PlaNet - Photo Geolocation with Convolutional Neural Networks

Is it possible to build a system to determine the location where a photo was taken using just its pixels? In general, the problem seems exceptionally difficult: it is trivial to construct situations where no location can be inferred. Yet images often contain informative cues such as landmarks, weather patterns, vegetation, road markings, and architectural details, which in combination may allow one to determine an approximate location and occasionally an exact location. Websites such as GeoGuessr and View from your Window suggest that humans are relatively good at integrating these cues to geolocate images, especially en-masse. In computer vision, the photo geolocation problem is usually approached using image retrieval methods. In contrast, we pose the problem as one of classification by subdividing the surface of the earth into thousands of multi-scale geographic cells, and train a deep network using millions of geotagged images. While previous approaches only recognize landmarks or perform approximate matching using global image descriptors, our model is able to use and integrate multiple visible cues. We show that the resulting model, called PlaNet, outperforms previous approaches and even attains superhuman levels of accuracy in some cases. Moreover, we extend our model to photo albums by combining it with a long short-term memory (LSTM) architecture. By learning to exploit temporal coherence to geolocate uncertain photos, we demonstrate that this model achieves a 50% performance improvement over the single-image model

An empirical vegetation correction for soil water content quantification using cosmic ray probes

Cosmic ray probes are an emerging technology to continuously monitor soil water content at a scale significant to land surface processes. However, the application of this method is hampered by its susceptibility to the presence of aboveground biomass. Here we present a simple empirical framework to account for moderation of fast neutrons by aboveground biomass in the calibration. The method extends the N0-calibration function and was developed using an extensive data set from a network of 10 cosmic ray probes located in the Rur catchment, Germany. The results suggest a 0.9% reduction in fast neutron intensity per 1 kg of dry aboveground biomass per m2 or per 2 kg of biomass water equivalent per m2. We successfully tested the novel vegetation correction using temporary cosmic ray probe measurements along a strong gradient in biomass due to deforestation, and using the COSMIC, and the hmf method as independent soil water content retrieval algorithms. The extended N0-calibration function was able to explain 95% of the overall variability in fast neutron intensity

From Multiview Image Curves to 3D Drawings

Reconstructing 3D scenes from multiple views has made impressive strides in recent years, chiefly by correlating isolated feature points, intensity patterns, or curvilinear structures. In the general setting - without controlled acquisition, abundant texture, curves and surfaces following specific models or limiting scene complexity - most methods produce unorganized point clouds, meshes, or voxel representations, with some exceptions producing unorganized clouds of 3D curve fragments. Ideally, many applications require structured representations of curves, surfaces and their spatial relationships. This paper presents a step in this direction by formulating an approach that combines 2D image curves into a collection of 3D curves, with topological connectivity between them represented as a 3D graph. This results in a 3D drawing, which is complementary to surface representations in the same sense as a 3D scaffold complements a tent taut over it. We evaluate our results against truth on synthetic and real datasets.Comment: Expanded ECCV 2016 version with tweaked figures and including an overview of the supplementary material available at multiview-3d-drawing.sourceforge.ne

Single-Image Depth Prediction Makes Feature Matching Easier

Good local features improve the robustness of many 3D re-localization and multi-view reconstruction pipelines. The problem is that viewing angle and distance severely impact the recognizability of a local feature. Attempts to improve appearance invariance by choosing better local feature points or by leveraging outside information, have come with pre-requisites that made some of them impractical. In this paper, we propose a surprisingly effective enhancement to local feature extraction, which improves matching. We show that CNN-based depths inferred from single RGB images are quite helpful, despite their flaws. They allow us to pre-warp images and rectify perspective distortions, to significantly enhance SIFT and BRISK features, enabling more good matches, even when cameras are looking at the same scene but in opposite directions.Comment: 14 pages, 7 figures, accepted for publication at the European conference on computer vision (ECCV) 202

Albumin Microspheres as “Trans-ferry-beads” for Easy Cell Passaging in Cell Culture Technology

