Automatic Analysis of Lens Distortions in Image Registration

Geometric image registration by estimating homographies is an important processing step in a wide variety of computer vision applications. The 2D registration of two images does not require an explicit reconstruction of intrinsic or extrinsic camera parameters. However, correcting images for non-linear lens distortions is highly recommended. Unfortunately, standard calibration techniques are sometimes difficult to apply and reliable estimations of lens distortions can only rarely be obtained. In this paper we present a new technique for automatically detecting and categorising lens distortions in pairs of images by analysing registration results. The approach is based on a new metric for registration quality assessment and facilitates a PCA-based statistical model for classifying distortion effects. In doing so the overall importance for lens calibration and image corrections can be checked, and a measure for the efficiency of accordant correction steps is given

Automatic Analysis of Lens Distortions in Image Registration

Geometric image registration by estimating homographies is an important processing step in a wide variety of computer vision applications. The 2D registration of two images does not require an explicit reconstruction of intrinsic or extrinsic camera parameters. However, correcting images for non-linear lens distortions is highly recommended. Unfortunately, standard calibration techniques are sometimes difficult to apply and reliable estimations of lens distortions can only rarely be obtained. In this paper we present a new technique for automatically detecting and categorising lens distortions in pairs of images by analysing registration results. The approach is based on a new metric for registration quality assessment and facilitates a PCA-based statistical model for classifying distortion effects. In doing so the overall importance for lens calibration and image corrections can be checked, and a measure for the efficiency of accordant correction steps is given

Extraction of protein profiles from primary neurons using active contour models and wavelets

AbstractThe function of complex networks in the nervous system relies on the proper formation of neuronal contacts and their remodeling. To decipher the molecular mechanisms underlying these processes, it is essential to establish unbiased automated tools allowing the correlation of neurite morphology and the subcellular distribution of molecules by quantitative means.We developed NeuronAnalyzer2D, a plugin for ImageJ, which allows the extraction of neuronal cell morphologies from two dimensional high resolution images, and in particular their correlation with protein profiles determined by indirect immunostaining of primary neurons. The prominent feature of our approach is the ability to extract subcellular distributions of distinct biomolecules along neurites. To extract the complete areas of neurons, required for this analysis, we employ active contours with a new distance based energy. For locating the structural parts of neurons and various morphological parameters we adopt a wavelet based approach. The presented approach is able to extract distinctive profiles of several proteins and reports detailed morphology measurements on neurites.We compare the detected neurons from NeuronAnalyzer2D with those obtained by NeuriteTracer and Vaa3D-Neuron, two popular tools for automatic neurite tracing. The distinctive profiles extracted for several proteins, for example, of the mRNA binding protein ZBP1, and a comparative evaluation of the neuron segmentation results proves the high quality of the quantitative data and proves its practical utility for biomedical analyses

Nanoscale Magnetic Imaging using Circularly Polarized High-Harmonic Radiation

This work demonstrates nanoscale magnetic imaging using bright circularly polarized high-harmonic radiation. We utilize the magneto-optical contrast of worm-like magnetic domains in a Co/Pd multilayer structure, obtaining quantitative amplitude and phase maps by lensless imaging. A diffraction-limited spatial resolution of 49 nm is achieved with iterative phase reconstruction enhanced by a holographic mask. Harnessing the unique coherence of high harmonics, this approach will facilitate quantitative, element-specific and spatially-resolved studies of ultrafast magnetization dynamics, advancing both fundamental and applied aspects of nanoscale magnetism.Comment: Ofer Kfir and Sergey Zayko contributed equally to this work. Presented in CLEO 2017 (Oral) doi.org/10.1364/CLEO_QELS.2017.FW1H.

VISION - Vienna survey in Orion. III. Young stellar objects in Orion A

38 pages, 25 figures, Accepted for publication by A&A. Reproduced with permission from Astronomy & Astrophysics. © 2018 ESOWe extend and refine the existing young stellar object (YSO) catalogs for the Orion A molecular cloud, the closest massive star-forming region to Earth. This updated catalog is driven by the large spatial coverage (18.3 deg^2, ~950 pc^2), seeing limited resolution (~0.7''), and sensitivity (Ks<19 mag) of the ESO-VISTA near-infrared survey of the Orion A cloud (VISION). Combined with archival mid- to far-infrared data, the VISTA data allow for a refined and more robust source selection. We estimate that among previously known protostars and pre-main-sequence stars with disks, source contamination levels (false positives) are at least ∼7% and ∼2.5%, respectively, mostly due to background galaxies and nebulosities. We identify 274 new YSO candidates using VISTA/Spitzer based selections within previously analyzed regions, and VISTA/WISE based selections to add sources in the surroundings, beyond previously analyzed regions. The WISE selection method recovers about 59% of the known YSOs in Orion A's low-mass star-forming part L1641, which shows what can be achieved by the all-sky WISE survey in combination with deep near-infrared data in regions without the influence of massive stars. The new catalog contains 2978 YSOs, which were classified based on the de-reddened mid-infrared spectral index into 188 protostars, 184 flat-spectrum sources, and 2606 pre-main-sequence stars with circumstellar disks. We find a statistically significant difference in the spatial distribution of the three evolutionary classes with respect to regions of high dust column-density, confirming that flat-spectrum sources are at a younger evolutionary phase compared to Class IIs, and are not a sub-sample seen at particular viewing angles.Peer reviewedFinal Accepted Versio

Distribution, structure and function of Nordic eelgrass (Zostera marina) ecosystems: implications for coastal management and conservation

This paper focuses on the marine foundation eelgrass species, Zostera marina, along a gradient from the northern Baltic Sea to the north-east Atlantic. This vast region supports a minimum of 1480 km2 eelgrass (maximum >2100 km2), which corresponds to more than four times the previously quantified area of eelgrass in Western Europe. Eelgrass meadows in the low salinity Baltic Sea support the highest diversity (4–6 spp.) of angiosperms overall, but eelgrass productivity is low (<2 g dw m-2 d-1) and meadows are isolated and genetically impoverished. Higher salinity areas support monospecific meadows, with higher productivity (3–10 g dw m-2 d-1) and greater genetic connectivity. The salinity gradient further imposes functional differences in biodiversity and food webs, in particular a decline in number, but increase in biomass of mesograzers in the Baltic. Significant declines in eelgrass depth limits and areal cover are documented, particularly in regions experiencing high human pressure. The failure of eelgrass to re-establish itself in affected areas, despite nutrient reductions and improved water quality, signals complex recovery trajectories and calls for much greater conservation effort to protect existing meadows. The knowledge base for Nordic eelgrass meadows is broad and sufficient to establish monitoring objectives across nine national borders. Nevertheless, ensuring awareness of their vulnerability remains challenging. Given the areal extent of Nordic eelgrass systems and the ecosystem services they provide, it is crucial to further develop incentives for protecting them

Socio-economic vision graph generation and handover in distributed smart camera networks

In this article we present an approach to object tracking handover in a network of smart cameras, based on self-interested autonomous agents, which exchange responsibility for tracking objects in a market mechanism, in order to maximise their own utility. A novel ant-colony inspired mechanism is used to learn the vision graph, that is, the camera neighbourhood relations, during runtime, which may then be used to optimise communication between cameras. The key benefits of our completely decentralised approach are on the one hand generating the vision graph online, enabling efficient deployment in unknown scenarios and camera network topologies, and on the other hand relying only on local information, increasing the robustness of the system. Since our market-based approach does not rely on a priori topology information, the need for any multicamera calibration can be avoided. We have evaluated our approach both in a simulation study and in network of real distributed smart cameras