Detect patterns with image filters
See how a small kernel combines neighboring pixels to reveal local patterns.
- Calculate a simple local difference and connect it to edge detection.
A filter computes a new pixel value from nearby pixels. Convolution slides a small grid of weights, called a kernel, across an image and combines each neighborhood with those weights. Edge filters respond to rapid intensity changes. This local operation is a building block in traditional image processing and convolutional neural networks.
1row = [10, 10, 200]
2left_change = row[1] - row[0]
3right_change = row[2] - row[1]
4print(left_change, right_change)0 190
This one-dimensional difference highlights a sharp change. A two-dimensional edge kernel checks changes in both directions. Filter outputs can be negative or exceed the input range, so image pipelines often scale or clip values for display. OpenCV’s filtering guide demonstrates applying a kernel across an image.
Key takeaways
Calculate a simple local difference and connect it to edge detection.
Small arrays make vision ideas concrete.
Check model performance across varied real-world examples.
Lesson quiz
4 questions · pass with 3 correct · up to 50 XP
Passing this quiz completes the lesson and keeps your streak going. Questions you miss come back in review sessions later.
Practice: apply computer vision with Python
Use small pixel arrays to explore vision concepts, run your code against sample images, and connect each result to the larger computer vision idea.
Find the strongest neighboring change
Read a JSON list of at least two grayscale values. Find the largest absolute difference between adjacent values and print it.
- One strong edge
- Small variations
Python runs in a sandboxed browser worker with a 60 second time limit. Its runtime loads from the Pyodide CDN; your code stays in this browser.
Questions about this lesson
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