| 3 credits | Prepared by Venkat Gudivada, May 2018 |
P: CSCI 2540; MATH 2228 or MATH 2283. Mathematical techniques and algorithms for image sampling, quantization, intensity transformations, spatial filtering, Fourier transforms, frequency domain filtering, restoration and reconstruction, morphological image processing, and segmentation.
Earth-orbiting satellites, gigapixel cameras, smartphones, and smart cameras are generating images at unprecedented levels. Digital image analysis offers advanced algorithms and tools for extracting information from images for decision-making.
How do you recover car license plate number from a blurred image of a speeding car?
How do you enhance noisy diagnostic medical images for enabling better diagnosis?
How do you automatically count the number of red blood cells in a sample?
How do you automate detection of optical defects in contact lenses?
How do you record light reflection of crops and use it to vary the amount of fertilizer spread?
How do you estimate crop yield by using satellite imagery?
How do you robots with vision?
In digital image processing course, you learn theory, algorithms, tools, and best practices to solve the above problems and more. This course gives you practical knowledge and skills to solve a wide range of problems that are vision-centric. Why are you waiting?
Course grade is based on four components:
| Activity | Weight (%) |
|---|---|
| Assignments (paper-and-pencil) | 20 |
| Assignments (programming) | 30 |
| Midterm exam | 20 |
| Final exam | 30 |
| Grade | Meaning |
|---|---|
| A | Achievement substantially exceeds basic course expectations |
| A− | |
| B+ | |
| B | Achievement exceeds basic course expectations |
| B− | |
| B+ | |
| C | Achievement adequately meets basic course expectations |
| C− | |
| D+ | |
| D | Achievement falls below basic course expectations |
| D− | |
| F | Failure – achievement does not justify credit for course |