SuperPoint: Self-Supervised Interest Point Detection and Description

This paper presents a self-supervised framework for training interest point detectors and descriptors suitable for a large number of multiple-view geometry problems in computer vision. As opposed to patch-based neural networks, our fully-convolutional model operates on full-sized images and jointly computes pixel-level interest point locations and associated descriptors in one forward pass. We introduce Homographic Adaptation, a multi-scale, multi-homography approach for boosting interest point detection repeatability and performing cross-domain adaptation (e.g., synthetic-to-real). Our model, when trained on the MS-COCO generic image dataset using Homographic Adaptation, is able to repeatedly detect a much richer set of interest points than the initial pre-adapted deep model and any other traditional corner detector. The final system gives rise to state-of-the-art homography estimation results on HPatches when compared to LIFT, SIFT and ORB.Comment: Camera-ready version for CVPR 2018 Deep Learning for Visual SLAM Workshop (DL4VSLAM2018

A Gaussian Approximation of Feature Space for Fast Image Similarity

We introduce a fast technique for the robust computation of image similarity. It builds on a re-interpretation of the recent exemplar-based SVM approach, where a linear SVM is trained at a query point and distance is computed as the dot product with the normal to the separating hyperplane. Although exemplar-based SVM is slow because it requires a new training for each exemplar, the latter approach has shown robustness for image retrieval and object classification, yielding state-of- the-art performance on the PASCAL VOC 2007 detection task despite its simplicity. We re-interpret it by viewing the SVM between a single point and the set of negative examples as the computation of the tangent to the manifold of images at the query. We show that, in a high-dimensional space such as that of image features, all points tend to lie at the periphery and that they are usually separable from the rest of the set. We then use a simple Gaussian approximation to the set of all images in feature space, and fit it by computing the covariance matrix on a large training set. Given the covariance matrix, the computation of the tangent or normal at a point is straightforward and is a simple multiplication by the inverse covariance. This allows us to dramatically speed up image retrieval tasks, going from more than ten minutes to a single second. We further show that our approach is equivalent to feature-space whitening and has links to image saliency

Interleukin-1β Interferes with Epidermal Homeostasis through Induction of Insulin Resistance: Implications for Psoriasis Pathogenesis

Response pathways of the metabolic and the immune system have been evolutionary conserved, resulting in a high degree of integrated regulation. Insulin is a central player in the metabolic system and potentially also in the homeostasis of the skin. Psoriasis is a frequent and often severe autoimmune skin disease, clinically characterized by altered epidermal homeostasis, of which the molecular pathomechanisms are only little understood. In this study, we have examined a potential role for insulin signaling in the pathogenesis of this disease. We show that IL-1β is present in high quantities in tissue fluid collected via microdialysis from patients with psoriasis; these levels are reduced under successful anti-psoriatic therapy. Our results suggest that IL-1β contributes to the disease by dual effects. First, it induces insulin resistance through p38MAPK (mitogen-activated protein kinase), which blocks insulin-dependent differentiation of keratinocytes, and at the same time IL-1β drives proliferation of keratinocytes, both being hallmarks of psoriasis. Taken together, our findings point toward insulin resistance as a contributing mechanism to the development of psoriasis; this not only drives cardiovascular comorbidities, but also its cutaneous phenotype. Key cytokines inducing insulin resistance in keratinocytes and kinases mediating their effects may represent attractive targets for novel anti-psoriatic therapies

Object Detection Through Exploration With A Foveated Visual Field

We present a foveated object detector (FOD) as a biologically-inspired alternative to the sliding window (SW) approach which is the dominant method of search in computer vision object detection. Similar to the human visual system, the FOD has higher resolution at the fovea and lower resolution at the visual periphery. Consequently, more computational resources are allocated at the fovea and relatively fewer at the periphery. The FOD processes the entire scene, uses retino-specific object detection classifiers to guide eye movements, aligns its fovea with regions of interest in the input image and integrates observations across multiple fixations. Our approach combines modern object detectors from computer vision with a recent model of peripheral pooling regions found at the V1 layer of the human visual system. We assessed various eye movement strategies on the PASCAL VOC 2007 dataset and show that the FOD performs on par with the SW detector while bringing significant computational cost savings.Comment: An extended version of this manuscript was published in PLOS Computational Biology (October 2017) at https://doi.org/10.1371/journal.pcbi.100574

S1 Guideline onychomycosis

Onychomycosis is a fungal infection of the fingernails and toenails. In Europe, tinea unguium is mainly caused by dermatophytes. The diagnostic workup comprises microscopic examination, culture and/or molecular testing (nail scrapings). Local treatment with antifungal nail polish is recommended for mild or moderate nail infections. In case of moderate to severe onychomycosis, oral treatment is recommended (in the absence of contraindications). Treatment should consist of topical and systemic agents. The aim of this update of the German S1 guideline is to simplify the selection and implementation of appropriate diagnostics and treatment. The guideline was based on current international guidelines and the results of a literature review conducted by the experts of the guideline committee. This multidisciplinary committee consisted of representatives from the German Society of Dermatology (DDG), the German‐Speaking Mycological Society (DMykG), the Association of German Dermatologists (BVDD), the German Society for Hygiene and Microbiology (DGHM), the German Society of Pediatric and Adolescent Medicine (DGKJ), the Working Group for Pediatric Dermatology (APD) and the German Society for Pediatric Infectious Diseases (DGPI). The Division of Evidence‐based Medicine (dEBM) provided methodological assistance. The guideline was approved by the participating medical societies following a comprehensive internal and external review