Image Transformations

Now that you understand how images are represented in memory, we move to an extremely important topic: image transformations.

Image transformations change the geometry or appearance of an image without changing its actual meaning. They are used everywhere — from mobile camera apps to self-driving cars.

Before computers can analyze images reliably, they often need images to be resized, rotated, aligned, or adjusted. That is exactly what image transformations do.

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What Is an Image Transformation?

An image transformation is an operation that maps original pixel locations to new pixel locations.

In simple terms:

• The image stays the same conceptually
• Pixel positions or values are changed
• The structure is adjusted for processing

Transformations can affect:

• Size
• Orientation
• Position
• Scale

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Why Image Transformations Are Needed

Real-world images are rarely perfect.

• Images come in different sizes
• Objects may appear rotated or tilted
• Cameras may capture images at odd angles

Transformations help us:

• Standardize images before analysis
• Align images properly
• Improve robustness of CV models
• Prepare data for machine learning

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Common Types of Image Transformations

Most image transformations fall into a few core categories.

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Resizing Images

Resizing changes the width and height of an image.

This is one of the most common preprocessing steps in Computer Vision.

• Deep learning models require fixed image sizes
• Large images consume more memory
• Smaller images process faster

However, careless resizing can distort images. That is why aspect ratio must be handled carefully.

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Scaling Images

Scaling is similar to resizing, but it focuses on proportional size change.

Instead of specifying exact dimensions, we scale by a factor:

• Scale > 1 → image enlarges
• Scale < 1 → image shrinks

Scaling preserves shape better than arbitrary resizing.

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Translation (Shifting Images)

Translation moves the image along the X or Y axis.

This does not change the image size — only the position of pixels.

• Shift left or right
• Shift up or down

Translation is useful when objects are not centered and need alignment.

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Rotation

Rotation turns an image around a point (usually the center).

Rotation is measured in degrees:

• Positive angle → counter-clockwise
• Negative angle → clockwise

Rotation introduces empty spaces at corners, which must be handled carefully in real systems.

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Flipping Images

Flipping creates a mirror image.

• Horizontal flip → left becomes right
• Vertical flip → top becomes bottom

Flipping is widely used in:

• Face recognition
• Pose estimation
• Data augmentation

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Geometric vs Intensity Transformations

Image transformations can be classified into two broad groups.

This lesson focuses on geometric transformations. Intensity transformations will be covered later.

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How Transformations Affect Pixels

After transformation:

• Original pixel locations may disappear
• New pixel values may be interpolated
• Edges may become smoother or distorted

That is why transformation quality matters, especially in medical or autonomous systems.

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Real-World Examples

• Passport photo resizing
• Camera auto-rotation on phones
• Aligning satellite images
• Normalizing images for AI models

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Practice Questions

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Quick Quiz

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Key Takeaways

• Image transformations modify image geometry
• Resizing and scaling adjust image size
• Translation shifts image position
• Rotation changes orientation
• Flipping creates mirror images

In the next lesson, we will study color spaces — how colors are represented beyond RGB and why it matters in Computer Vision.