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.
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I am working on a program that gets exact pixel values of the shoreline in a given image. What is the best way to preprocess these types of images in order to make my life easier?
A sample image:
I suppose that you want to be able to segment the land from the water this way defining a path for the shoreline.
For this task I recommend you using an edge detection algorithm. A simple vertical Sobel filter should be enough given the image that you have provided. More details about its insides and API call here.
Do you have images with different meteorological conditions? Your algorithm should be robust when it comes to different lighting scenarios: night, rain etc (if that is the case).
A thresholding with respect to the tones that you have in your image might also help, details here.
For a proper binarized image the following contour finding methods proposed by OpenCV should do the job for you.
As a part of our project we have to find the dimensions of a given object in a particular image ex- dimensions of a given sunken ship which is underwater. This is totally new to me so my friend told me that in matlab its possible. Kindly help me out
I think you should look into image blobs and Edge detection. That's where I would start
Are you already set on MatLab? If you can use C#, I would look into: AForge.NET Image processing library for C#:
http://www.aforgenet.com/projects/iplab/
I have used AForge before to identify "blobs" in images and perform other image processing operations.
If you have still not finalized on Matlab, then AForge.NET or Magick.NET from ImageMagick can be tried.
To identify the dimensions of the image, we have to think thru the manual process of identifying the same. How are we able to identify ship in water from an image? How is the object different from sorrounding area in the image?
From that, you may try to identify ship as a blob and work on the blob. Sometimes, you may not be able to identify ship as blob, probably due to noise of the sorrounding. Find means to remove that noise or differentiate the object further from sorrounding by errosion or dillation or combination.
I'm currently working on my thesis on the neural networks. I'm using the CIFAR10 as a reference dataset. Now I would like to show some example results in my paper. The problem is, that the images in the dataset are 32x32 pixels so it's really hard to recognize something on them when printed on paper.
Is there any way to get hold of the original images with higher resolution?
UPDATE: I'm not asking for image processing algorithm, but for the original images presented in CIFAR-10. I need some higher resolution samples to put in my paper.
I now have the same problem and I just found your question.
It seems that CIFAR was built from labeling the tinyimages dataset, and are kind enough to share the indexing from CIFAR to tinyimages. Now tinyimages contain metadata file with URL of the original images and a toolbox for getting for any image you wish (e.g. those included in the CIFAR index).
So one may write a mat file which does this and share the results...
They're just small:
The CIFAR-10 and CIFAR-100 are labeled subsets of the 80 million tiny images dataset.
You could use Google reverse image search if you're curious.
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
I am working on a project which gives plots real time traffic status on Google Maps, & make it available to user on an Android phone and web browser.
http://www.youtube.com/watch?v=tcAyMngkzjk
I need to compare 2 images in openCV in order to determine traffic density. Can you please guide me how to compare the images? Should I go for histogram comparison or simple image subtraction?
One common solution is using background subtraction to track moving objects (cars) and then export an image with the moving objects remarked, so you can easily extract the objects from the image. If this is not the case, you will have to detect the vehicles and that's more challenging task because as carlosdc says there are many approaches depending on the angle of the camera, the size of vehicles, light conditions, cluttered backgrounds, etc.
If you specify a little more the problem ...
It really depends, and it would be impossible to determine without looking at your images.
Also, let me point out that it may be quite difficult to make this work adequately in all conditions: day/night, ray/shine, etc. Perhaps you should start by looking at what others have done and how good/bad it works. One such example would be this
try to read this two tutorials about OpenCV versus detect/recognition and find contur.
http://python-catalin.blogspot.ro/search/label/OpenCV
or try to find the color change in your image ... ( for example find colors versus background street )