CODEWITHSUNDEEP
imageimage-processing2dcomputer-visionedge-detection
C graphics
C graphics

Reputation: 7458

One dimensional edge detection

Instead of edge detection of a 2D image, I would like to detect edges on every single row (i.g. a line) of an image separately. That is detection of edges from an input 1D vector whose values are pixel intensities ranging from 0 to 255 ( image below): enter image description here

I would like to detect the major edges as appear in the sample input( image below) enter image description here

Upvotes: 10

Views: 11319

Answers (3)

Matthias Odisio
Matthias Odisio

Reputation: 2038

One way to get to your desired result is to adapt the 2D Canny edge detector as follows (code in Mathematica):

First, compute the spatial derivative using a Gaussian derivative filter, setting the sigma value relative to the scale of the edges you want to detect. Take the absolute value of the result.

d = Abs@GaussianFilter[data, {{10, 5}}, 1];

Then, determine a threshold automatically to cluster the previous derivative values in two groups (here using Otsu's method).

thrd = FindThreshold[d];

Then, detect the steps of the derivative values (transitions into/from the "dead band").

steps = Flatten@Image`StepDetect[d, thrd]["NonzeroPositions"];

At this point you have the ends of the edges:

ListLinePlot[data, Epilog -> {Red, PointSize[Large], Map[Point[{#, data[[#]]}] &, steps]}]

enter image description here

Optionally--it seems that's what you'd like--keep only the lowest ends of the edges. Clustering the data points at the ends of the edges works in this case, but I'm not sure how robust it is.

t = FindThreshold@data[[steps]];
steps2 = Select[steps, data[[#]] <= t &];

ListLinePlot[data, Epilog -> {Red, PointSize[Large], Map[Point[{#, data[[#]]}] &, steps2]}]

enter image description here

Upvotes: 10

user1196549
user1196549

Reputation:

Given the nice contrast of these edges, there is an easy solution that will work robustly: detect all monotonous sequences of pixel values (strictly increasing or decreasing). You will keep the sequences having a total height above a threshold (50 in your case) to reject the noisy peaks.

As a byproduct, you'll get the starting and ending points (not exactly where you expect them though, but this can be improved on if needed).

Barcodes ?

Upvotes: 3

Martin Beckett
Martin Beckett

Reputation: 96109

So you are looking for a particular change in slope - ie a certain change in Y per sample?

Isn't it simply look at the difference in Y between two samples and if it's absolute value changed by more than some limit mark that as an edge?

Upvotes: 1

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