An Edge Detector Finds Image Transitions, Not Automatically Seams — concept overview: Intensity transition, Detected edge, Interpretation.

An Edge Detector Finds Image Transitions, Not Automatically Seams

Pile, shadows and printed decoration can all create strong image transitions. A line returned by an edge detector does not explain what physical feature produced it.

OpenCV's Canny tutorial describes a multi-stage method for detecting image edges. OpenCV — edges.

Compare against the intended feature

For a seam-location task, inspect whether the detected lines follow the construction or merely the lighting and texture. Keep examples where the distinction is difficult.

Topic map for An Edge Detector Finds Image Transitions, Not Automatically Seams: Intensity transition; Detected edge; Interpretation.
Topic map: Intensity transition → Detected edge → Interpretation.

Decide whether more information is needed

A task may require controlled capture, additional features or a trained approach rather than another edge threshold. Judge the result against the actual location question. A visually dramatic edge map is not proof that a seam has been inspected for its physical quality.

Review distinctions for An Edge Detector Finds Image Transitions, Not Automatically Seams: Lighting; Texture; Construction.
Review distinctions: Lighting; Texture; Construction.

Source reviewed 25 October 2026. Interpretation and original diagrams: PLUSHIP / Qunhe. Examples are illustrative.

Related reading

An Unusual Image Is Not Automatically a Defective Product

Adaptive Thresholding Responds to Local Image Conditions

Image Smoothing Can Hide the Detail an Inspection Needs

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