How can I identify a particular shape at all degrees, while ignoring the rest?

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Kimo Kalip 2018-6-13

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评论： Kimo Kalip 2018-6-20

Hello, I have an image of various lines and blobs, and I want my image to only show the lines. To accomplish this, I have utilized watershed, regionprops, ismember, and a few other methods in order to get to the image I am now. At this point, I can't reduce the area boundries, as I'll start losing lines before I lose the blobs, and solidity wouldn't accomplish anything as all of the shapes appear to have the same solidity (probably due to how the shapes were generated.) What options do I have left? Thanks in advance!

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Florian Morsch 2018-6-14

You could try to use SURF features, but if the object you want to identify is the long line-wise one then you will have a really hard time.

Kimo Kalip 2018-6-14

Yeah, I have thousands of those longer lines at various angles (So unfortunately there is no uniform orientation). The idea is to reduce the image to the point where only those white lines remain, and all extraneous noise is weeded out - but I'm wondering if I've hit a wall. Thanks for the idea though!

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Answer 1

Image Analyst 2018-6-14

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Try the ratio of the majoraxislength to minoraxisLength.

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Kimo Kalip 2018-6-14

pic01.png

I definitely agree that it feels like an art. Luckily, a threshold of 80 seems to be working for me for the most part, but sometimes the lines are of various lengths, or what I need to detect are in fact dots, or they are different colors and shapes etc. I don't have permission to post the actual image online, but for lack of a better example I have included a picture of a donut. What I am trying to accomplish is identifying each of the lighter sprinkles on the donut (white sprinkles or frosting on the background should be considered 'noise'). Using the techniques you suggested in https://www.mathworks.com/matlabcentral/answers/166363-edge-detection-method-for-image-cropping, I've managed to weed out a lot of the background, but wondering if there is a relatively easier way of autocropping the image around the donut (When donuts can be various sizes, I can't simply hardcode a cropping, but wondering if I can just detect the edges of the circle before modifying the image further, or simply attempting to ignore the background as considered in the example). After a lot of messing with thresholds and whatnot, I'm happily convinced it is possible, but not sure if there is mathematical logic that I can program the computer to do most if not all of the adjustments necessary.

Anyway, I'll take a look at that thresholding image tutorial you linked, and I think I'm on the right track, so thanks again!

Also, if what I'm trying to accomplish sounds rather unrealistic, knowing now before I go deeper down the rabbit hole would be appreciated as well...

Kimo Kalip 2018-6-14

编辑：Kimo Kalip 2018-6-20

在 MATLAB Online 中打开

As an aside, for one of my images I have:

rows = 4318

columns = 4318

numberOfColorBands = 1

And surprisingly for the most part, this chunk of code runs almost perfectly the same unchanged (Except for the fact that apparently I don't have licensing for the Saviztky-Golay filter, but it was optional anyway)

==========================

 % Let's get the vertical profile
 sides = [grayImage(:, 1:70), grayImage(:, 300:end)];
 verticalProfile = mean(sides, 2);
 % verticalProfile = int8(verticalProfile);
 % Let's smooth it with a Saviztky-Golay filter (optional)
 % verticalProfile = sgolayfilt(verticalProfile, 2, 45);
 subplot(3, 3, 3); 
 plot(verticalProfile, 'b-', 'LineWidth', 2);
 grid on;
 title('Vertical Profile of Background', 'FontSize', fontSize);
 % Make a background image that we can divide by.
 backgroundImage = repmat(verticalProfile, [1, columns]);
    subplot(3, 3, 4);
 imshow(backgroundImage, []);
 axis on;
 title('Background Image', 'FontSize', fontSize);
 % divide the image by the profile
 flattenedImage = uint8(255 * double(grayImage) ./ backgroundImage);
 subplot(3, 3, 5);
 imshow(flattenedImage, []);
 axis on;
 title('Background Corrected Image', 'FontSize', fontSize);

========================================

Will changing some of the dimensions here to fit my actual image get rid of some of that extraneous noise in the background?

Kimo Kalip 2018-6-18

1 pixel count.PNG

So I have successfully filtered out large unwanted aspect ratios from my image, but in this variant I have several that have an aspect ratio of around 1. The problem is, this little pixel I'm zoomed in here also has a aspect ratio of 1 (I think, having a hard time verifying, but I think its a reasonable assumption).

Is there a way to get bwconncomp() to have a minimum and maximum "object" size before you run it? The way I've been doing it so far is filtering out the image prior, but if there is a way to teach the program what is and isn't a reasonable and regular size, it may be easier.

Image Analyst 2018-6-18

You can use bwareafilt() if you want instead.

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Answer 2

Image Analyst 2018-6-18

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You might enjoy learning about Hu's moments: http://www.youtube.com/watch?v=Nc06tlZAv_Q

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Kimo Kalip 2018-6-20

Pretty close more or less to what I'm trying to achieve. Does he upload a reference photo for each logo at the start there though? In my case, the idea is that there are thousands of relatively similar shaped boxes rotated around a circle, the image is grayscale, and they usually the darkest thing on the image (Although not always, which is another thing I need to account for). What I'm getting at is: in many cases I won't have a reference to provide, other than the other objects in the image - does this still work? Also, the mms function he uses to determine the moments was something he wrote himself, and not provided by matlab? Thanks for the video though! End result was pretty cool

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