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ELEC 474 Machine Vision Units: 3.50
Image acquisition and representation, histogramming, spatial- and frequency-domain filtering, edge detection, motion segmentation, color indexing, blob detection, interest operators, feature extraction, camera models and calibration, epipolar geometry and stereovision. The lab and assignments will emphasize practical examples of machine vision techniques to industrial and mechatronic applications.
NOT OFFERED 2024-2025
(Lec: 3, Lab: 0, Tut: 0.5)
NOT OFFERED 2024-2025
(Lec: 3, Lab: 0, Tut: 0.5)
Offering Term: F
CEAB Units:
Mathematics 0
Natural Sciences 0
Complementary Studies 0
Engineering Science 31
Engineering Design 11
Offering Faculty: Smith Engineering
Course Learning Outcomes:
- Describe the basis of computer vision, and its applicability to solving a range of pertinent problems.
- Recognize and apply methods to enhance, smooth and sharpen an image, segment an image, and detect and extract edges, corners, lines and circles.
- Describe the basic pinhole and thin lens camera models; the main steps of the SIFT feature detector; and how to calibrate a camera's intrinsic parameters.
- Model the object recognition problem and apply appropriate methods for object recognition and image reconstruction.
- Have knowledge of and experience with the OpenCV software library, and have the skill set to analyze and implement Machine Vision methods in OpenCV.