Image thresholding (binary, adaptive, Otsu) in Computer Vision - Model Pipeline Trace

Model Pipeline - Image thresholding (binary, adaptive, Otsu)

This pipeline processes a grayscale image to separate objects from the background by turning pixels into black or white. It uses three methods: simple binary thresholding, adaptive thresholding that changes based on local areas, and Otsu's method which finds the best threshold automatically.

Data Flow - 4 Stages

1Input Image

256 rows x 256 columns x 1 channel→Load grayscale image→256 rows x 256 columns x 1 channel

Pixel values range from 0 (black) to 255 (white)

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2Binary Thresholding

256 rows x 256 columns x 1 channel→Apply fixed threshold (e.g., 127) to convert pixels to 0 or 255→256 rows x 256 columns x 1 channel

Pixels >127 become 255 (white), others become 0 (black)

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3Adaptive Thresholding

256 rows x 256 columns x 1 channel→Calculate threshold for small regions and apply locally→256 rows x 256 columns x 1 channel

Different parts of image have different thresholds for better detail

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4Otsu's Thresholding

256 rows x 256 columns x 1 channel→Automatically find optimal global threshold by minimizing intra-class variance→256 rows x 256 columns x 1 channel

Threshold chosen to best separate background and foreground

Training Trace - Epoch by Epoch

N/A

Epoch	Loss ↓	Accuracy ↑	Observation
1	N/A	N/A	Thresholding methods do not require training; they are rule-based.

Prediction Trace - 4 Layers

Layer 1: Input Grayscale Image

Layer 2: Binary Thresholding

Layer 3: Adaptive Thresholding

Layer 4: Otsu's Thresholding

Model Quiz - 3 Questions

What does binary thresholding do to pixels below the threshold?

A Turns them white (255)

B Turns them black (0)

C Leaves them unchanged

D Blurs them

Key Insight

Image thresholding methods convert grayscale images into clear black-and-white images by deciding which pixels belong to the object or background. Adaptive and Otsu's methods improve results by adjusting thresholds based on local details or automatically finding the best threshold, making them powerful for different lighting and image conditions.