Kernel methods have gained a great deal of popularity in the machine learning community as a method to learn indirectly in highdimensional feature spaces. Those interested in rela...
Searching approximate nearest neighbors in large scale high dimensional data set has been a challenging problem. This paper presents a novel and fast algorithm for learning binary...
— In this paper we present a new approach for labeling 3D points with different geometric surface primitives using a novel feature descriptor – the Fast Point Feature Histogram...
Radu Bogdan Rusu, Andreas Holzbach, Nico Blodow, M...
PCA-SIFT is an extension to SIFT which aims to reduce SIFT’s high dimensionality (128 dimensions) by applying PCA to the gradient image patches. However PCA is not a discriminati...
Noisy or distorted video/audio training sets represent constant challenges in automated identification and verification tasks. We propose the method of Mutual Interdependence An...