A new Edge Detector Based on Parametric Surface Model: Regression Surface Descriptor

In this paper we present a new methodology for edge detection in digital images. The first originality of the proposed method is to consider image content as a parametric surface. Then, an original parametric local model of this surface representing image content is proposed. The few parameters involved in the proposed model are shown to be very sensitive to discontinuities in surface which correspond to edges in image content. This naturally leads to the design of an efficient edge detector. Moreover, a thorough analysis of the proposed model also allows us to explain how these parameters can be used to obtain edge descriptors such as orientations and curvatures. In practice, the proposed methodology offers two main advantages. First, it has high customization possibilities in order to be adjusted to a wide range of different problems, from coarse to fine scale edge detection. Second, it is very robust to blurring process and additive noise. Numerical results are presented to emphasis these properties and to confirm efficiency of the proposed method through a comparative study with other edge detectors.Comment: 21 pages, 13 figures and 2 table

Seismic Stabilization of Historic Adobe Structures: Final Report of the Getty Seismic Adobe Project

Provides the final report of GSAP activities, and the first publication to provide an overview of the results of scale-model laboratory research along with field data from a survey of damage to historic adobe buildings after an actual earthquake

Using site-specific art as an alternative for interpreting Port Hudson State Historic Park, Louisiana

This study investigates the use of site-specific art as a means of enhancing and interpreting an historic battlefield. The finding of this study are demonstrated in a series of designs for interpretive installations for the Port Hudson State Historic Site, a Civil War battlefield located in Louisiana. The interpretive methods commonly used in historic battlefields today, as identified in chapter two of this thesis, tend to produce remote relationship between visitors of the current generation and the site. To help visitors understand the meaning of historic battlefields batter, site-specific art is introduced in this thesis as an instrument to retrieve the subtle relationship between humans and their land. To employ art as an interpretive in an historic battlefield is a novel experiment in the United States. This study therefore conducts a review of the genre of site-specific art in order to inform readers of its nature. Notable works by contemporary land artists are described, and certain landscape architects\u27 adaptation of site-specific art in historical commemorating are discussed as well. After modes of application of site-specific art are identified, I survey the local history of the study site in order to explore the site specificity of the place through its past patterns of human occupation. The settlements and the Civil War military deployments are both found to have been closely related to local geographic characteristics, demonstrating a high degree of material site-specificity. An ethnography of the Historic Site follows to discover the meanings that the Site\u27s staff and visitors routinely attach to it (immaterial site-specificity). Combing the results of these two studies, the sense of place and the fundamental interpretive subjects of the Site emerge. Several significant spots in the historic site are then selected to demonstrate site-specific art. Through a series of rehabilitative designs, this kind of creative interpretation is shown to be an effective means of conveying the meaning of an historic place to visitors. Applied in conjunction with the existing traditional interpretive methods, site-specific art is thus shown to be effective in bringing a close relationship between the current generation and their legacy of historic battlefields

A Gigantic Sarcopterygian (Tetrapodomorph Lobe-Finned Fish) from the Upper Devonian of Gondwana (Eden, New South Wales, Australia)

