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:

• Apply normalization and color-space conversions to preprocess image data • Apply optical flow and frame differencing techniques to extract motion features from video This course is unique because it combines fundamental preprocessing with advanced motion analysis in practical, hands-on implementations. To be successful in this project, you should have a background in Python programming, basic computer vision concepts, and familiarity with NumPy arrays.e.g. This is primarily aimed at first- and second-year undergraduates interested in engineering or science, along with high school students and professionals with an interest in programming.