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Filters for Detecting Patterns in Images

In CNNs, filters refer to small matrices that perform convolution operations.

They detect meaningful patterns in images and are also known as kernels.

Filters are composed of small-sized matrices and analyze specific regions of an image to extract features such as edges, color changes, and textures.


How Do Filters Work?​

Filters perform operations on the pixel values of an image to extract new features.

When applying a filter, a small-sized filter (e.g., 3×3 or 5×5) is moved over the image at a consistent interval to carry out the operation.

  1. Applying a Filter: Select a specific part of the image, multiply it by the filter's values, and then add them up.

  2. Creating a New Feature Map: As the filter moves, it creates a new image that emphasizes certain patterns from the original image.

  3. Using Multiple Filters: Different filters are applied to detect various features.

For example, an edge-detection filter highlights the outlines of objects in an image, making previously blurred outlines more distinct.


What Types of Filters Are There?​

Filters emphasize or eliminate specific features in an image.

The key roles of major filters are as follows:

  • Edge Detection Filter: Enhances lines and shapes in an image, making the boundaries of objects sharper. (e.g., detecting the outlines of letters)

  • Blur Filter: Used to reduce noise or smooth the background. (e.g., removing unnecessary small elements in a photo)

  • Enhancement Filter: Makes specific patterns (e.g., facial features like eyes, nose, mouth) or color changes more pronounced.

CNNs use combinations of these filters, and in deep neural networks, more complex filters are learned to detect even more refined features.


While traditional neural networks need to individually analyze each pixel of an image, CNNs can selectively analyze important patterns using filters.

This approach reduces computational load and allows learning while maintaining the spatial structure of the image.

In the next lesson, we'll take a short quiz based on what we've learned so far.

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