Survey of the Arkansas Campanulaceae (Including the Lobeliaceae)

This paper is a summary of the Campanulaceae of Arkansas, based on the material on file in the University of Arkansas herbarium. A key to the species is included, followed by an alphabetical listing by genus and species of the taxa in the Campanulaceae known to occur in the state. After each taxon, the following information is included in this order: blooming period (as indicated on our material), known distribution in general terms (NW-northwest, E-east, G-general, C- central, etc.), habitat, chromosome number (as reported in Darlington & Wylie, 1955; in the Index to Plant Chromosome Numbers, Vol. I.II, and Supplement; and in Vol. 50 of Regnum Vegebabile), synonymy in double parentheses (this is minimized), citation of two specimens, and in some cases comments about the particular taxon. All of the taxa have been previously reported from the state. One species previously listed for the state is excluded. The survey includes 12 species in 4 genera. The distribution of most of the taxa is probably more extensive than indicated. Differences in the key to the species, as compared to Steyermark (1963) or McVaugh (1943), reflect overlap in characters of Lobelia appendiculata and L. spicata observed in the study of Arkansas material

Recovering facial shape using a statistical model of surface normal direction

In this paper, we show how a statistical model of facial shape can be embedded within a shape-from-shading algorithm. We describe how facial shape can be captured using a statistical model of variations in surface normal direction. To construct this model, we make use of the azimuthal equidistant projection to map the distribution of surface normals from the polar representation on a unit sphere to Cartesian points on a local tangent plane. The distribution of surface normal directions is captured using the covariance matrix for the projected point positions. The eigenvectors of the covariance matrix define the modes of shape-variation in the fields of transformed surface normals. We show how this model can be trained using surface normal data acquired from range images and how to fit the model to intensity images of faces using constraints on the surface normal direction provided by Lambert's law. We demonstrate that the combination of a global statistical constraint and local irradiance constraint yields an efficient and accurate approach to facial shape recovery and is capable of recovering fine local surface details. We assess the accuracy of the technique on a variety of images with ground truth and real-world images

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