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# Common Concepts

Term | Description |

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

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 🙃

An application of convolution operation

Result: horizontal lines pop out

Result: vertical lines pop out

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

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.

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`

- 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 .

Last modified 2yr ago