I have 1000 of images having watermark (text). The position of watermark is random.
I just want to remove it using matlab as manually using (photoshop) is big task.
My question is, what should be work flow.
Any help about literature, link, routines in matlab etc.
You need to use an Image Recovery algorithm and more specifically an inpainting alogrithm would serve your purpose.
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I have 10,000 examples 20x20 png image (binary image) about triangle. My mission is build program, which predict new image is whether triangle. I think I should convert these image to 400 features example, but I don't know how convert fastest.
Can you show me the way?
Here are a image .
Your question is too broad as you dont specify which technologies you are using , but in general you need to create a vector from an array , that depends on your tools , for example if you use python(and the numpy library) you could use flatten().
image_array.flatten();
If you want to do it manually you just need to move every row to a single row.
The previous answer is correct. Yet I want to add something to it:
The example image that you provided is noisy. This is rather problematic as you are working with only binary images. Therefore I want to suggest preprocessing, such as gaussian filter or edge detection. Denoising will improve your clustering algorithms accuracy stronlgy (to my knowledge).
One important question:
What are the other pictures showing? Do you have to seperate triangles from circles? You will get much better answers if you provide more information.
Anyhow, my key message is: Preprocessing is vital for image-processing.
I have an image for mobile phone credit recharge card and I want to extract the recharge number only (the gray area) as a sequence of number that can be used to recharge the phone directly
This is a sample photo only and cannot be considered as standard, thus the rectangle area may differ in position , in the background and the card also may differ in size .The scratch area may not be fully scratched , the camera's depth and position may differ too . I read a lots and lots of papers on the internet but i can't find any thing that could be interesting and most of papers discuss detection of handwritten numbers .
Any links or algorithms names could be very useful .
You can search the papers on vehicle plate number detection with machine learning methods. Basically you need to extract the number first, you may use sobel filter to extract the vertical edges , then threshold (binary image) and morphologic operations (remove blank spaces between each vertical edge line, and connect all regions that have a high number of edges). Finally retrieve the contour and fill in the connected components with mask.
After you extract the numbers , you can use machine learning method such as neural network and svm to recognize them.
Hope it helps.
Extract the GRAY part from image and then Use Tesseract(OCR) to extract the text written on the gray image.
I think you may not find the algorithm to read from the image on the internet. Nobody will disclose that. I think, if you are a hardcore programmer you can crack that using your own code. I tried from the screenshots where the fonts were clearer and the algorithm was simple. For this, the algorithm should be complex since you are reading from photo source instead of a screenshot.
Follow the following steps:
Load the image.
Select the digits ( By contour finding and applying constraints on area and height of letters to avoid false detections). This will split the image and thus modularise the OCR operation you want to perform.
A simple K - nearest neighbour algorithm for performing the identification and classification.
If the end goal was just to make a bot, you could probably pull the text directly from the app rather than worrying about OCR, but if you want to learn more about machine learning and you haven't done them already the MNIST and CIFAR-10 datasets are fantastic places to start.
If you preprocessed your image so that yellow pixels are black and all others are white you would have a much cleaner source to work with.
If you want to push forward with Tesseract for this and the preprocessing isn't enough then you will probably have to retrain it for this font. You will need to prepare a corpus, process it similarly to how you expect your source data to look, and then use something like qt-box-editor to correct the data. This guide should be able to walk you through the basic steps of retraining.
Is there a possibility to reduce calculation time when stitching more than two images with opencv stitcher class? I noticed that it grows rather exponentially the more images I want to stitch (why?). Is it possible that opencv stitcher tries to stitch every single image with every other image because I got a defined order for stitching my images. So maybe this would be a way to reduce calculation time. I hope you understand what I mean and maybe you can give me some advice to solve my problem.
Do you have a gpu available?
An easy way to dramatically reduce you processing time is to use GPU when possible.
Opencv makes it easier every day, and if you look at the doc you'll see that there is a GP flag for the stitcher.
