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しきい値セグメンテーション

しきい値セグメンテーションは、主要なタスクが明るい領域から暗い領域を分離したり、前景のオブジェクトを分離したり、後の形態学、OCR、輪郭分析、または連結コンポーネント用にバイナリ イメージを準備したりする場合に役立ちます。これは、コンピューター ビジョンにおける最も一般的な前処理ステップの 1 つです。

ドキュメントのクリーンアップ、マスク生成、領域分離、二値化ワークフローのためのグローバルで適応的なしきい値処理を使用して、前景を背景からセグメント化します。

All processing runs locally in your browser. Your files never leave your device — no upload, no server, no signup required.

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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