So now that i clearly explained how chain code relates to neural edge detection, So i will move

on.

My chain code is the angle to the next pixel in a outline. Not matter how bumpy or lumpy

the outline is the sum of all values of a outline are always sums to 360. So this will make

it easy to find the centroid position of any outline.

If polar regression is done to chain code outline, the

outline will form into perfect circle and will have its own radius and pixel count to identify it.

Or spline smoothing algorithm can be applied iteratively to remove noise from the out line

or to get the amount of iteration to make it a perfect circle.

Out lines of any objects can be described with chain code. And a narrow focus can follow a

a out line around silhouette. So a sting of chin code could be place into a data base

or temporal array. And a sliding window algorithm could follow the outline.

Predictions could be made of will occur a head of the sliding window based on Markov chain

model. Or LSTM or RNN could be used to re create what lies a head.

The only thing left is conscious logic make the decision of what outline to follow at branch

decision points.

Computer vision uses a kernel mask to fined edges and blobs. Brain use neurons to compare

differences of two location to find a edge. And also, if neurons find same in two different location

then they have found a blob.

I think quad tree would be good for finding edges in a image. First starting high up and then

move on to higher detail so that lot of data can be generated.

Quad tree: