- Instructor: Prof.
- CRN: 46101
- Meeting Times: T 06:00 - 08:40 PM, Spring 2004
- Place: 163 WM 1525
This course introduces the student to computer vision algorithms,
methods and concepts which will enable the
student to implement computer vision systems with emphasis on visual pattern recognition.
Upon successful completion of this course of study a student will have general knowledge of
image analysis and processing, pattern recognition techniques,
and some experience with research in computer vision.
Topics to be studied: data structures for visual pattern representation,
feature extraction, basian theory,
decision trees, nearest neighbor, artificial neural networks, clustering, etc.
The students, once completing the course, should be competent enough
to conduct research in this area.
The students will be required to critique a current paper from the literature
in this area, present it to the class, implement the presented algorithm
and evaluate the strengths and shortcomings.
- Prerequisites: None
- Lecture Notes: can be accessed using the http://blackboard.pace.edu
Blackboard Login Procedures for Registered Students are available
- Useful Links: click here
- Project: click here.
- Tentative Schedule:
||Bayes Decision Theory
||Artificial Neural Networks in Vision|
& Matlab intro.
||Image Processing & Prj
| ||Spring Break|
||Passover (No class)|
||SOM and Segmentation|
||Skeletonization & 3D reconstruction|
||Multiple Classifier Combination|
||Project presentation & Demo|
- Project (50%): Students are required to implement
one computer vision application (Presentation and report required.)
- Homeworks (40%): 4 homeworks
- Attendane (10%):