Course Description
Course will guide students to automatically process digital images and videos from biological systems. Students will be provided with introduction to basic concepts and methodologies for digital image processing and programming skills via Python. Basic elements include image segmentation, pattern recognition, machine learning for image classification, and object tracking.
Athena Title
Image Process Biological Syst
Pre or Corequisite
BIOL 1107 or BIOL 1107E or BIOL 2107H or CSCI 1300-1300L or CSCI 1360 or CSCI 1360E or permission of department
Semester Course Offered
Offered fall and spring
Grading System
A - F (Traditional)
Course Objectives
After completing this course, students will be able to: • Understand basics of digital images and videos • Use Python and open-source packages for automated digital image processing • Manipulate image morphology, patterns, and features • Segment, filter, and enhance images • Track and analyze specific objects of interest in videos • Learn simple supervised and unsupervised machine learning algorithms for image processing
Topical Outline
1. Basic Input/output, arithmetic operations, and region of intertest selection for image processing 2. Operations and transformation based on basic geometric patterns, such as line, circle, and ellipse 3. Morphological image processing for closing, erosion, and dilation 4. Global and local image thresholding and segmentation 5. Image filtering, smoothing, and enhancements with different kernels 6. Image pattern recognition, including gradients, edge, contour, convex, and hierarchy 7. Image feature extractions for the detection of corners and edges 8. Video analysis for analyzing pixel intensity changes and optical flow and tracking objects 9. Machine learning for image clustering, classification, and regression