Figure 1: Real image augmented with a virtual laptop, with no occlusion handling (left), occlusion handling based directly on depths from an RGBD camera (centre), and occlusion using the proposed approach (right). Note that the inaccurate occlusion in the centre image is due to noise in the depth map, and the camera's inability to recover depth values for the user's watch.

A correct analysis of hierarchical selection processes must specify 1) the objects that succeed differentially as units, and 2) the properties that provide the causal bases for differential success. Here I illustrate how failing to recognize the units/bases distinction creates a contradiction in Elliott Sober's recent account of selection. A revised criterion for units of selection is developed and applied to examples at several biological levels. Criteria for bases of selection are discussed in terms of the degree of context-dependence and directness of a property's effect on the success of units. The significance of previous work by Sober, Wimsatt and Brandon is thereby clarified.

The historical data from the MEdium Resolution Imaging Spectrometer (MERIS) is an invaluable archive for studying global waters from inland lakes to open oceans. Although the MERIS sensor ceased to operate in April 2012, the data capacities are now re-established through the recently launched Sentinel-3 Ocean and Land Colour Instrument (OLCI). The development of a consistent time series for investigating phytoplankton phenology features is crucial if the potential of MERIS and OLCI data is to be fully exploited for inland water monitoring. This study presents a time series of phytoplankton abundance and bloom spatial extent for the highly eutrophic inland water of the Baltic Sea using the 10-year MERIS archive (2002-2011) and a chlorophyll-a based Summed Positive Peaks (SPP) algorithm. A gradient approach in conjunction with the histogram analysis was used to determine a global threshold from the entire collection of SPP images for identifying phytoplankton blooms. This allows spatio-temporal dynamics of daily bloom coverage, timing, phytoplankton abundance and spatial extent to be investigated for each Baltic basin. Furthermore, a number of meteorological and hydrological variables, including spring excess phosphate, summer sea surface temperature and photosynthetically active radiation, were explored using boosted regression trees and generalised additive models to understand the ecological response of phytoplankton assemblages to environmental perturbations and potential predictor variables of summer blooms. The results indicate that the surface layer excess phosphate available in February and March had paramount importance over all other variables considered in governing summer bloom abundance in the major Baltic basins. This finding allows new insights into the development of early warning systems for summer phytoplankton blooms in the Baltic Sea and elsewhere.

A novel approach, termed Summed Positive Peaks (SPP), is proposed for determining phytoplankton abundances (Chlorophyll-a or Chl-a) and surface phytoplankton bloom extent in the optically complex Baltic Sea. The SPP approach is established on the basis of a baseline subtraction method using Rayleigh corrected top-of-atmosphere data from the Medium Resolution Imaging Spectrometer (MERIS) measurements. It calculates the reflectance differences between phytoplankton related signals observed in the MERIS red and near infrared (NIR) bands, such as sun-induced chlorophyll fluorescence (SICF) and the backscattering at 709nm, and considers the summation of the positive line heights for estimating Chl-a concentrations. The SPP algorithm is calibrated against near coincident in situ data collected from three types of phytoplankton dominant waters encountered in the Baltic Sea during 2010 (N=379). The validation results show that the algorithm is capable of retrieving Chl-a concentrations ranging from 0.5 to 3mgm, with an RMSE of 0.24mgm (R=0.69, N=264). Additionally, the comparison results with several Chl-a algorithms demonstrates the robustness of the SPP approach and its sensitivity to low to medium biomass waters. Based on the red and NIR reflectance features, a flagging method is also proposed to distinguish intensive surface phytoplankton blooms from the background water.

When rendering virtual objects in a mixed reality application, it is helpful to have access to an environment map that captures the appearance of the scene from the perspective of the virtual object. It is straightforward to render virtual objects into such maps, but capturing and correctly rendering the real components of the scene into the map is much more challenging. This information is often recovered from physical light probes, such as reflective spheres or fisheye cameras, placed at the location of the virtual object in the scene. For many application areas, however, real light probes would be intrusive or impractical. Ideally, all of the information necessary to produce detailed environment maps could be captured using a single device. We introduce a method using an RGBD camera and a small fisheye camera, contained in a single unit, to create environment maps at any location in an indoor scene. The method combines the output from both cameras to correct for their limited field of view and the displacement from the virtual object, producing complete environment maps suitable for rendering the virtual content in real time. Our method improves on previous probeless approaches by its ability to recover high-frequency environment maps. We demonstrate how this can be used to render virtual objects which shadow, reflect and refract their environment convincingly.