Edenopteron keithcrooki gen. et sp. nov. is described from the Famennian Worange Point Formation; the holotype is amongst the largest tristichopterids and sarcopterygians documented by semi-articulated remains from the Devonian Period. The new taxon has dentary fangs and premaxillary tusks, features assumed to be derived for large Northern Hemisphere tristichopterids (Eusthenodon, Hyneria, Langlieria). It resembles Eusthenodon in ornament, but is distinguished by longer proportions of the parietal compared to the post-parietal shield, and numerous differences in shape and proportions of other bones. Several characters (accessory vomers in the palate, submandibulars overlapping ventral jaw margin, scales ornamented with widely-spaced deep grooves) are recorded only in tristichopterids from East Gondwana (Australia-Antarctica). On this evidence Edenopteron gen. nov. is placed in an endemic Gondwanan subfamily Mandageriinae within the Tristichopteridae; it differs from the nominal genotype Mandageria in its larger size, less pointed skull, shape of the orbits and other skull characters. The hypothesis that tristichopterids evolved in Laurussia and later dispersed into Gondwana, and a derived subgroup of large Late Devonian genera dispersed from Gondwana, is inconsistent with the evidence of the new taxon. Using oldest fossil and most primitive clade criteria the most recent phylogeny resolves South China and Gondwana as areas of origin for all tetrapodomorphs. The immediate outgroup to tristichopterids remains unresolved - either Spodichthys from Greenland as recently proposed, or Marsdenichthys from Gondwana, earlier suggested to be the sister group to all tristichopterids. Both taxa combine two characters that do not co-occur in other tetrapodomorphs (extratemporal bone in the skull; non-cosmoid round scales with an internal boss). Recently both 'primitive' and 'derived' tristichopterids have been discovered in the late Middle Devonian of both hemispheres, implying extensive ghost lineages within the group. Resolving their phylogeny and biogeography will depend on a comprehensive new phylogenetic analysis.This research was supported by Australian Research Council [www.arc.gov.au] Discovery Grants DP0558499 (‘Australia’s exceptional Palaeozoic fossil fishes, and a Gondwana origin for land vertebrates’) and DP0772138 (‘Old brains, new data–early evolution of structural complexity in the vertebrate head’). Surface scanning and 3D printing equipment was partly financed by an Australian National University [www.anu.edu.au] Major Equipment Grant (10MEC15). No additional external funding received for this study. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Reconstructing vectorised photographic images

We address the problem of representing captured images in the continuous mathematical space more usually associated with certain forms of drawn ('vector') images. Such an image is resolution-independent so can be used as a master for varying resolution-specific formats. We briefly describe the main features of a vectorising codec for photographic images, whose significance is that drawing programs can access images and image components as first-class vector objects. This paper focuses on the problem of rendering from the isochromic contour form of a vectorised image and demonstrates a new fill algorithm which could also be used in drawing generally. The fill method is described in terms of level set diffusion equations for clarity. Finally we show that image warping is both simplified and enhanced in this form and that we can demonstrate real histogram equalisation with genuinely rectangular histograms

Machine Learning Approach to Retrieving Physical Variables from Remotely Sensed Data

Scientists from all over the world make use of remotely sensed data from hundreds of satellites to better understand the Earth. However, physical measurements from an instrument is sometimes missing either because the instrument hasn\u27t been launched yet or the design of the instrument omitted a particular spectral band. Measurements received from the instrument may also be corrupt due to malfunction in the detectors on the instrument. Fortunately, there are machine learning techniques to estimate the missing or corrupt data. Using these techniques we can make use of the available data to its full potential. We present work on four different problems where the use of machine learning techniques helps to extract more information from available data. We demonstrate how missing or corrupt spectral measurements from a sensor can be accurately interpolated from existing spectral observations. Sometimes this requires data fusion from multiple sensors at different spatial and spectral resolution. The reconstructed measurements can then be used to develop products useful to scientists, such as cloud-top pressure, or produce true color imagery for visualization. Additionally, segmentation and image processing techniques can help solve classification problems important for ocean studies, such as the detection of clear-sky over ocean for a sea surface temperature product. In each case, we provide detailed analysis of the problem and empirical evidence that these problems can be solved effectively using machine learning techniques

A combined first and second order variational approach for image reconstruction

In this paper we study a variational problem in the space of functions of bounded Hessian. Our model constitutes a straightforward higher-order extension of the well known ROF functional (total variation minimisation) to which we add a non-smooth second order regulariser. It combines convex functions of the total variation and the total variation of the first derivatives. In what follows, we prove existence and uniqueness of minimisers of the combined model and present the numerical solution of the corresponding discretised problem by employing the split Bregman method. The paper is furnished with applications of our model to image denoising, deblurring as well as image inpainting. The obtained numerical results are compared with results obtained from total generalised variation (TGV), infimal convolution and Euler's elastica, three other state of the art higher-order models. The numerical discussion confirms that the proposed higher-order model competes with models of its kind in avoiding the creation of undesirable artifacts and blocky-like structures in the reconstructed images -- a known disadvantage of the ROF model -- while being simple and efficiently numerically solvable.Comment: 34 pages, 89 figure