From Box Filtering to Fast Explicit Diffusion

Abstract. There are two popular ways to implement anisotropic diffusion filters with a diffusion tensor: Explicit finite difference schemes are simple but become inefficient due to severe time step size restrictions, while semi-implicit schemes are more efficient but require to solve large linear systems of equations. In our paper we present a novel class of algorithms that combine the advantages of both worlds: They are based on simple explicit schemes, while being more efficient than semi-implicit approaches. These so-called fast explicit diffusion (FED) schemes perform cycles of explicit schemes with varying time step sizes that may violate the stability restriction in up to 50 percent of all cases. FED schemes can be motivated from a decomposition of box filters in terms of explicit schemes for linear diffusion problems. Experiments demonstrate the advantages of the FED approach for time-dependent (parabolic) image enhancement problems as well as for steady state (elliptic) image compression tasks. In the latter case FED schemes are speeded up substantially by embedding them in a cascadic coarse-to-fine approach.

Anisotropic range image integration

Abstract. Obtaining high-quality 3D models of real world objects is an important task in computer vision. A very promising approach to achieve this is given by variational range image integration methods: They are able to deal with a substantial amount of noise and outliers, while regularising and thus creating smooth surfaces at the same time. Our paper extends the state-of-the-art approach of Zach et al. (2007) in several ways: (i) We replace the isotropic space-variant smoothing behaviour by an anisotropic (direction-dependent) one. Due to the directional adaptation, a better control of the smoothing with respect to the local structure of the signed distance field can be achieved. (ii) In order to keep data and smoothness term in balance, a normalisation factor is introduced. As a result, oversmoothing of locations that are seen seldom is prevented. This allows high quality reconstructions in uncontrolled capture setups, where the camera positions are unevenly distributed around an object. (iii) Finally, we use the more accurate closest signed distances instead of directional signed distances when converting range images into 3D signed distance fields. Experiments demonstrate that each of our three contributions leads to clearly visible improvements in the reconstruction quality.

Oncolytic H-1 parvovirus binds to sialic acid on laminins for cell attachment and entry

H-1 parvovirus (H-1PV) is a promising anticancer therapy. However, in-depth understanding of its life cycle, including the host cell factors needed for infectivity and oncolysis, is lacking. This understanding may guide the rational design of combination strategies, aid development of more effective viruses, and help identify biomarkers of susceptibility to H-1PV treatment. To identify the host cell factors involved, we carry out siRNA library screening using a druggable genome library. We identify one crucial modulator of H-1PV infection: laminin γ1 (LAMC1). Using loss- and gain-of-function studies, competition experiments, and ELISA, we validate LAMC1 and laminin family members as being essential to H-1PV cell attachment and entry. H-1PV binding to laminins is dependent on their sialic acid moieties and is inhibited by heparin. We show that laminins are differentially expressed in various tumour entities, including glioblastoma. We confirm the expression pattern of laminin γ1 in glioblastoma biopsies by immunohistochemistry. We also provide evidence of a direct correlation between LAMC1 expression levels and H-1PV oncolytic activity in 59 cancer cell lines and in 3D organotypic spheroid cultures with different sensitivities to H-1PV infection. These results support the idea that tumours with elevated levels of γ1 containing laminins are more susceptible to H-1PV-based therapies