Applications of terrestrial LiDAR, infrared thermography, and photogrammetry for mapping volcanic rocks in southern BC

Remote sensing methods are widely used in geological applications today, as many outcrops are difficult to access. Terrestrial LiDAR, infrared thermography, and photogrammetry are used at two field sites in BC: the Cheakamus Valley Basalts (CVB) and Chilcotin Group basalts (CG). The physical properties of the rock at each field site such as composition, texture and structure were studied through remote sensing, and compared to analyses completed in the laboratory as well as traditional contact mapping. The CVB site consists of two outcrops of isolated lava flows approximately 10 km southwest of Whistler, BC, and the CG basalts are observed at the Chasm, a 7 km-long canyon approximately 20 km northeast of Clinton, BC. A virtual field site of the Chasm site was constructed from the remote sensing data, and in conjunction with these analyses, this research clearly shows that it is possible to remotely map otherwise inaccessible volcanic rock masses

Filtrage anisotrope robuste et segmentation par B-spline snake : application aux images échographiques

Le contexte de ce travail est le traitement d'images échographiques. Plus précisément, on s'est intéressé au filtrage et à la segmentation automatique d'images dégradées par du speckle. La première partie concerne les travaux effectués sur le filtrage du speckle. Ils ont abouti à la conception d'une méthode de diffusion anisotrope robuste, nommée -diffusion. Elle se fonde sur un coefficient de diffusion original qui exploite lui-mˆeme la statistique du coefficient de variation et une adaptation de la fonction de Tukey. Un estimateur robuste du paramètre d'échelle de ce filtre est présenté. L'évolution de la diffusion est modélisée par une équation aux dérivées partielles s'appliquant sur l'enveloppe du signal brut, non compressée logarithmiquement. Cette approche permet de réduire le bruit des images échographiques, tout en préservant les structures importantes pour leur interprétation. Dans la deuxieme partie, nous présentons un contour actif paramétrique de type B-spline snake. L'étude de la continuité géométrique des B-splines nous permet de justifier le choix de l'énergie interne. Nous proposons deux nouvelles énergies externes qui exploitent notamment un champ de flux de vecteurs gradients, nommé s-GVF, calculé sur une carte de coefficients de variation locaux. Une fonction d'inhibition contrôle l'influence respective de ces deux énergies externe lors de l'évolution du snake. Enfin, nous proposons une nouvelle méthode d'initialisation automatique pour contour actif paramétrique. Une application au cas du filtrage des images echographiques et de la segmentation des cavités cardiaques est présentée. Les résultats démontrent une robustesse et une précision accrue par les modèles proposés par rapport aux techniques classiques de filtrage et segmentation par contours actifs. ABSTRACT : This thesis presents a robust model for speckle anisotropic filtering, and a parametric active contour model (B-spline snake) for the segmentation of images affected by speckle. First an original diffusion tensor is developed. It is based on the Tukey's error norm and on a local estimation of the coefficient of variation. The diffusion evolution is modelled by a partial derivative equation for raw images with no log-compression. This model reduces speckle while preserving important image features that are used by doctors to perform a diagnosis. Then we present a B-spline snake model with an external energy term that uses the amplitude and direction of the coefficient of variation gradient. The geometric continuity is guaranted by a uniform parametrisation and an internal energy term which penalizes the curve for irregular nodes spacing. An application to ultrasound image filtering and heart cavities detection is presented

The deep structure of Gaussian scale space images

In order to be able to deal with the discrete nature of images in a continuous way, one can use results of the mathematical field of 'distribution theory'. Under almost trivial assumptions, like 'we know nothing', one ends up with convolving the image with a Gaussian filter. In this manner scale is introduced by means of the filter's width. The ensemble of the image and its convolved versions at al scales is called a 'Gaussian scale space image'. The filter's main property is that the scale derivative equals the Laplacean of the spatial variables: it is the Greens function of the so-called Heat, or Diffusion, Equation. The investigation of the image all scales simultaneously is called 'deep structure'. In this thesis I focus on the behaviour of the elementary topological items 'spatial critical points' and 'iso-intensity manifolds'. The spatial critical points are traced over scale. Generically they are annihilated and sometimes created pair wise, involving extrema and saddles. The locations of these so-called 'catastrophe events' are calculated with sub-pixel precision. Regarded in the scale space image, these spatial critical points form one-dimensional manifolds, the so-called critical curves. A second type of critical points is formed by the scale space saddles. They are the only possible critical points in the scale space image. At these points the iso-intensity manifolds exhibit special behaviour: they consist of two touching parts, of which one intersects an extremum that is part of the critical curve containing the scale space saddle. This part of the manifold uniquely assigns an area in scale space to this extremum. The remaining part uniquely assigns it to 'other structure'. Since this can be repeated, automatically an algorithm is obtained that reveals the 'hidden' structure present in the scale space image. This topological structure can be hierarchically presented as a binary tree, enabling one to (de-)select parts of it, sweeping out parts, simplify, etc. This structure can easily be projected to the initial image resulting in an uncommitted 'pre-segmentation': a segmentation of the image based on the topological properties without any user-defined parameters or whatsoever. Investigation of non-generic catastrophes shows that symmetries can easily be dealt with. Furthermore, the appearance of creations is shown to be nothing but (instable) protuberances at critical curves. There is also biological inspiration for using a Gaussian scale space, since the visual system seems to use Gaussian-like filters: we are able of seeing and interpreting multi-scale