Edge Detection Comparison Lab for Sobel, Laplacian, and Canny

Added Jun 2025 3 design docs

Edges are where images carry most of their information, and edge detection remains fundamental to feature extraction, object boundary finding, and image understanding. Yet the classic operators behave very differently, and learners rarely develop a feel for those differences by reading formulas, since the interesting behavior only shows up on real images with noise and texture. The intern builds an educational Streamlit application where users upload any image and apply the three landmark techniques implemented through OpenCV: the Sobel operator, which computes directional gradients; the Laplacian, which responds to intensity change in all directions; and the Canny detector, which layers gradient analysis with non-maximum suppression and hysteresis thresholding to produce clean edge maps. Results render side by side for direct comparison, and the intern exposes the parameters that matter, kernel sizes for the gradient operators and the dual thresholds for Canny, so users can watch edge maps sharpen, fragment, or dissolve as values change. The project gives the intern a durable understanding of gradient-based image analysis, hands-on skill with OpenCV and Python, and a portfolio piece demonstrating they can explain classical computer vision through interactive software rather than slides.

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