My input will be the gear detect file. I am trying figure out what kind of a binary image
processing pipeline I should set up in order to detect the defects of these gears. An
output example is also provided (Ouput of result). How to detect gear with using image processing techniques. I am studying image processing (opening,dilation,closing,erosion,addition, subtraction etc. which linear operations.) Thank for help.
Related
I'm trying to learn image processing for a new job--e.g. sharpening, noise reduction, edge detection, white balancing, color reconstruction, scaling--and all I have for my current work MatLab is the basic 2017b package with no additional toolboxes. I'm perfectly willing to pay a few hundred dollars for home use of MatLab plus some toolboxes and the appropriate Simulink package. Simulink seems to be necessary for several of the tasks I mentioned such as sharpening and edge detection. Can someone who does image processing please list what packages I should purchase? I'd be very grateful! I'm starting a Udemy course on image processing but it only does some very basic image manipulation.
Trying to use Matlab 2017b. Doesn't have the necessary functionality do do advanced image processing.
I have set of images in which I want to separate graphs from others. I am looking at OpenCV but since I don't have any experience in image processing I don't know what technique or set of techniques should be applied in order to get this done.
I need to know the techniques of image processing for this task.
Sample Graphs are follows:
Other images can be of any type.. for e.g.
Thanks,
Jawad.
I am thinking of using OpenCV library for image analysis. Basically I want to automate in my project the extraction of image label from wine bottle.
This is the sample input image:
This is the sample output:
I am thinking what should be my general strategy to extract the image. I am not asking for direct code. Just want to know the general approach to solve the problem.
Thanks!
Sorry for vage answer but in applied computer vision is no such thing like general approach.
some will disagree of course but in reality
all CV applications are custom made for some specific purpose/task
in your case is the idea to find cylindric and probably standing object (bottle)
and then finding of irregular parts in it
I would do it like this:
1.remove noise as much as possible (smooth/sharpen filters)
2.(optionaly) reduce image data (via (i)FT or (i)DCT for example)
3.segmentate objects (usually by homogenity of color or by edge detection or by booth)
4.identify bottle object (by color,shape,or illumination (glass is transparent))
5.identify objects inside bottle (homogenity,not transparent,usually sharp edges,color is not good some labels are black on dark glass)
6.(optional) project label back from cylindric space to flat texture
[notes]
create app with many scrollbars and checkboxes
to be able to change all tresholds and enable disable filters or their order on the run
all parts will take a lot of tweaking of tresholds and weights
you have to do a lot of trial and error runs to find the best filters and their config for your task
Say i have this old manuscript ..What am trying to do is making the manuscript such that all the characters present in it can be perfectly recognized what are the things i should keep in mind ?
While approaching such a problem any methods for the same?
Please help thank you
Some graphics applications have macro recorders (e.g. Paint Shop Pro). They can record a sequence of operations applied to an image and store them as macro script. You can then run the macro in a batch process, in order to process all the images contained in a folder automatically. This might be a better option, than re-inventing the wheel.
I would start by playing around with the different functions manually, in order to see what they do to your image. There are an awful number of things you can try: Sharpening, smoothing and remove noise with a lot of different methods and options. You can work on the contrast in many different ways (stretch, gamma correction, expand, and so on).
In addition, if your image has a yellowish background, then working on the red or green channel alone would probably lead to better results, because then the blue channel has a bad contrast.
Do you mean that you want to make it easier for people to read the characters, or are you trying to improve image quality so that optical character recognition (OCR) software can read them?
I'd recommend that you select a specific goal for readability. For example, you might want readers to be able to read the text 20% faster if the image has been processed. If you're using OCR software to read the text, set a read rate you'd like to achieve. Having a concrete goal makes it easier to keep track of your progress.
The image processing book Digital Image Processing by Gonzalez and Woods (3rd edition) has a nice example showing how to convert an image like this to a black-on-white representation. Once you have black text on a white background, you can perform a few additional image processing steps to "clean up" the image and make it a little more readable.
Sample steps:
Convert the image to black and white (grayscale)
Apply a moving average threshold to the image. If the characters are usually about the same size in an image, then you shouldn't have much trouble selecting values for the two parameters of the moving average threshold algorithm.
Once the image has been converted to just black characters on a white background, try simple operations such as morphological "close" to fill in small gaps.
Present the original image and the cleaned image to adult readers, and time how long it takes for them to read each sample. This will give you some indication of the improvement in image quality.
A technique call Stroke Width Transform has been discussed on SO previously. It can be used to extract character strokes from even very complex backgrounds. The SWT would be harder to implement, but could work for quite a wide variety of images:
Stroke Width Transform (SWT) implementation (Java, C#...)
The texture in the paper could present a problem for many algorithms. However, there are technique for denoising images based on the Fast Fourier Transform (FFT), an algorithm that you can use to find 1D or 2D sinusoidal patterns in an image (e.g. grid patterns). About halfway down the following page you can see examples of FFT-based techniques for removing periodic noise:
http://www.fmwconcepts.com/misc_tests/FFT_tests/index.html
If you find a technique that works for the images you're testing, I'm sure a number of people would be interested to see the unprocessed and processed images.
I'm about to start a project that will record and edit audio files, and I'm looking for a good library (preferably Ruby, but will consider anything other than Java or .NET) for on-the-fly visualization of waveforms.
Does anybody know where I should start my search?
That's a lot of data to be streaming into a browser. Flash or Flex charts is probably the only solution that will be memory efficient. Javascript charting tends to break-down for large data sets.
When displaying an audio waveform, you will want to do some sort of data reduction on the original data, because there is usually more data available in an audio file than pixels on the screen. Most audio editors build a separate file (called a peak file or overview file) which stores a subset of the audio data (usually the peaks and valleys of a waveform) for use at different zoom levels. Then as you zoom in past a certain point you start referencing the raw audio data itself.
Here are some good articles on this:
Waveform Display
Build an Audio Waveform Display
As far as source code goes, I would recommend looking through the Audacity source code. Audacity's waveform display is pretty good and mostly likely does a similar sort of data reduction when rendering the waveforms.
i wrote one:
http://github.com/pangdudu/rude/tree/master/lib/waveform_narray_testing.rb
,nick
The other option is generating the waveforms on the server-side with GD or RMagick. But good luck getting RubyGD to compile.
Processing is often used for visualization, and it has a Ruby port:
https://github.com/jashkenas/ruby-processing/wiki