This paper introduces a method to recover photometric parameters of a set of 3D surfaces from a single image with significant global-illumination effects such as interreflections and transparencies. Since this problem is ambiguous for arbitrary unknown scenes, our formulation assumes that the scene consists of a small set of photometrically homogeneous surfaces with known 3D shapes, illuminated by known light sources.We show that under these conditions, the system of nonlinear equations that defines how the image is formed may be factorized into a vector composed only of products of some photometric parameters, and a matrix, whose elements depend non-linearly on both the known illumination, the known 3D shapes and the remaining photometric parameters. This factorization leads to an efficient optimization-based algorithm to compute all unknown photometric parameters from a single input image. Experiments with real data show that this algorithm is more stable and efficient than simpler alternatives.
We evaluated fungal and bacterial diversity in an established moss carpet on King George Island, Antarctica, affected by ‘fairy ring’ disease using metabarcoding. These microbial communities were assessed through the main stages of the disease. A total of 127 fungal and 706 bacterial taxa were assigned. The phylum Ascomycota dominated the fungal assemblages, followed by Basidiomycota, Rozellomycota, Chytridiomycota, Mortierellomycota and Monoblepharomycota. The fungal community displayed high indices of diversity, richness and dominance, which increased from healthy through infected to dead moss samples. Bacterial diversity and richness were greatest in healthy moss and least within the infected fairy ring. Chalara sp. 1, Alpinaria sp., Helotiaceae sp. 2, Chaetothyriales sp. 1, Ascomycota sp. 1, Rozellomycota sp. and Fungi sp. were most abundant within the fairy ring samples. A range of fungal taxa were more abundant in dead rather than healthy or fairy ring moss samples. The dominant prokaryotic phyla were Actinobacteriota, Proteobacteria, Bacteroidota and Cyanobacteria. The taxon Cyanobacteriia sp., whilst consistently dominant, were less abundant in fairy ring samples. Microbacteriaceae sp. and Chloroflexi sp. were the most abundant taxa within the fairy rings. Our data confirmed the presence and abundance of a range of plant pathogenic fungi, supporting the hypothesis that the disease is linked with multiple fungal taxas. Further studies are required to characterise the interactions between plant pathogenic fungi and their host Antarctic mosses. Monitoring the dynamics of mutualist, phytopathogenic and decomposer microorganisms associated with moss carpets may provide bioindicators of moss health.
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