CODEWITHSUNDEEP
matlabimage-processingcomputer-visionlinecontour
Autonomous
Autonomous

Reputation: 9075

Evaluate straightness of an arbitrary countour

I want to get a metric of straightness of contour in my binary image (relatively faster). The image looks as follows:

Contours

Now, the contours in the red box are the ones which I would like to be removed preferably. Since they are not straight. These are the things I have tried. I am as of now implementing in MATLAB.

1.Collect row and column coordinates of each contour and then take derivative. For straight objects (such as rectangle), derivative will be mostly low with a few spikes (along the corners of the rectangle).

Problem: The coordinates collected are not in order i.e. the order in which the contour will be traversed if we imaging it as a path. Therefore, derivative gives absurdly high values sometimes. Also, the contour is not absolutely straight, its an output of edge detection algorithm, so you can imagine that there might be some discontinuity (see the rectangle at the bottom, human eye can understand that it is a rectangle though it is not absolutely straight).

2.Tried to think about polyfit, but again this contour issue comes up. Since its a rectangle I don't know how to apply polyfit to that point set.

Also, I would like to remove contours which are distributed vertically/horizontally. Basically this is a lane detection algorithm. So lanes cannot be absolutely vertical/horizontal.

Any ideas?

Upvotes: 0

Views: 1289

Answers (2)

bla
bla

Reputation: 26069

You can segment your image using bwlabel, then work separately on each bwlabel connected object, using find. This should help solve your order problem.

About a metric, the only thing that come to mind at the moment is to fit to an ellipse, and set the a/b (major axis/minor axis) ratio (basically eccentricity) a parameter. For example a straight line (even if not perfect) will be fitted to an ellipse with a very big major axis and a very small minor axis. So say you set a ratio threshold of >10 etc... Fitting to an ellipse can be done using this FEX submission for example.

Upvotes: 1

JustinBlaber
JustinBlaber

Reputation: 4650

You should look into the features of regionprops more. To be fair I stole the script from this answer, but here it is:

BW = imread('lanes.png');
BW  = im2bw(BW);
figure(1),
subplot(1,2,1);
imshow(BW);

cc = bwconncomp(BW);
l = labelmatrix(cc);

a_rp = regionprops(CC,'Area','MajorAxisLength','MinorAxislength','Orientation','PixelList','Eccentricity');
idx = ([a_rp.Eccentricity] > 0.99 & [a_rp.Area] > 100 & [a_rp.Orientation] < 70 & [a_rp.Orientation] > -90);

BW2 = ismember(l,find(idx));

subplot(1,2,2);
imshow(BW2);

You can mess around with the properties. 'Orientation', 'Eccentricity', and 'Area' are probably the parameters you want to mess with. I also messed with the ratios of the major/minor axis lengths but eccentricity basically does this (eccentricity is a measure of how "circular" an ellipse is). Here's the output:

enter image description here

I actually saw a good video specifically from matlab for lane detection using regionprops. I'll try to see if I can find it and link it.

Upvotes: 2

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