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Learn Thresholding Computer Vision Tutorial, validate concepts with Thresholding Computer Vision MCQ Questions, and prepare interviews through Thresholding Computer Vision Interview Questions and Answers.
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
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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