ENHANCEMENT OF DEPTH MAP BY FUSION USING ADAPTIVE AND SEMANTIC-GUIDED SPATIOTEMPORAL FILTERING

Extracting detailed geometric information about a scene relies on the quality of the depth maps (e.g. Digital Elevation Surfaces, DSM) to enhance the performance of 3D model reconstruction. Elevation information from LiDAR is often expensive and hard to obtain. The most common approach to generate depth maps is through multi-view stereo (MVS) methods (e.g. dense stereo image matching). The quality of single depth maps, however, is often prone to noise, outliers, and missing data points due to the quality of the acquired image pairs. A reference multi-view image pair must be noise-free and clear to ensure high-quality depth maps. To avoid such a problem, current researches are headed toward fusing multiple depth maps to recover the shortcomings of single-depth maps resulted from a single pair of multi-view images. Several approaches tackled this problem by merging and fusing depth maps, using probabilistic and deterministic methods, but few discussed how these fused depth maps can be refined through adaptive spatiotemporal analysis algorithms (e.g. spatiotemporal filters). The motivation is to push towards preserving the high precision and detail level of depth maps while optimizing the performance, robustness, and efficiency of the algorithm.

Conditionally rare taxa contribute but do not account for prokaryotic community changes in soils

Conditionally rare taxa (CRT) are thought to greatly impact microbial community turnover across many environments, but little is known about their role in soils. Here, we investigate the contribution of CRT to whole community variation over space and time in a series of geographically distinct soils dedicated to three agricultural practices of differing intensities and sampled over a full seasonal cycle. We demonstrate that soil CRT do not account for observed total community changes, but that these rare taxa can be modified by spatiotemporal filters.

Synaptic pathways that shape the excitatory drive in an OFF retinal ganglion cell

Different types of retinal ganglion cells represent distinct spatiotemporal filters that respond selectively to specific features in the visual input. Much about the circuitry and synaptic mechanisms that underlie such specificity remains to be determined. This study examines how N-methyl-d-aspartate (NMDA) receptor signaling combines with other excitatory and inhibitory mechanisms to shape the output of small-field OFF brisk-sustained ganglion cells (OFF-BSGCs) in the rabbit retina. We used voltage clamp to separately resolve NMDA, α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA), and inhibitory inputs elicited by stimulation of the receptive field center. Three converging circuits were identified. First is a direct glutamatergic input, arising from OFF cone bipolar cells (CBCs), which is mediated by synaptic NMDA and AMPA receptors. The NMDA input was saturated at 10% contrast, whereas the AMPA input increased monotonically up to 60% contrast. We propose that NMDA inputs selectively enhance sensitivity to low contrasts. The OFF bipolar cells, mediating this direct excitatory input, express dendritic kainate (KA) receptors, which are resistant to the nonselective AMPA/KA receptor antagonist, 2,3-dioxo-6-nitro-1,2,3,4-tetrahydrobenzo[f]quinoxaline-7-sulfonamide disodium salt (NBQX), but are suppressed by a GluK1- and GluK3-selective antagonist, ( S)-1-(2-amino-2-carboxyethyl)-3-(2-carboxy-thiophene-3-yl-methyl)-5-methylpyrimidine-2,4-dione (UBP-310). The second circuit entails glycinergic crossover inhibition, arising from ON-CBCs and mediated by AII amacrine cells, which modulate glutamate release from the OFF-CBC terminals. The third circuit also comprises glycinergic crossover inhibition, which is driven by the ON pathway; however, this inhibition impinges directly on the OFF-BSGCs and is mediated by an unknown glycinergic amacrine cell that expresses AMPA but not KA receptors.

SCALE-DEPENDENCE OF SPATIOTEMPORAL FILTERS INSPIRED BY CELLULAR AUTOMATA

We discuss analysis tools of spatiotemporal patterns. These tools are based on nearest-neighbor considerations similar to cellular automata. Application of these methods to a spatiotemporal data set means selecting certain scales in space and in time. Focusing on spatial length we show that the dependence of the results on this scale can be used to quantify separately the contribution to the dynamics of measurement noise and of dynamical (internal) noise, respectively. In particular, we test the spatiotemporal filters using sample data generated with a network of coupled Sel'kov oscillators. Possible application of our results to biological systems are briefly discussed.