Here we model the effect of non-overlapping voxels on image registration, and show that a major defect of overlap-only models--their limited capture range--can be alleviated. Theor...
In this paper, a novel spatial feature, namely maximum distance-gradient-magnitude (MDGM), is defined for registration tasks. For each voxel in an image, the MDGM feature encodes s...
Abstract. We propose a novel method, fMRI-Informed Regional Estimation (FIRE), which utilizes information from fMRI in E/MEG source reconstruction. FIRE takes advantage of the spat...
In this paper, we introduce a generic framework for semi-supervised kernel learning. Given pairwise (dis-)similarity constraints, we learn a kernel matrix over the data that respe...
This paper describes an unsupervised learning technique for modeling human locomotion styles, such as distinct related activities (e.g. running and striding) or variations of the ...