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

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