaces and supports Windows, Linux, Mac OS, iOS and Android.

Mirage is a publicly available Java-based tool for exploratory analysis and visualization of large data sets written by Tin Kam Ho at Bell Labs; she now works at IBM Watson. Tin is one of the world's top researchers in statistical pattern recognition.

Comparisons of these tools:

Computer Vision Resources

CVonline, a compendium of computer vision. Covers many topics, such as Hidden Markov Models (HMMs).
Supplemental information with CVonline: online and hardcopy books, datasets for research and student projects, software packages

Video lectures for an introductory course on computer vision. Topics include flat part recognition, deformable part recognition, range data and stereo data 3D part recognition, detecting & tracking objects in video,and behaviour recognition

CVDICT: Dictionary of Computer Vision and Image Processing.

Other Resources

An extensive list of links for pattern recognition and statistics.

Document Layout Interpretation and its Application lists research groups, conferences, data sets, software, and bibliographies.