Could anybody suggest an automatic way to convert from a list of images (without Kinect) to a point cloud in opencv?
Take a look at OpenCV Contrib Structure From Motion module (SFM). There are two nice examples trajectory_reconstruction.cpp and scene_reconstruction.cpp.
Also, there is alternative called Multi-View Environment which you could find on GitHub at simonfuhrmann/mve and which might meet your criteria too.
Related
I have a sequence of DICOM images constituting a single scan. I would like to build a CGAL mesh representing 3D volume segmented out of that scan by thresholding. I prefer Windows and few, easy to build dependencies, if any.
I've heard that ITK can be used for this purpose, but it is a large library with a lot of overlap with CGAL. Are there any other options?
The example CGAL-4.9/examples/Mesh_3/mesh_3D_gray_vtk_image.cpp should be a good starting point. As this is not easy to find we will add a link to it in the CGAL User manual, see the pull request on github
I'm wondering if it is possible to use the OpenCV framework to recognise a building?
For example, if I store an image of a building, is it possible to use OpenCV to detect this building through the iPhone camera?
Thanks!
Detecting known objects such as your building in an image can be done using the features2d module in OpenCV.
It works by detecting key points in the known image and computing a set of descriptors for these that can be compared to the key points and descriptors computed from the unknown scene image by a process known as matching.
The find_obj.py demo in the samples/python2 folder of OpenCV shows how to detect a known object in an image.
There is also a tutorial in the user guide, see http://docs.opencv.org/doc/user_guide/ug_features2d.html
Note that some of the algorithms often used (e.g. SURF and SIFT) are not free, and need to be licensed separately if you use them.
Is possible, but, you have a long road to go.
One way to do this: use visual keypoints to recognise objects.
OpenCV Sift Documentation
I am involved in a project regarding image processing where I need to extract features of a given image. I am supposed to do that using wavelets and curvelets. But I cannot find any source where I can fully understand them. I have downloaded several journals and publications but couldn't figure out exactly how features are extracted using them.
Can someone explain how its done. Any tutorial that easily explains them is also welcome.
Thanks in advance.
If you are interested in image processing, you musst know the existance of the library OpenCV. This is the most usefull library for image processing.
In This library there is an implementation of Haar Wavelet transform, maybe that could interest you.
For all this kine of algorithms there is another powerfull source of data. That is Matworks File Exchange. This web page is a matlab open source platform. If you don't use matlab, you can see source codes provided on this web site to understand how does wavelet and curvelet works.
For example, this project may interest you :
http://www.mathworks.com/matlabcentral/fileexchange/33146-feature-extraction-using-multisignal-wavelet-packet-decomposition
I´m a beginner on computer vision, but I know how to use some functions on opencv. I´m tryng to use Opencv for Document Recognition, I want a help to find the steps for it.
I´m thinking to use opencv example find_obj.cpp , but the documents, for example passport, has some variables, name, birthdate, pictures. So, I need a help to define the steps for it, and if is possible how function I have to use on the steps.
I'm not asking a whole code, but if anyone has any example link or you can just type a walkthrough, it is of great help.
There are two very different steps involved here. One is detecting your object, and the other is analyzing it.
For object detection, you're just trying to figure out whether the object is in the frame, and approximately where it's located. The OpenCv features framework is great for this. For some tutorials and comprehensive sample code, see the OpenCv features2d tutorials and especially the feature matching tutorial.
For analysis, you need to dig into optical character recognition (OCR). OpenCv does not include OCR libraries, but I recommend checking out tesseract-ocr, which is a great OCR library. If your documents have a fixed structured (consistent layout of text fields) then tesseract-ocr is all you need. For more advanced analysis checking out ocropus, which uses tesseract-ocr but adds layout analysis.
I need to process DICOM formatted medical images and visualize them in 3D, also do some image processing on these images on real-time. Therefore, I am asking this question to learn which SDK has better real-time characteristics for medical visualization and image processing?
The Visualization Toolkit (VTK) is an open-source, freely available software system for 3D computer graphics, image processing and visualization.
You can find details here.
Or another solution would be the modifying or utilizing 3D engine that supports volume rendering.
Moreover, for computer vision algorithms, OpenCV seems promising.
osgVolume is an add-in to the popular openscenegraph library for doing this
Just use GDCM+VTK. In 2D simply use gdcmviewer. In 3D you need to build gdcmorthoplanes.
Ref:
http://sourceforge.net/apps/mediawiki/gdcm/index.php?title=Gdcmviewer
http://sourceforge.net/apps/mediawiki/gdcm/index.php?title=Using_GDCM_API
You could check out MITK (http://mitk.org) which combines the already mentioned VTK with the Insight Toolkit (http://www.itk.org) for image processing. Another option to start from could be Slicer (http://www.slicer.org), but this depends on the license you need.
In a uni we were taught Matlab for DICOM file processing. I think it has pretty nice and easy to use plugins for that as well. The end results were that using Matlab I was able to do all kinds of DICOM image processing, filtering and so forth.
As you probably know, Matlab is not SDK but a complete environment. Nevertheless you can write scripts to achieve normal application behavior: Create windows, buttons, images, etc.