12 research outputs found
Estimating Depth from RGB and Sparse Sensing
We present a deep model that can accurately produce dense depth maps given an
RGB image with known depth at a very sparse set of pixels. The model works
simultaneously for both indoor/outdoor scenes and produces state-of-the-art
dense depth maps at nearly real-time speeds on both the NYUv2 and KITTI
datasets. We surpass the state-of-the-art for monocular depth estimation even
with depth values for only 1 out of every ~10000 image pixels, and we
outperform other sparse-to-dense depth methods at all sparsity levels. With
depth values for 1/256 of the image pixels, we achieve a mean absolute error of
less than 1% of actual depth on indoor scenes, comparable to the performance of
consumer-grade depth sensor hardware. Our experiments demonstrate that it would
indeed be possible to efficiently transform sparse depth measurements obtained
using e.g. lower-power depth sensors or SLAM systems into high-quality dense
depth maps.Comment: European Conference on Computer Vision (ECCV) 2018. Updated to
camera-ready version with additional experiment
Natural Image Coding in V1: How Much Use is Orientation Selectivity?
Orientation selectivity is the most striking feature of simple cell coding in
V1 which has been shown to emerge from the reduction of higher-order
correlations in natural images in a large variety of statistical image models.
The most parsimonious one among these models is linear Independent Component
Analysis (ICA), whereas second-order decorrelation transformations such as
Principal Component Analysis (PCA) do not yield oriented filters. Because of
this finding it has been suggested that the emergence of orientation
selectivity may be explained by higher-order redundancy reduction. In order to
assess the tenability of this hypothesis, it is an important empirical question
how much more redundancies can be removed with ICA in comparison to PCA, or
other second-order decorrelation methods. This question has not yet been
settled, as over the last ten years contradicting results have been reported
ranging from less than five to more than hundred percent extra gain for ICA.
Here, we aim at resolving this conflict by presenting a very careful and
comprehensive analysis using three evaluation criteria related to redundancy
reduction: In addition to the multi-information and the average log-loss we
compute, for the first time, complete rate-distortion curves for ICA in
comparison with PCA. Without exception, we find that the advantage of the ICA
filters is surprisingly small. Furthermore, we show that a simple spherically
symmetric distribution with only two parameters can fit the data even better
than the probabilistic model underlying ICA. Since spherically symmetric models
are agnostic with respect to the specific filter shapes, we conlude that
orientation selectivity is unlikely to play a critical role for redundancy
reduction
“De-novo” amino acid sequence elucidation of protein G′e by combined “Top-Down” and “Bottom-Up” mass spectrometry
Inter-domain dynamics in the chaperone SurA and multi-site binding to its outer membrane protein clients
Investigating the role of mitochondria in type 2 diabetes lessons from lipidomics and proteomics studies of skeletal muscle and liver.
Mitochondrial dysfunction is discussed as a key player in the pathogenesis of type 2 diabetes mellitus (T2Dm), a highly prevalent disease rapidly developing as one of the greatest global health challenges of this century. Data however about the involvement of mitochondria, central hubs in bioenergetic processes, in the disease development are still controversial. Lipid and protein homeostasis are under intense discussion to be crucial for proper mitochondrial function. Consequently proteomics and lipidomics analyses might help to understand how molecular changes in mitochondria translate to alterations in energy transduction as observed in the healthy and metabolic diseases such as T2Dm and other related disorders. Mitochondrial lipids integrated in a tool covering proteomic and functional analyses were up to now rarely investigated, although mitochondria]. lipids might provide a possible lynchpin in the understanding of type 2 diabetes development and thereby prevention. In this chapter state-of-the-art analytical strategies, pre -analytical aspects, potential pitfalls as well as current proteomics and lipidomics-based knowledge about the pathophysiological role of mitochondria in the pathogenesis of type 2 diabetes will be discussed