Image Thresholding MCQ
Global fixed T, histogram-based Otsu, adaptive local thresholds, and when binarization helps or hurts.
Global
Single T
Otsu
Auto T
Adaptive
Local T
Binary map
0 / 255
Thresholding for segmentation
Thresholding partitions pixels into foreground and background (or multiple levels). It is fast but assumes separable intensity distributions.
Otsu’s method
Chooses T by maximizing between-class variance for a bimodal-ish histogram—automatic global threshold when classes are separable.
When to use what
Global
One T for the whole image—simple, fails under uneven illumination.
Adaptive
Local mean/Gaussian-weighted thresholds per neighborhood—handles shading gradients.
Invert & polarity
Know whether objects are dark-on-bright or bright-on-dark; invert maps if needed.
Post-process
Morphology often cleans threshold noise (salt-and-pepper) before contour extraction.
Pipeline snippet
Optional blur → Threshold → Morphology → Contours / features