A Line of Insight from Simplifying Contours
Technical bottlenecks often stem not from a lack of knowledge, but from misdefining the problem.
In 2019, I worked on a project that extracted clothing from 2D images and applied them to OpenGL-based 3D characters. Background removal was handled easily via thresholding, but generating meshes along the garment’s contour exploded the computation. Segmentation algorithms (like GrabCut) were overkill for the task. The essence of the problem was: “Keep only the essential points along the edge.” I found the answer in a GIS algorithm — Douglas-Peucker.
This algorithm simplifies complex polylines by retaining only the key points — reducing data without losing the essence. Exactly what I needed.
I later tested GPT with the same problem. It offered predictable answers. Why? Because I described the problem like a “pathfinding” issue.
This experience taught me: How you define the problem matters more than the answer itself.
"It wasn’t segmentation. It was contour simplification. I should have redefined the problem before choosing an algorithm."
— Developer Kihoon
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