3,451 research outputs found
Cell Detection by Functional Inverse Diffusion and Non-negative Group SparsityPart II: Proximal Optimization and Performance Evaluation
In this two-part paper, we present a novel framework and methodology to
analyze data from certain image-based biochemical assays, e.g., ELISPOT and
Fluorospot assays. In this second part, we focus on our algorithmic
contributions. We provide an algorithm for functional inverse diffusion that
solves the variational problem we posed in Part I. As part of the derivation of
this algorithm, we present the proximal operator for the non-negative
group-sparsity regularizer, which is a novel result that is of interest in
itself, also in comparison to previous results on the proximal operator of a
sum of functions. We then present a discretized approximated implementation of
our algorithm and evaluate it both in terms of operational cell-detection
metrics and in terms of distributional optimal-transport metrics.Comment: published, 16 page
NICP: Dense normal based point cloud registration
In this paper we present a novel on-line method to recursively align point clouds. By considering each point together with the local features of the surface (normal and curvature), our method takes advantage of the 3D structure around the points for the determination of the data association between two clouds. The algorithm relies on a least squares formulation of the alignment problem, that minimizes an error metric depending on these surface characteristics. We named the approach Normal Iterative Closest Point (NICP in short). Extensive experiments on publicly available benchmark data show that NICP outperforms other state-of-the-art approaches
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