The final exam will be on Monday December 8, 1:00-4:00PM, in Dunning Hall Room 27
CISC859 is an introductory course in pattern recognition, suitable for students with general background in computer science or electrical engineering.
The recognition of patterns is required in many application areas. The type of data that is analyzed varies greatly. Examples of one-dimensional data include speech signals, electrocardiograms, and seismic data. Examples of two-dimensional data include scanned document images, medical images, and satellite images. Three-dimensional data arises in image sequences, in crystallography, and in tomography. The goal of pattern-recognition research is to develop general, domain- independent techniques for data analysis.
This course introduces statistical and structural pattern recognition techniques. Statistical pattern recognition techniques are useful for solving classification problems. For example, a character recognizer classifies a pattern into one of the 26 categories "a" to "z". Similarly, during signature validation, we classify a signature into one of the two categories "real" or "forgery". To apply statistical pattern recognition, we choose a set of features and characterize the distribution of feature values for each pattern class; we then classify an unknown pattern based on its observed feature values.
Structural pattern recognition, a newer branch of pattern recognition, constructs descriptions of internal pattern structure. For example, image analysis often involves constructing a description, rather than just a classification, of a picture. A description contains information about the primitives in the picture and about the relations among the primitives. Syntactic pattern recognition (one form of structural pattern recognition) uses grammatical techniques to describe and analyze the structure of a pattern. Grammars and parsing methods for one-dimensional strings are extended in various ways to apply to two-dimensional and three-dimensional patterns. The syntactic treatment of errors and uncertainty is a subject of ongoing research.
Lectures reflect my interest in document-image recognition: the interpretation of scanned images of documents containing text and/or figures. Pattern recognition problems arising in this area include character recognition, layout analysis (e.g. separation of figures from text), and recognition of diagram notations used for music, chemical structures, mathematics, engineering drawings and so on.
Prerequisites
Digit Recognizer Assignment
Click here for sample programs
and images for the digit recognizer assignment.
Technical Writing and Oral Presentations
Assignment 1
Assignment 2. Note: The Pattern Recognition Resources (below) provide a tool for evaluating the Normal density, if you want to use that.
Assignment 3
Assignment 4
Assignment 5
Here is a website for evaluating the normal density. For example, you can use this in assignment 2, to obtain a numerical value for the probability of error when p(x | w) is normally distributed. Alternatively, you can leave your answer for P(error) in the form of an integral.
Here is a description of a Digit Classifier written by Henry Xiao (student in CISC859 in Fall 2004). You may find this site useful. In particular, the "Downloads" section allows you to download Henry's training and testing data.
The Pattern Recognition Files. This site provides extensive information about: introduction to pattern recognition, list of journals, books, review papers, job announcements, conferences, professional organizations, news groups, research groups.
IAPR The International Association for Pattern Recognition. Much useful information can be found at the IAPR website. In particular, I recommend taking at look at the information provided by the various Technical Committees of the IAPR, including the following committees:
Document Layout Interpretation and its Application is a site that lists research groups, conferences, data sets, software, and bibliographies.
Here is a tutorial on the Nearest Neighbor Rule for pattern classification.
Related to Pattern Recogntion: CVonline, a compendium of computer vision. Covers many topics, including Hidden Markov Models (HMMs).