Im new in the Computer Vision field, so I'm learning from scratch how to generate point clouds from multiple image captures. I'm not implementing any of this on code yet, first I want to learn how this whole process should be done and then I'll code it.
So far I've learned about features detection algorithms , mostly SIFT and the remarkably more accurate A-KAZE, which detects much more features on each image and thus generates more dense clouds.
Then there comes the key matching algorithms, mainly Brute Force (BF) and FLANN.
Finally it should be a process in which you:
-first: get all the cameras orientation
-finally : generate the sparse point cloud.
But, until now , I've only found examples in OpenCV in which only two images are matched and their matched features are drawn. Im not able to find any example in which more images are matched and , more importantly, Im not able to find out how to find camera's orientation and to generate point clouds on OpenCV. Please, I need some help on those last stages. If you find any example of multiple image matching, point cloud generation it would be very helpfull . Thanks in advance!
OpenMvg has a nice structure-from-motion pipeline example to reconstruct 3D sparse point clouds from SIFT and AKAZE features. It even works without given any camera intrinsics (focal length, principal point).
Related
I'm developing an Android application to match two images using ORB feature detection .
The processing and matching logic is called in java using JNI functions.
The problem is that the feature detections works well for some images, but fails in some images and some cases.
Here is an example of images that fails in some unknown conditions
After some thoughts and discussions, I figured out that the problem is that the problem is the lack of features that's why the program fails. Someone in the opencv community tried this image and it gave him 60 keypoints which all of them doesn't survive the RobustMatcher tests.
So I need to enhance to features in this image in order to make the matching work.
In addition to equalizeHist, what can I do ?
I hope you can help me with some suggestions and maybe some examples guys.
One way is to enhance the edges of the image. Do a Laplacian filter for example and multiply the result to the original image. This job makes the features (edge) more salient. Of course before everything convert the image to a float type and at the end, normalize your image.
I would like to make an Android Application that captures an image and searches it for coins and paper notes and then determines the value of the money in the image.
Additionally, the output of the system will be such that it can be understood by a blind person.
What functions and techniques in openCV would suit these tasks?
What limitations and development hurdles can I expect?
Assuming you already know how to program android apps,you need to do the following:-
Download the OpenCV SDK and set it up with the IDE.
Recognising shapes will be a huge part of your project, see the contour detection example that is present with the SDK examples. Your primary goal will be detecting a circle. Later you need to adapt your algorithm according to the currency. This will be of particular interest to you.
Learn the different image processing techniques like thresholding for better accurate results. Understanding what a Mat object is and how it can be manipulated is important.
Finally improve the accuracy of your algorithm, as sometimes lighting conditions make the difference between a good review and a dissatisfied user.
I am trying to create an android app which can recognize Billiard balls on a pool table in an image coming from the camera. What would be the best approach to do this?
We can assume that the camera and the pool table are in fixed positions, but there could be object other than the balls on the pool table.
I am currently looking into two possible solutions:
Vuforia SDK - Simple API for object tracking / recognition, but I couldn't find any information about ball/sphere shape tracking. They have Cylinder and Image target that could possibly be used somehow to track the balls.
OpenCV - Seems much richer and steeper learning curve in comparison with Vuforia, but there is some information about Billiard ball detection online (e.g. this, and this).
Are there any addition approaches for solving this problem? What would be the easiest working approach for this?
Thanks!
The balls are moving or not?
I've used SURF (and SFIT) they work great for arrested objects. Have a look to the documentation page there are also two questions you should see this and this. Than if you want to calculate the trajectory I've tried Pymecavideo that uses OpenCv maybe a look into the source code could be interesting for your work.
We're currently working on an android ocr app using opencv.pre-processing ,segmentation ,Feature extraction steps are done. Classification is the remaining step and we're stuck ..We're using a DB table which is filled with each letter features ..Firstly we had only 1 feature per letter and we used euclidean distance ,but results wasn't accurate and more features needed to be obtained and so we did.The problem now is we have 7 features per letter and absolutely no idea of how to classify i/p based on them..some have recommended using knn ,but we can't figure out how and the opencv documentation in that part ain't clear ..so if anybody can help it wud be great.
Thanks in advance
Briefly and without discussing the details. Vector space comes in handy here. You need to build a feature vector
<feature1, feature2, feature3.. featureN> for each of the instances in your training set.
From each of these images you extract features that you think or you read in the research articles are important for image classification. For example you can do centroid, Gaussian blur, histograms, etc.
Once you have these values linear algebra comes into play with some classification algorithm: knn, svm, naive bayes etc that you run on your training set, that is you build your model.
If the model is ready you run it on your test set.
Use cross validation for more comprehensive results.
For more details check the course notes:
http://www.inf.ed.ac.uk/teaching/courses/iaml/slides/knn-2x2.pdf
or
http://www.inf.ed.ac.uk/teaching/courses/inf2b/lectureSchedule.html
would like to add that OpenCV may not have the sort of classifiers you might prefer.
There are several libraries out there, though you may have to see which works best when on a mobile platform. Could you give some details on the features you are using?
The simplest KNN (k-nearest neighbors) measure would be to find the Euclidean distance in n dimensions (for an n-dimensional feature vector) between the input sample's features and each of the vectors in your DB table. Also explore Mahalanobis distance (used to measure distance between a point and a dataset/class) if you have multiple classes and the input image is to be classified as one such 'type' or 'class' of image.
As #matcheek mentioned, more sophistication can be possible using machine learning techniques such as SVM, Neural Nets, etc. However first you might consider a simpler thing like kNN, considering its a mobile platform which may limit the computational complexity.
I want to develop app which will recognize object(like monument or something) present in front of camera using OpenCv and then it will show information about it.
So the question is how to recognize object(like monument or something) shape or compare to images with OpenCV?
And what is the best method for doing this?
It would be good if there was some kind of samples or tutorials for object detection and comparison.
Thank you.
The best method for what you ask is using local features detectors like OpenCV's SIFT, SURF and ORB, for example.
You need at least one picture from the object you want to detect. Afterwards, those algorithms can compare that image with other images to see if they are similar enough.
Here is the Documentation for the algorithms.
ORB and others:
http://docs.opencv.org/modules/features2d/doc/feature_detection_and_description.html
SURF and SIFT ('nonfree'):
http://docs.opencv.org/modules/nonfree/doc/feature_detection.html
The way these algorithms work for that task is by selecting interesting points for each image, and compare them to see if they match. If several matches are found, it is most likely the images have the same object.
Tutorials (from Feature Detection and below):
http://docs.opencv.org/doc/tutorials/features2d/table_of_content_features2d/table_of_content_features2d.html
You can also find C++ samples related to this topic here (samples are also within OpenCV download package):
eg. "matching_to_many_images.cpp"
"video_homography.cpp"
http://code.opencv.org/projects/opencv/repository/revisions/master/show/samples/cpp
And Android Java samples here (unrelated but also helpful):
http://code.opencv.org/projects/opencv/repository/revisions/master/show/samples/android
Or Python samples which are actually the more updated ones for this topic (at the time this post was written):
http://code.opencv.org/projects/opencv/repository/revisions/master/show/samples/python2
As a final note, like #BDFun said in the comment, this is not trivial to do.
More - if you want an overview of OpenCV Feature detection and description, check this post.