Here is the doc
You want to play with that element:
--try_gpu (yes|no) Try to use GPU. The default value is 'no'. All default values are for CPU mode.
Be careful that you need OpenCV to be compiled with GPU support for this to work. You can find more information about GPU support in OPencv here.
And if you cannot use GPUs, as #perfanoff said downsampling your image is 99% of the time a good idea in image processing.
The OpenCV Stitcher class matches every input image with all other images, thus an exponential runtime. It is indeed possible to modify the example code provided by OpenCV to match only the first with the second image, the second with the third image and so on. This will result in a more or less linear runtime.
To reduce the rumtime it even further you can reduce the input image size, the kind of features (sift and surf are slow but more robust than others), and the threshold for bundle adjustment.
My current project involves transcribing texts in pdf into text files, and I first tried putting the image file directly into OCR program (tesseract) and it didnt' do that well.
The original image files are old news papers, basically, and have some background noises, which I am sure tesseract has problem with. So I am trying to use some image preprocessing before feeding it into tesseract. Is there any suggestion for open source image preprocessing engine that fits well to this situation??? And instructions on how to use it would be even more appreciated !
I never heard of an "image preprocessing engine" for that purpose, but you can take a look at OpenCV (Open Source Computer Vision Library) and implement your own "pre-processing engine". OpenCV is a computer vision library that offers many features to perform image processing.
One interesting thing you might want test as a preprocessing step is apply a threshold to the image to remove noises and stuff. Anyway, I've talked about this kind of stuff in this thread.
Like #karlphillip mentioned, I highly doubt there's a readily available preprocessing engine for your purposes as the preprocessing technique vary greatly with the desired result.
Some common approaches to clearing up the text in noisy images include:
1. Adaptive thresholding (Sauvola or Niblack binarization)
2. Applying a median filter of a size slightly larger than the text to get a background image, then subtract out the background from the original image (to remove the larger noise like creases, stains, handwritten notes, etc.).
OpenCV has implementations of these filters/binarization methods. If you have access to published literature there's quite a bit of work on binarization of noisy documents.
Check out ScanTailor. It has pretty impressive pre-processing functionality and it is open source.
I'm thinking of starting a project for school where I'll use genetic algorithms to optimize digital sharpening of images. I've been playing around with unsharp masking (USM) techniques in Photoshop. Basically, I want to create a software that optimizes the parameters (i.e. blur radius, types of blur, blending the image) to create the "best-fit" set of filters.
I'm sort of quickly planning this project before starting it, and I can't think of a good fitness function for the 'selection' part. How would I determine the 'quality' of the filter sets, or measure how sharp the image is?
Also, I will be programming using python (with the Python Imaging Library) since it's the only language I'm proficient with. Should I learn a low-level language instead?
Any advice/tips on anything is greatly appreciated. Thanks in advance!
tl;dr How do I measure how 'sharp' an image is?
if its for tuning parameters you could take a known image and apply a known blurring/low pass filter. Then sharpen this with your GA+USM algorithm. Calculate your fitness function making use of the original image, e.g maybe something as simple as the mean absolute error. May need to create different datasets, e.g. landscape images (mostly sharp, in focus with large depth of field), portrait images (could be large areas deliberately out of focus and "soft"), along with low noise and noisy images. Sharpening noisy images is actually quite a challenge.
It would definitely be worth taking a look at Bruce Frasier' work on sharpening techniques for Photoshop etc.
Also it might worth checking out Imatest (www.imatest.com) to see if there is anything regarding sharpness/resolution. And finally you might also consider resolution charts.
And finally I seroiusly doubt one set of ideal parameters exists for USM, the optimum parameters will be image dependant and indeed be a personal perference (thatwhy I suggest starting for a known sharp image and blurring it). Understanding the type of image is probably as important and in itself and very interesting and challenging problem. Although perhaps basic hueristics like image varinance and edge histogram would reveal suitable clues.
Anyway just a thought, hopefully some of the above is useful