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Point cloud completion tool based on dictionary learning. Takes a PCL point cloud surface and fills in gaps or densifies sparse regions by learning from the various surface features of the cloud. This is done using a variation of the k-SVD dictionary learning algorithm that allows for continuous atoms and dealing with unstructured point cloud data. Written in Qt C++, using Eigen, OpenMP, and OpenGL.
DL2S: Dictionary learning level set for ultrasound image segmentation
SDL: salinecy based dictionary learning
Image denoising with sparse coding through K-SVD
A parallelized C++ implementation of discrete K-SVD and 2D continuous K-SVD for unstructured variable length signals,
Learn an Sparse dictionary of 8 x 8 patches using an algorithm similar to K-SVD
Unofficial implementation of SVMs multi-class loss feedback based discriminative dictionary learning in python