In image processing, detecting local changes — such as edges, contours, or intensity transitions — is a fundamental task. These features often reveal structure, shape, and boundaries in visual data.
To accomplish this, high-pass filters are used to highlight abrupt changes in pixel intensity. One classic method involves applying simple derivative kernels, like the [−1, 0, 1] operator, which estimates the first derivative and emphasizes local transitions.
However, there's a catch:
These basic filters are extremely sensitive to noise, often detecting edges where there are none.
In my latest article, I explain how to:
Understand the role of discrete derivatives in edge detection
Reduce noise interference through image smoothing
Combine smoothing and differentiation into a single, efficient kernel
Whether you're a beginner in computer vision or refreshing your signal processing fundamentals, I think you'll find it insightful.
Read the article here
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