Morphological Image Operations Explorer with Adjustable Kernels

Added Jun 2025 3 design docs

Morphological operations, dilation, erosion, opening, and closing, are essential for cleaning up binary images before measurement or recognition, but their behavior depends entirely on kernel size and shape in ways that are hard to grasp from equations alone. Practitioners who cannot predict what an opening operation will do to their image end up tuning pipelines by superstition. The intern builds a web application with Streamlit and OpenCV that makes these operations observable. Users upload an image, choose an operation, and drag sliders to adjust the structuring element's size and shape while the processed result updates live beside the original. The intern implements all four core operations, adds views that show how opening removes small noise specks while closing fills small holes, and lets users chain operations to see how real preprocessing pipelines are composed. Sensible defaults and explanatory captions make the tool useful as a classroom demonstration. Through the project the intern develops an engineer's intuition for binary image cleanup, one of the most frequently needed and least taught skills in applied computer vision, while practicing interactive interface design in Python. The finished explorer demonstrates they can build educational tooling that converts theory into visible cause and effect.

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