I am currently doing my thesis and it is about paper currency recognition.
My problem is I can't remove the vandals in the scanned images of the money. Does anyone know how to do this?
Thanks!
Apply the following image pre-processing techniques:
Image Resizing,
Binarization,
Marginal Noise Removal,
Skew Detection,
Adaptive Thresholding,
Image Smothening/Filter,
Erosion,
Dilation,
Search on this topics... This one is pretty tough. Goodluck.. :D
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How to detect such cracks that you can see in the attached images? I have tried some OpenCV algorithms like blob detection (cv::SimpleBlobDetector) but couldn't get any results.
It is a cropped image, the full image has some other features as well, so I am not sure thresholding can work because I have to get the bounding box of the detected crack. One way is to assign several (region of interest) ROI and try to detect within that ROI, but this crack doesn't appear at the same location in the image. Any idea?
Can this problem be solved with machine/deep learning (like object detection)? If I train a model with a crack dataset? Because the crack part of the image doesn't have lots of features so I am not sure this method will work. Please guide.
Thanks.
These cracks are difficult to detect because the image is noisy (presumably X-ray) and the contrast poor, so the signal-to-noise ratio is low.
I would try by applying a gaussian filter for denoising, but only in the horizontal direction, to preserve the horizontal edges. Then detection of the horizontal edges.
This is about what a Gabor filter does. You can try different orientations.
Use mathematical morphology operation.
By example Matlab code:
a=imread('in.png');
se=strel( 'disk', 7);
b = imgaussfilt(a,1.3);
c=b-imopen(b,se);
c=3*c;
d=imclearborder(c);
imwrite(d, 'out.png');
I have a set of images that I am using for a typical classification problem using Tensorflow. The images come in different sizes so I wrote a small piece of code to resize them all. But the question is what is the best strategy of resizing for training purposes? For example, is it better to resize them, no matter how they scale up or down, or it is better to keep the aspect ratio and add some artificial zero padding around the resized images? I believe this is a typical question with some existing studies or solutions. Appreciate your advice.
Regards,
Hamid
I'd like to know if you are aware of any algorithm to detect low-resolution faces in an image.
The image could have any resolution and the faces resolution could be as low as 10x10 or 15x15.
I am using OpenCV but I don't think the Haar classifiers provided allow me to work with resolutions so small.
Are there any other alternative?
Thanks
The Eigenface technique is (IIRC) known to work fairly well on low-resolution images. Human faces have a distinct pattern to them that's still visible at low quality, and I believe that using a sliding window technique in conjunction with this algorithm might produce good results.
I want to make an effective illumination compensation on iris images and I want this compensation to be based on color i.e. illumination compensation using color rather than texture. I corrected my images for various mechanical errors but I want a simple algorithm to compensate the illumination based on color. Any ideas?
Try subtracting a low-pass copy of the same image?
What you are interested in is white balancing (i.e. achieving color constancy). One of the simplest algorithms is the Gray-World algorithm and I would try that one first because it's very easy to implement (even though it's not very precise).
You also might want to try some Retinex based algorithms. If so, visit this site: http://www.fer.unizg.hr/ipg/resources/color_constancy/
It contains C++ implementations of several Retinex-based color constancy algorithms.
I'm trying to get an image of a blackboard readable by OCR. Naturally, most OCR software doesn't like dirty images. What image processing should I try to put the image through to clean the image up?
Have you tried the OCR software yet? It's likely that the OCR software is well suited to reading what's essentially already a black and white image.
However, if you were required to do so you could try to:
Threshold the image.
Essentially take a greyscale version of the image and turn it into black / white pixels
Perform Binary Dilation to grow the remaining objects
Perform Binary Erosion
The idea is by dilating then eroding you would remove any rough / noisy edges and then you can pass the skeletonized image to the OCR.
There are probably plenty of methods to achieve a similar result. Given that there are entire books devoted to computer vision this answer will hardly do them justice.
The only texts I have are from 1997, but surely there's been more written on the subject since.
Algorithms for Image Processing and Computer Vision - J.R. Parker
Digital Image Processing - Gonzalez / Woods
Offhand, I'd say invert the image (reverse the colors, so that the writing is black on white) and increase the contrast a bit. You can try modifying the brightness to get the erased chalk fogginess to disappear into the background.
In Photoshop, the Levels dialog may be your most useful image adjustment. Mimicking this in code is another subject, entirely.
The basis of Levels is that you adjust the max, min and midpoints of the brightness levels. Usually shown on a histogram, you adjust the points such that you obtain the desired amount of contrast, but also move the midpoint such that text in the image is the most well-defined; critical for OCR applications. By moving the midpoint you can "eliminate" the grayscale fuzz that ordinarily surrounds handwriting by causing it to disappear into the light (or dark) areas of the image.
Also you might try converting the image to 1-bit after such an adjustment, forcing everything to black or white. Sometimes this speeds up the OCR process. But be careful, it also will discard detail.
Have you tried edge detection techniques such as Roberts Cross and Sobel operator to filter noise out of the image? Without seeing the quality of the image, can't say how effective that'd be.
Not sure how constrained you are in the choice of OCR solution, but the ABBYY OCR engine (and a web API based on it, http://www.wisetrend.com/wisetrend_ocr_cloud.shtml ) includes automatic image cleanup / texture removal options.
There are commercial solutions but cleaning up board images appears to be an open problem. Add OCR to an unsolved problem, and you get... an unsolved problem.