📌Common Concepts

📚 Important Terms

Term	Description
Convolution	Applying some filter on an image so certain features in the image get emphasized

🎀 Convolution Example

🤔 How did we find -7?

We did element wise product then we get the sum of the result matrix; so:

3*1 + 1*0 + 1*(-1)
+
1*1 + 0*0 + 7*(-1)
+
2*1 + 3*0 + 5*(-1)
=
-7

And so on for other elements 🙃

👼 Visualization of Calculation

🔎 Edge Detection

An application of convolution operation

🔎 Edge Detection Examples

Result: horizontal lines pop out

Result: vertical lines pop out

🙄 What About The Other Numbers

There are a lot of ways we can put number inside elements of the filter.

For example Sobel filter is like:

1   0   -1
2   0   -2
1   0   -1

Scharr filter is like:

3    0   -3
10   0   -10
3    0   -3

Prewitt filter is like:

-1   0   1
-1   0   1
-1   0   1

So the point here is to pay attention to the middle row

And Roberts filter is like:

1    0
0   -1

✨ Another Approach

We can tune these numbers by ML approach; we can say that the filter is a group of weights that:

w1    w2   w3
w4    w5   w6
w7    w8   w9

By that we can get -learned- horizontal, vertical, angled, or any edge type automatically rather than getting them by hand.

🤸‍♀️ Computational Details

If we have an n*n image and we convolve it by f*f filter the the output image will be n-f+1*n-f+1

😐 Downsides

1. 🌀 If we apply many filters then our image shrinks.

2. 🤨 Pixels at corners aren't being touched enough, so we are throwing away a lot of information from the edges of the image .

💡 Solution

We can pad the image 💪

🧐 References

• More on Convolutional Neural Networks