Course Title:

Image Processing - Introduction

Instructor:

To Be Assigned

Course Description:

It is an understatement to say that today's automobile is becoming more intelligent.  Given today's algorithms and the availability of low-cost, high-powered digital signal processors, even the complex task of image processing is a candidate technology that may solve some of the difficult problems such as occupant detection, lane-departure warning, drowsy-driver detection and collision avoidance, just to name a few.   Designers recognize that a single automotive image processing system can perform multiple functions due to the richness of information contained in image sequences. This course provides an introduction to image processing, beginning with an overview of all it's variation in both technology and application areas and then focusing on automotive applications -- specifically feature detection and motion detection.

 

Course Objectives:

At the end of this course, participants will be able to:

1.  Identify and understand key technologies that are currently available and in development for image processing in automotive applications.

2.  Apply two-dimensional FIR digital filters to images

3.  Develop edge-finding

3.  Develop a feature-tracking system using the Lucas-Kanade algorithm

4.  Perform image segmentation

 

Main Topics to Be Covered:

* Technology overview of image processing

* Image representation

* Color representation

* Filtering Images

* Edge finding algorithms

* Combine image derivatives

* Lucas-Kanade feature tracking

* Identifying features in an image with motion tracking

 

 

Intended Audience:

Engineers and managers who would like to become familiar with image processing applications and solutions

Method of Delivery:

Lecture. Class notes provided via provided in paper-form and web site

Course Duration:

2 hours

 

Prerequisites:

N/A but technical background or interest suggested

Computer Requirements:

Should have a laptop with MATLAB software (no toolboxes req’d)

 

 

 

 

Enrollment Size:

Minimum #:

10

Maximum #:

40

Course Content:

Applied:

80%

Theoretical:

20%