IMAGE PROCESSING

Prerequisites & course information

Requisites: E C E 330 and ( MATH 320 or 340 ), graduate/professional standing, or member of Engineering Guest Students Course Designation: Breadth - Physical Science Level - Advanced L&S Credit - L&S Liberal Arts and Science Grad 50% - Counts toward 50% graduate coursework requirement Repeatable for Credit: No Last Taught: Fall 2026 Learning Outcomes: 1. Process digital images using available engineering software Audience: Both Grad & Undergrad 2. Use time-domain and frequency-domain methods to analyze images and their properties Audience: Both Grad & Undergrad 3. Apply nonlinear filters to images such as morphological operations, edge-preserving nonlinearities, and optimization-based filters Audience: Both Grad & Undergrad 4. Segment images using both small-scale and large-scale techniques Audience: Both Grad & Undergrad 5. Apply classification techniques to image recognition Audience: Both Grad & Undergrad 6. Extract features from images and apply to tasks such as registration and super-resolution Audience: Both Grad & Undergrad 7. Lead team in meeting objectives of image processing operations Audience: Graduate Learning Outcomes: 1. Process digital images using available engineering software Audience: Both Grad & Undergrad 2. Use time-domain and frequency-domain methods to analyze images and their properties Audience: Both Grad & Undergrad 3. Apply nonlinear filters to images such as morphological operations, edge-preserving nonlinearities, and optimization-based filters Audience: Both Grad & Undergrad 4. Segment images using both small-scale and large-scale techniques Audience: Both Grad & Undergrad 5. Apply classification techniques to image recognition Audience: Both Grad & Undergrad 6. Extract features from images and apply to tasks such as registration and super-resolution Audience: Both Grad & Undergrad 7. Lead team in meeting objectives of image processing operations Audience: Graduate