I want to know if there is any implementation of GMM (Gaussian Mixture Model) for PCL library?
Actually I have searched but I couldn't find any library or source code!
The main idea is using this GMM technique to detect moving object's point cloud acquired by Kinect sensor in the scene.
Try using segmentation module, concretely GrabCut algorithm, which can be useful for background/foreground estimation and uses GMM for that purpose.
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I am working on a project that requires real-time conversion of images and videos to a 3d model using deep learning . Although I have found ways to get a voxel model, I feel polygon meshes give finer models. Is there any way I can do this using python's libraries . I would love to know about any previous works on this topic.
I'm trying to make a OpenCV program in Python 3 to detect the faces of my friends. I've seen that one can train a Cascade Classifier using OpenCV to specify a certain type of object. However, it isn't clear whether that could create a classifier refined enough to pick only my friends' faces out of a large sample size, or whether this is something I could achieve without making my own Cascade Classifier. Can anyone help?
Cascade classifiers usually are built for face detection. You are trying to solve a different problem, face recognition.
Deep learning is a common framework nowdays, but other models do exist. http://www.face-rec.org/algorithms/ makes a very good job at presenting the main algorithms.
This presents an interesting implementation in OpenCV.
I study SVM and I will implement svm using python sklearn.svm.SVC.
As i know SVM problem can be represented a QP(Quadratic Programming)
So here i was wondering which QP solver is used to solve the SVM QP problem in sklearn svm.
I think it may be SMO or coordinate descent algorithm.
Please let me know what the exact algorithm is used in sklearn svm
Off-the-shelf QP-solvers have been used in the past, but for many years now dedicated code is used (much faster and more robust). Those solvers are not (general) QP-solvers anymore and are just build for this one use-case.
sklearn's SVC is a wrapper for libsvm (proof).
As the link says:
Since version 2.8, it implements an SMO-type algorithm proposed in this paper:
R.-E. Fan, P.-H. Chen, and C.-J. Lin. Working set selection using second order information for training SVM. Journal of Machine Learning Research 6, 1889-1918, 2005.
(link to paper)
I have a new decision tree ensemble regression method algorithm I need to implement, and I would
like to build on the infrastructure that the Python-based scikit-learn package provides if I can. I want to create a new type of base decision tree and a new way of combining them to make ensemble regression predictions.
The documentation for scikit-learn is focused on usage, not extension. I have looked at the code for the existing ensemble methods, but there is some fairly subtle Python-fu being used, which makes it hard to know where to start. Does anyone know of documentation that explains how to extend scikit with custom classes? Or is there an example of a simple extension of this kind? (It could be on any module; it doesn't have to be ensembles.)
Thanks.
I'm working on face detection algorithm which extracts Haar-like features and then classifies the face and non faces using SVM. I'll be implementing whole algorithm including SVM in C language because i have to run the code on Stretch SCP board.
I have lot of doubts regarding which kernel is most suitable for face-detection problem; is it linear, RBF or something else?
I already extracted haar-features and tried to classify using libsvm and liblinear but didn't get appropriate results.
Please suggest which kernel to be used and what parameter to be considered ?