This lesson explains what it means for a computer vision system to “see.” Instead of treating images as meaningful scenes right away, a vision system begins with numeric pixel values, organizes them into useful representations, and then applies models that detect patterns such as edges, textures, shapes, objects, motion, and spatial relationships.
Students will learn the practical pipeline behind visual computing: image capture, digitization, preprocessing, feature representation, model inference, and interpretation. The lesson keeps the focus on foundational concepts so later lessons can build toward specific tasks such as classification, object detection, segmentation, tracking, and deep learning architectures.
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