Thresholding Techniques in Computer Vision

Thresholding techniques in computer vision are used to separate objects from the background by converting images into binary format. This is one of the most important preprocessing steps in image segmentation, object detection, and OCR systems.

In this lesson, you will learn different types of thresholding techniques using OpenCV.

What are Thresholding Techniques in Computer Vision?

Thresholding techniques in computer vision convert a grayscale image into a binary image (black and white) based on a threshold value.

• Pixel value > threshold → White (255)
• Pixel value < threshold → Black (0)

This helps in identifying important regions in an image.

Types of Thresholding Techniques

1. Simple Thresholding

Applies a fixed threshold value to the entire image.

import cv2

gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
_, thresh = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)

Types of Simple Thresholding

• THRESH_BINARY
• THRESH_BINARY_INV
• THRESH_TRUNC
• THRESH_TOZERO
• THRESH_TOZERO_INV

2. Adaptive Thresholding

Adaptive thresholding calculates threshold values for smaller regions.

adaptive = cv2.adaptiveThreshold(gray, 255,
                                 cv2.ADAPTIVE_THRESH_MEAN_C,
                                 cv2.THRESH_BINARY,
                                 11, 2)

Advantages

• Works well under varying lighting conditions
• More accurate than simple thresholding

3. Otsu’s Thresholding

Automatically determines the optimal threshold value.

_, otsu = cv2.threshold(gray, 0, 255,
                        cv2.THRESH_BINARY + cv2.THRESH_OTSU)

Advantages

• No need to manually set threshold
• Works well for bimodal images

Difference Between Thresholding Techniques

Why Thresholding Techniques in Computer Vision are Important

Thresholding techniques in computer vision help:

• Separate foreground from background
• Simplify image data
• Improve segmentation accuracy
• Prepare images for OCR and detection

They are widely used in document processing and object detection.

Real-World Applications

• Document scanning and OCR
• Medical image segmentation
• License plate detection
• Object detection preprocessing

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FAQs

What are thresholding techniques in computer vision?

They are methods used to convert images into binary format for segmentation.

What is the difference between simple and adaptive thresholding?

Simple uses a fixed value, while adaptive uses local values.

What is Otsu thresholding?

It automatically finds the optimal threshold value.

Why is thresholding important?

It simplifies images and helps in object detection.

Where is thresholding used?

It is used in OCR, medical imaging, and AI applications.