Master the essential preprocessing techniques that transform raw visual data into model-ready inputs for computer vision systems. This course empowers you to systematically prepare image data through normalization and color-space conversions, then advance to extracting meaningful motion information from video sequences. You'll apply pixel value normalization, execute color transformations between RGB, grayscale, HSV, and BGR formats, then implement optical flow algorithms and frame differencing to capture temporal dynamics. By completing this course, you'll be able to:

Process Images, Create Captioning AI Models
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Process Images, Create Captioning AI Models
This course is part of Vision & Audio AI Systems Specialization

Instructor: Hurix Digital
Included with
Recommended experience
What you'll learn
Image preprocessing using normalization and color-space conversion ensures stable training and consistent model performance.
Optical flow and frame differencing complement motion analysis, helping systems capture scene dynamics over time.
Preprocessing is essential for vision tasks, directly affecting model convergence, stability, and real-world results
Motion feature extraction links static images with dynamic understanding for recognition, tracking, and navigation.
Skills you'll gain
Tools you'll learn
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March 2026
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