Image thresholding

Image thresholding classifies pixels by comparing their intensities with a threshold, often producing a binary image. A global threshold uses the same value throughout the image; adaptive thresholding permits the threshold to depend on position and nearby intensities.^[ref]

For example, defineIntensities $99,100,101$ then become $0,0,1$. The comparison at equality matters: a rule using $I\ge 100$ would instead give $0,1,1$. OpenCV's THRESH_BINARY uses the strict greater-than rule, assigning its chosen maximum output value only above the threshold.^[ref]

Threshold choice affects which distinctions survive. Two different bright intensities can become the same foreground value, while a small change near the boundary can change a pixel's classification. The resulting mask can then feed connected-component labeling, where pixel connectivity separately decides which selected pixels form a region.

A useful motivating experiment keeps one image of dark marks fixed while adding or recording a gradual illumination variation across the background. Compare a global threshold with a declared local-neighborhood rule, inspecting lost strokes and falsely selected background. OpenCV documents local mean and Gaussian-weighted rules with an offset; this addresses varying illumination rather than proving that every resulting region is a meaningful object.^[ref]

A histogram can guide global selection, but intensity statistics alone do not determine semantic identity. Noise and overlapping foreground/background intensities can make every threshold imperfect. Report the input representation, comparison convention and any filtering or local-window settings so that a thresholded result can be reproduced.