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阈值分割

当关键任务是将暗区与亮区分离、隔离前景对象或为以后的形态学、OCR、轮廓分析或连接组件准备二值图像时,阈值分割非常有用。它是计算机视觉中最常见的预处理步骤之一。

通过用于文档清理、掩模生成、区域分离和二值化工作流程的全局和自适应阈值,将前景从背景中分割出来。

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Frequently Asked Questions

What is image thresholding?

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.

What is Otsu thresholding?

Otsu\

When should I use adaptive thresholding?

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.

What are Niblack, Sauvola, Wolf, and NICK?

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.

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