Assessing the drivers shaping global patterns of urban vegetation landscape structure

Vegetation is one of the main resources involve in ecosystem functioning and providing ecosystem services in urban areas. Little is known on the landscape structure patterns of vegetation existing in urban areas at the global scale and the drivers of these patterns. We studied the landscape structure of one hundred cities around the globe, and their relation to demography (population), socioeconomic factors (GDP, Gini Index), climate factors (temperature and rain) and topographic characteristics (altitude, variation in altitude). The data revealed that the best descriptors of landscape structure were amount, fragmentation and spatial distribution of vegetation. Populated cities tend to have less, more fragmented, less connected vegetation with a centre of the city with low vegetation cover. Results also provided insights on the influence of socioeconomics at a global scale, as landscape structure was more fragmented in areas that are economically unequal and coming from emergent economies. This study shows the effects of the social system and climate on urban landscape patterns that gives useful insights for the distribution in the provision of ecosystem services in urban areas and therefore the maintenance of human well-being. This information can support local and global policy and planning which is committing our cities to provide accessible and inclusive green space for all urban inhabitants.

Registration of eye reflection and scene images using an aspherical eye model

This paper introduces an image registration algorithm between an eye reflection and a scene image. Although there are currently a large number of image registration algorithms, this task remains difficult due to nonlinear distortions at the eye surface and large amounts of noise, such as iris texture, eyelids, eyelashes, and their shadows. To overcome this issue, we developed an image registration method combining an aspherical eye model that simulates nonlinear distortions considering eye geometry and a two-step iterative registration strategy that obtains dense correspondence of the feature points to achieve accurate image registrations for the entire image region. We obtained a database of eye reflection and scene images featuring four subjects in indoor and outdoor scenes and compared the registration performance with different asphericity conditions. Results showed that the proposed approach can perform accurate registration with an average accuracy of 1.05 deg by using the aspherical cornea model. This work is relevant for eye image analysis in general, enabling novel applications and scenarios.

Corneal-Imaging Calibration for Optical See-Through Head-Mounted Displays

In recent years optical see-through head-mounted displays (OST-HMDs) have moved from conceptual research to a market of mass-produced devices with new models and applications being released continuously. It remains challenging to deploy augmented reality (AR) applications that require consistent spatial visualization. Examples include maintenance, training and medical tasks, as the view of the attached scene camera is shifted from the user's view. A calibration step can compute the relationship between the HMD-screen and the user's eye to align the digital content. However, this alignment is only viable as long as the display does not move, an assumption that rarely holds for an extended period of time. As a consequence, continuous recalibration is necessary. Manual calibration methods are tedious and rarely support practical applications. Existing automated methods do not account for user-specific parameters and are error prone. We propose the combination of a pre-calibrated display with a per-frame estimation of the user's cornea position to estimate the individual eye center and continuously recalibrate the system. With this, we also obtain the gaze direction, which allows for instantaneous uncalibrated eye gaze tracking, without the need for additional hardware and complex illumination. Contrary to existing methods, we use simple image processing and do not rely on iris tracking, which is typically noisy and can be ambiguous. Evaluation with simulated and real data shows that our approach achieves a more accurate and stable eye pose estimation, which results in an improved and practical calibration with a largely improved distribution of projection error.

Structural diversity and spectral and thermal properties of the first alkaline earth metal tetracyanidoborates: [Mg(H2O)6][B(CN)4]2, [Mg(H2O)2][B(CN)4]2, [Mg(DMF)6][B(CN)4]2, [Ca(H2O)3][B(CN)4]2, and [Ca(H2O)2(CH3CN)][B(CN)4]2

The syntheses, structures, and spectral and thermal properties of the first tetracyanidoborate salts coordinating weakly to solvated alkaline earth cations are discussed.

Ferrocenyldiselenolate-Stabilized Copper−Selenium Clusters

The silylated ferrocenyl selenium reagent 1,1'-Fe(eta5-C5H4SeSiMe3)2 has been used for the high yield formation of the phosphine-ligated copper complexes Cu2(fcSe2)(PiPr3)2 (1) and Cu4(fcSe2)2(PnPr3)4 (2) from solublilized CuOAc, as determined by single-crystal X-ray diffraction. The incorporation of a source of Se2- into the reaction scheme with the reagent Se(SiMe3)2 yields the mixed selenide/ferrocenyldiselenolate cluster [Cu20Se6(Se2fc)4(PnPr3)10] (3). Partial substitution of the PnPr3 ligand shell in 3 with the phosphinothiol Ph2P(CH2)3SH leads to an expansion of the framework and the high yield formation of the crystallographically characterized cluster Cu36(fcSe2)6Se12(PnPr3)10(Ph2P(CH2)3SH)2 (5), which contains surface alkylthiol groups on a copper-selenium core.