I want to be able to create vector files like Illustrator does on the iPhone. Does anyone know of an algorithm?
for each pixel try to grow by testing against it's neighbours for colour similarity with a threshhold. keep growing until no more expansion is possible due to threshold then you make a path using the outermost border pixels. Now repeat for the other pixels in the orignal raster image which were not already included in your previous expansions.
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I have an ellipse in the image.After segmentation i got a broken ellipse as shown .which morphological operation is used to get the perfect ellipse
Actual input file is
output obtained is
i tried imopen ,but i will lose lower ellipse like structure .how to close the upper ellipse like structure without losing lower ones
Mask i created is
i want to segment the ellipse like structure.but some of these structures are connected with rectangular like bodies.how to separate it. erode will eliminate small ellipses
If you want to reconnect something with a mathematical morphology operator, do not use an opening (it increases the gap), but a closing (imclose)! The names are explicits.
In you case, you want to reconnect something vertically cut, so use a horizontal structuring element (type segment).
And yes, you have to invert your image, black pixels representing the absence of information.
Usually, for closing gaps, you would need the close operator.
However, since most software assume active pixels are white, you would either need to invert the image, or use the open operator.
On this image, in matlab, the following works well:
imopen(I,ones(32))
This uses a square structuring element. You may want to experiment with other shapes.
Your example also looks like you moved half of the ellipse, as opposed to some process which deleted pixels in the middle. No simple morphological operation can create a perfect ellipse out of the sample image, unless you use the knowledge that multiple components can be moved to re-form the ellipse. If that is the actual case, you can scan connected components and try to match them together.
I have scanned copies of currency notes from which I need to extract only the rectangular notes.
Although the scanned copies have a very blank background, the note itself can be rotated or aligned correctly. I'm using matlab.
Example input:
Example output:
I have tried using thresholding and canny/sobel edge detection to no avail.
I also tried the solution given here but it detects the entire image for cropping and it would not work for rotated images.
PS: My primary objective is to determine the denomination of the currency. There are a couple of methods I thought I could use:
Color based, since all currency notes have varying primary colors.
The advantage of this method is that it's independent of the
rotation or scale of the input image.
Detect the small black triangle on the lower left corner of the note. This shape is unique
for each denomination.
Calculating the difference between 2 images. Since this is a small project, all input images will be of the same dpi and resolution and hence, once aligned, the difference between the input and the true images can give a rough estimate.
Which method do you think is the most viable?
It seems you are further advanced than you looked (seeing you comments) which is good! Im going to show you more or less the way you can go to solve you problem, however im not posting the whole code, just the important parts.
You have an image quite cropped and segmented. First you need to ensure that your image is without holes. So fill them!
Iinv=I==0; % you want 1 in money, 0 in not-money;
Ifill=imfill(Iinv,8,'holes'); % Fill holes
After that, you want to get only the boundary of the image:
Iedge=edge(Ifill);
And in the end you want to get the corners of that square:
C=corner(Iedge);
Now that you have 4 corners, you should be able to know the angle of this rotated "square". Once you get it do:
Irotate=imrotate(Icroped,angle);
Once here you may want to crop it again to end up just with the money! (aaah money always as an objective!)
Hope this helps!
I'd like to resize the components contained in a 3D binary image sequence without changing any of the dimensions of the sequence itself.
I'm not sure if I need to do it on a component-by-component basis, if yes, then how do I create a transform such that the resized components are re-positioned 'correctly' in the image sequence? By 'correctly', I mean with the same centre of mass as the original unprocessed components.
(If that last paragraph doesn't make sense then please ignore)
A 2D example: suppose I wanted to enlarge by 10% the white blobs in the following [295x445] image
How would you do this without making the image itself larger?
you could use the imdilate function to dilate the regions of interest. The examples in the webpage show how to use this function.
I would like to check whether an image has a lot of homogeneous areas. Therefore I would like to get some kind of value of an image that declares a ratio for images depending on the amount/size of homogeneous areas (e.g. that value could have a range from 0 to 5).
Instead of a value there could be some kind of classification as well.
[many homogeneous areas -> value/class 5 ; few homogeneous areas -> value/class 0]
I would like to do that in perl. Is there a package/function or something like that?
What you want seems to be an area of image processing research which I am not familiar with. However, GraphicsMagick's mogrify utility has a -segment option:
Use -segment to segment an image by analyzing the histograms of the color components and identifying units that are homogeneous with the fuzzy c-means technique. The scale-space filter analyzes the histograms of the three color components of the image and identifies a set of classes. The extents of each class is used to coarsely segment the image with thresholding. The color associated with each class is determined by the mean color of all pixels within the extents of a particular class. Finally, any unclassified pixels are assigned to the closest class with the fuzzy c-means technique.
I don't know if this is any use to you. You might have to hit the library on this one, and read some research. You do have access to this through PerlMagick as well. However, it does not look like it gives access to the internals, but just produces an image based on parameters.
In my tests (without really understanding what the parameters do), photos turned entirely black, whereas PNG images with large areas of similar colors were reduced to a sort of an average color. Whether you can use that fact to develop a measure is an open question I am not going to investigate ;-)
I have a 2D color-map plot created with imagesc and want to export it as a .eps file using
print -depsc.
The problem is that the "original" image data is from a rather small matrix (131 x 131). When I view the image in the matlab figure window, I can see all the individual pixels if I zoom a bit closer.
When I export to eps, however, there seems to be some interpolation or anti-aliasing going on, in that neighboring pixels get blurred/blended into each other. I don't get the problem if I export a high-resolution tiff, but that format is not an option (as demanded by a publisher).
How can I obtain an eps that preserves the pixely structure of my image without applying interpolation or anti-aliasing?
The blurring actually depends on the rendering software your viewer application or printer uses. To get good results all the time, make each pixel in your image an 8x8 block of pixels of the same color. The blurring then only affects the pixels at the edge of each block. 8x8 blocks are best as they compress without nasty artifacts using DCT compression (sometimes used in eps files).
Old question, but highly ranked in Google, so here is my answer:
Open the .eps-file with a text editor, search for "Interpolate" and change the following "true" to "false". Repeat that step for all Interpolate-statements.
It might also depend on the viewer you're using, but probably just because some viewers ignore the "Interpolate"s...
Had the same problem using plot2svg in Matlab and exporting from Inkscape to eps.