Howtosignalprocessing
- What does this kernel do in a convolution?
- Why is kernel flipped in convolution?
- How does a kernel work in image processing?
- What is a kernel in CV?
What does this kernel do in a convolution?
Convolution is using a 'kernel' to extract certain 'features' from an input image. Let me explain. A kernel is a matrix, which is slid across the image and multiplied with the input such that the output is enhanced in a certain desirable manner.
Why is kernel flipped in convolution?
Basically it's because time goes along the x axis with the small time values on the left and the big (later) time values on the right. So if you start shifting in, you're having the big time values hit your signal first, which is not right (causal). So you have to flip it to make the small time values shift in first.
How does a kernel work in image processing?
An image kernel is a small matrix used to apply effects like the ones you might find in Photoshop or Gimp, such as blurring, sharpening, outlining or embossing. They're also used in machine learning for 'feature extraction', a technique for determining the most important portions of an image.
What is a kernel in CV?
OpenCV blurs an image by applying what's called a Kernel. A Kernel tells you how to change the value of any given pixel by combining it with different amounts of the neighboring pixels. The kernel is applied to every pixel in the image one-by-one to produce the final image (this operation known as a convolution).
