Optimal 3D surface reconstruction from multiview photographic images

This paper describes a new method for reconstructing 3D surface using a small number, e.g. 10, of 2D photographic images. The images are taken at different viewing directions by a perspective camera with full prior knowledge of the camera configurations. The reconstructed object's surface is represented a set of triangular facets. We empirically demonstrate that if the viewing directions are uniformly distributed around the object's viewing sphere, then the reconstructed 3D points optimally cluster closely on a highly curved part of the surface and are widely, spread on smooth or fat parts. The advantage of this property is that the reconstructed points along a surface or a contour generator are not undersampled or underrepresented because surfaces or contours should be sampled or represented with more densely points where their curvatures are high. The more complex the contour's shape, the greater is the number of points required, but the greater the number of points is automatically generated by the proposed method Given that the viewing directions are uniformly distributed, the number and distribution of the reconstructed points depend on the shape or the curvature of the surface regardless of the size of the surface or the size of the object.

3D surface point and wireframe reconstruction from multiview photographic images

This paper describes a new method for reconstructing 3D surface points and a wireframe on the surface of a freeform object using a small number, e.g. 10, of 2D photographic images. The images are taken at different viewing directions by a perspective camera with full prior knowledge of the camera configurations. The reconstructed surface points are frontier points and the wireframe is a network of contour generators. Both of them are reconstructed by pairing apparent contours in the 2D images. Unlike previous works, we empirically demonstrate that if the viewing directions are uniformly distributed around the object's viewing sphere, then the reconstructed 3D points automatically cluster closely on a highly curved part of the surface and are widely spread on smooth or flat parts. The advantage of this property is that the reconstructed points along a surface or a contour generator are not under-sampled or under-represented because surfaces or contours should be sampled or represented with more densely points where their curvatures are high. The more complex the contour's shape, the greater is the number of points required, but the greater the number of points is automatically generated by the proposed method. Given that the viewing directions are uniformly distributed, the number and distribution of the reconstructed points depend on the shape or the curvature of the surface regardless of the size of the surface or the size of the object. The unique pattern of the reconstructed points and contours may be used in 31) object recognition and measurement without computationally intensive full surface reconstruction. The results are obtained from both computer-generated and real objects. (C) 2007 Elsevier B.V. All rights reserved.

Optimal 3D surface reconstruction from a small number of conventional 2D X-ray images

This paper describes a method for reconstructing 3D frontier points, contour generators and surfaces of anatomical objects or smooth surfaces from a small number, e. g. 10, of conventional 2D X-ray images. The X-ray images are taken at different viewing directions with full prior knowledge of the X-ray source and sensor configurations. Unlike previous works, we empirically demonstrate that if the viewing directions are uniformly distributed around the object's viewing sphere, then the reconstructed 3D points automatically cluster closely on a highly curved part of the surface and are widely spread on smooth or flat parts. The advantage of this property is that the reconstructed points along a surface or a contour generator are not under-sampled or under-represented because surfaces or contours should be sampled or represented with more densely points where their curvatures are high. The more complex the contour's shape, the greater is the number of points required, but the greater the number of points is automatically generated by the proposed method. Given that the number of viewing directions is fixed and the viewing directions are uniformly distributed, the number and distribution of the reconstructed points depend on the shape or the curvature of the surface regardless of the size of the surface or the size of the object. The technique may be used not only in medicine but also in industrial applications.

A finite difference scheme for inviscid flows with non-eequilibrium chemistry and internal energy

A finite difference scheme is presented for the inviscid terms of the equations of compressible fluid dynamics with general non-equilibrium chemistry and internal energy.

Sea-breeze modification of the growth of a marine internal boundary layer

A numerical mesoscale model is used to make a high-resolution simulation of the marine boundary layer in the Persian Gulf, during conditions of offshore flow from Saudi Arabia. A marine internal boundary layer (MIBL) and a sea-breeze circulation (SBC) are found to co-exist. The sea breeze develops in the mid-afternoon, at which time its front is displaced several tens of kilometres offshore. Between the coast and the sea-breeze system, the MIBL that occurs is consistent with a picture described in the existing literature. However, the MIBL is perturbed by the SBC, the boundary layer deepening significantly seaward of the sea-breeze front. Our analysis suggests that this strong, localized deepening is not a direct consequence of frontal uplift, but rather that the immediate cause is the retardation of the prevailing, low-level offshore wind by the SBC. The simulated boundary-layer development can be accounted for by using a simple 1D Lagrangian model of growth driven by the surface heat flux. This model is obtained as a straightforward modification of an established MIBL analytic growth model.

Reconstruction of historical pollination rates reveals linked declines of pollinators and plants

Widespread reports of low pollination rates suggest a recent anthropogenic decline in pollination that could threaten natural and agricultural ecosystems. Nevertheless, unequivocal evidence for a decline in pollination over time has remained elusive because it was not possible to determine historical pollination rates. Here we demonstrate a widely applicable method for reconstructing historical pollination rates, thus allowing comparison with contemporary rates from the same sites. We focused on the relationship between the oil-collecting bee Rediviva peringueyi (Melittidae) and the guild of oil-secreting orchid species (Coryciinae) that depends on it for pollination. The guild is distributed across the highly transformed and fragmented lowlands of the Cape Region of South Africa. We show that rehydrated herbarium specimens of Pterygodium catholicum, the most abundant member of the guild, contain a record of past pollinator activity in the form of pollinarium removal rates. Analysis of a pollination time series showed a recent decline in pollination on Signal Hill, a small urban conservation area. The same herbaria contain historical species occurrence data. We analyzed this data and found that there has been a contemporaneous shift in orchid guild composition in urban areas due to the local extirpation of the non-clonal species, consistent with their greater dependence on seeds and pollination for population persistence.

Inverting recurrent neural networks for internal model control of nonlinear systems

In this paper, we show how a set of recently derived theoretical results for recurrent neural networks can be applied to the production of an internal model control system for a nonlinear plant. The results include determination of the relative order of a recurrent neural network and invertibility of such a network. A closed loop controller is produced without the need to retrain the neural network plant model. Stability of the closed-loop controller is also demonstrated.

Internal model control of nonlinear systems through the inversion of recurrent neural networks

Recurrent neural networks can be used for both the identification and control of nonlinear systems. This paper takes a previously derived set of theoretical results about recurrent neural networks and applies them to the task of providing internal model control for a nonlinear plant. Using the theoretical results, we show how an inverse controller can be produced from a neural network model of the plant, without the need to train an additional network to perform the inverse control.