Can the SVM work on data with different dimensions? (using libsvm)
If the images have different size, I can resize to a standard value.
But if they have different aspect ratios, it seems not to make sense to resize without keeping the original aspect ratio.
Or shall I pad the images with zeros to make them have the same aspect ratio?
Can the SVM work on data with different dimensions?
No it can't, but you already give an answer about how to overcome that (resizing the images).
But if they have different aspect ratios, it seems not to make sense to resize without keeping the original aspect ratio. Or shall I pad the images with zeros to make them have the same aspect ratio?
Agreed, not maintaining the aspect ratio just would make the problem even harder. Usually people pre-process all the images to one aspect ratio and use letterboxing or pillarboxing when necessary.
Related
I am working with GD library, and I'm looking for a way to detect the nearest pixel to the middle center of shapes, as well as total area used by each shape in a monochromic black-and-white image.
I'm having difficulty coming up with an efficient algorithm to do this. If you have done something similar to this in the past, I'd be grateful for any solution that would help.
Check out the binary image library
Essentially, Otsu threshold to separate out foreground from background, then label connected components. That particular image looks very clean but you might need morph ops to clean it up a bit and get rid of small holes and other artifacts.
Then you have area trivially (count pixels in component) or almost as trivially (use the weighted area function that penalises edge pixels). Centre is just mean.
http://malcolmmclean.github.io/binaryimagelibrary/
#MalcolmMcLean is right but there are remaining difficulties (if you are after maximum accuracy).
If you threshold with Otsu, there are a few pairs of "kissing" dots which will form a single blob using connected component analysis.
In addition, Otsu threshoding will discard some of the partially filled edge pixels so that the weighted averages will be inaccurate. A cure would be to increase the threshold (up to 254 is possible), but that worsens the problem of the kissing dots.
A workaround is to keep a low threshold and dilate the blobs individually to obtain suitable masks that cover all edge pixels. Even so, slight inaccuracies will result in the vicinity of the kissings.
Blob splitting by the watershed transform is also possible but more care is required to handle the common pixels. I doubt that a prefect solution is possible.
An alternative is the use of subpixel edge detection and least-squares circle fitting (after blob detection with a very low threshold to separate the dots). By avoiding the edge pixels common to two circles, you can probably achieve excellent results.
This example allows the classification of images with scikit-learn:
http://scikit-learn.org/stable/auto_examples/classification/plot_digits_classification.html
However, it is important that all the images have the same size (width and height, as written in the comments).
How can I modify this code to allow classification of images with different sizes?
You will need to define your own Feature Extraction.
In example from above, every pixel is represent a feature. If your images of different sizes, most trivial (but certainly not the best) thing that you can do is pad all images to the size of largest image with, for example, white pixels.
Here an example how to add boarders to image.
I am working on a classic RPG that requires a pixelated style of graphics. I want to do this by making a small image and scaling it up. However, when I do this, it gets fuzzy. Is there any way to scale it while keeping a crisp edge for every pixel, or do I just need to make a bigger image?
You cannot scale an image expecting it to keep a crisp aspect if it's not made in a big enough resolution in the first place. In your case you would have to make a bigger image and scale it down to make the small image.
If you do not use the large image all the time however, you should consider having two versions of the same image (one small / one large) for optimization sake.
I'm researching the the possibility of performing occlusion culling in voxel/cube-based games like Minecraft and I've come across a challenging sub-problem. I'll give the 2D version of it.
I have a bitmap, which infrequently has pixels get either added to or removed from it.
Image Link
What I want to do is maintain some arbitrarily small set of geometry primitives that cover an arbitrarily large area, such that the area covered by all the primitives is within the colored part of the bitmap.
Image Link
Is there a smart way to maintain these sets? Please not that this is different from typical image tracing in that the primitives can not go outside the lines. If it helps, I already have the bitmap organized into a quadtree.
SVG images are great for high detailed graphics, but since they consist of a number of coordinates that need to be calculated before rendering, are they potentially bad for performance, say compared to rendering a jpg which is simply drawing an array of pre-calculated pixels?
I use Context.drawImage, and I do not know if the SVG graphics need to be calculated every drawn frame of the canvas or if they are perhaps cached somehow? or maybe I'm worrying about nothing?
The performance will depend on your specific application and the complexity of your graphic, but generally speaking the vector graphics are not going to have a significant impact. Your main bottleneck will typically be in manipulating the pixel data in the canvas; the larger your canvas, the more time it will take to draw.
Unless you are redrawing the canvas every frame however, the only calculations that are performed at all are those made when you initially draw the image. When you are not modifying it, the canvas is effectively nothing more than a static bitmap.