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Kapaki-pakinabang ang pagse-segment ng threshold kapag ang pangunahing gawain ay ang paghihiwalay ng madilim mula sa maliwanag na mga rehiyon, paghihiwalay ng mga bagay sa harapan, o paghahanda ng binary na larawan para sa mas huling morphology, OCR, contour analysis, o mga konektadong bahagi. Ito ay isa sa mga pinakakaraniwang hakbang sa preprocessing sa computer vision.
I-segment ang foreground mula sa background na may global at adaptive na thresholding para sa paglilinis ng dokumento, paggawa ng mask, paghihiwalay ng rehiyon, at binarization workflow.
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Thresholding converts a grayscale image to binary (black and white) by comparing each pixel to a threshold value. Pixels above the threshold become white, below become black.
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Adaptive thresholding is best for images with uneven lighting, shadows, or varying backgrounds. It calculates a different threshold for each local region of the image.
These are advanced local binarization methods from OpenCV ximgproc. They compute per-pixel thresholds using local mean and standard deviation. Sauvola and Wolf are generally better for document images. NICK works well with low-contrast text.