Protein hydrogels represent ideal materials for advanced cell culture applications, including 3D-cultivation of even fastidious cells. Key properties of fully functional and, at the same time, economically successful cell culture materials are excellent biocompatibility and advanced fabrication processes allowing their easy production even on a large scale based on affordable compounds. Chemical crosslinking of bovine serum albumin (BSA) with N-(3-dimethylaminopropyl)-N’-ethylcarbodiimide hydrochloride (EDC) in a water-in-oil emulsion with isoparaffinic oil as the continuous phase and sorbitan monooleate as surfactant generates micro-meter-scale spherical particles. They allow a significant simplification of an indispensable and laborious step in traditional cell culture workflows. This cell passaging (or splitting) to fresh culture vessels/flasks conventionally requires harsh trypsinization, which can be omitted by using the “trans-ferry-beads” presented here. When added to different pre-cultivated adherent cell lines, the beads are efficiently boarded by cells as passengers and can be easily transferred afterward for the embarkment of novel flasks. After this procedure, cells are perfectly viable and show normal growth behavior. Thus, the trans-ferry-beads not only may become extremely affordable as a final product but also may generally replace trypsinization in conventional cell culture, thereby opening new routes for the establishment of optimized and resource-efficient workflows in biological and medical cell culture laboratories

A supervised hierarchical segmentation of remote-sensing images using a committee of multi-scale convolutional neural networks

This paper presents a supervised, hierarchical remote-sensing image segmentation technique using a committee of multi-scale convolutional neural networks. With existing techniques, segmentation is achieved through fine-tuning a set of predefined feature detectors. However, such a solution is not robust since the introduction of new sensors or applications would require novel features and techniques to be developed. Conversely, the proposed method achieves segmentation through a set of learnt feature detectors. In order to learn feature detectors, the proposed method exploits a committee of convolutional neural networks that perform multi-scale analysis on each band in order to derive individual confidence maps on region boundaries. Confidence maps are then inter-fused in order to produce a fused confidence map. Furthermore, the fused map is intra-fused using a morphological scheme into a hierarchical segmentation map. The proposed method is quantitatively compared to baseline techniques on a publicly available data set. The results presented in this paper highlight the improved accuracy of the proposed method

Semi-automatic identification of punching areas for tissue microarray building: the tubular breast cancer pilot study

Background: Tissue MicroArray technology aims to perform immunohistochemical staining on hundreds of different tissue samples simultaneously. It allows faster analysis, considerably reducing costs incurred in staining. A time consuming phase of the methodology is the selection of tissue areas within paraffin blocks: no utilities have been developed for the identification of areas to be punched from the donor block and assembled in the recipient block.Results: The presented work supports, in the specific case of a primary subtype of breast cancer (tubular breast cancer), the semi-automatic discrimination and localization between normal and pathological regions within the tissues. The diagnosis is performed by analysing specific morphological features of the sample such as the absence of a double layer of cells around the lumen and the decay of a regular glands-and-lobules structure. These features are analysed using an algorithm which performs the extraction of morphological parameters from images and compares them to experimentally validated threshold values. Results are satisfactory since in most of the cases the automatic diagnosis matches the response of the pathologists. In particular, on a total of 1296 sub-images showing normal and pathological areas of breast specimens, algorithm accuracy, sensitivity and specificity are respectively 89%, 84% and 94%.Conclusions: The proposed work is a first attempt to demonstrate that automation in the Tissue MicroArray field is feasible and it can represent an important tool for scientists to cope with this high-throughput technique

Polymer-lipid interactions:biomimetic self-assembly behaviour and surface properties of poly(styrene-alt-maleic acid) with diacylphosphatidylcholines

Abstract Various lubricating body fluids at tissue interfaces are composed mainly of combinations of phospholipids and amphipathic apoproteins. The challenge in producing synthetic replacements for them is not replacing the phospholipid, which is readily available in synthetic form, but replacing the apoprotein component, more specifically, its unique biophysical properties rather than its chemistry. The potential of amphiphilic reactive hypercoiling behaviour of poly(styrene-alt-maleic acid) (PSMA) was studied in combination with two diacylphosphatidylcholines (PC) of different chain lengths in aqueous solution. The surface properties of the mixtures were characterized by conventional Langmuir-Wilhelmy balance (surface pressure under compression) and the du Noüy tensiometer (surface tension of the non-compressed mixtures). Surface tension values and 31P NMR demonstrated that self-assembly of polymer-phospholipid mixtures were pH and concentration-dependent. Finally, the particle size and zeta potential measurements of this self-assembly showed that it can form negatively charged nanosized structures that might find use as drug or lipids release systems on interfaces such as the tear film or lung interfacial layers. The structural reorganization was sensitive to the alkyl chain length of the PC

Novel standards in the measurement of rat insulin granules combining electron microscopy, high-content image analysis and in silico modelling

Knowledge of number, size and content of insulin secretory granules is pivotal for understanding the physiology of pancreatic beta cells. Here we re-evaluated key structural features of rat beta cells, including insulin granule size, number and distribution as well as cell size