An efficient descriptor and a complete scheme for removing incorrect correspondences are two essentials for accurate feature points matching in image processing and pattern recognition. This paper proposes a new rigid feature points matching algorithm. First, a new feature descriptor based on the local sharpness distribution is proposed to extract features, which leads to a feature set of extre...

BACKGROUND Many classification methods have been proposed based on magnetic resonance images. Most methods rely on measures such as volume, the cerebral cortical thickness and grey matter density. These measures are susceptible to the performance of registration and limited in representation of anatomical structure. This paper proposes a two-stage local feature fusion method, in which deformabl...

We present a feature extraction method for RGB-D data based on k-means clustering that builds on recent work by Coates et al. Using unsupervised learning methods we are able to automatically learn feature responses that combine all available information (color and depth) into one, concise representation. We show that depth information can substantially increase the recognition performance and t...

In this paper we apply boosting to learn complex non-linear local visual feature representations, drawing inspiration from its successful application to visual object detection. The main goal of local feature descriptors is to distinctively represent a salient image region while remaining invariant to viewpoint and illumination changes. This representation can be improved using machine learning...