Feature-rich distance-based terrain synthesis

This thesis describes a novel terrain synthesis method based on distances in a weighted graph. The method begins with a regular lattice with arbitrary edge weights; heights are determined by path cost from a set of generator nodes. The shapes of individual terrain features, such as mountains, hills, and craters, are specified by a monotonically decreasing profile describing the cross-sectional shape of a feature, while the locations of features in the terrain are specified by placing the generators. Pathing places ridges whose initial location have a dendritic shape. The method is robust and easy to control, making it possible to create pareidolia effects. It can produce a wide range of realistic synthetic terrains such as mountain ranges, craters, faults, cinder cones, and hills. The algorithm incorporates random graph edge weights, permits the inclusion of multiple topography profiles, and allows precise control over placement of terrain features and their heights. These properties all allow the artist to create highly heterogeneous terrains that compare quite favorably to existing methods

Level set segmentation of bovine corpora lutea in ex situ ovarian ultrasound images

<p>Abstract</p> <p>Background</p> <p>The objective of this study was to investigate the viability of level set image segmentation methods for the detection of corpora lutea (corpus luteum, CL) boundaries in ultrasonographic ovarian images. It was hypothesized that bovine CL boundaries could be located within 1–2 mm by a level set image segmentation methodology.</p> <p>Methods</p> <p>Level set methods embed a 2D contour in a 3D surface and evolve that surface over time according to an image-dependent speed function. A speed function suitable for segmentation of CL's in ovarian ultrasound images was developed. An initial contour was manually placed and contour evolution was allowed to proceed until the rate of change of the area was sufficiently small. The method was tested on ovarian ultrasonographic images (<it>n </it>= 8) obtained <it>ex situ</it>. A expert in ovarian ultrasound interpretation delineated CL boundaries manually to serve as a "ground truth". Accuracy of the level set segmentation algorithm was determined by comparing semi-automatically determined contours with ground truth contours using the mean absolute difference (MAD), root mean squared difference (RMSD), Hausdorff distance (HD), sensitivity, and specificity metrics.</p> <p>Results and discussion</p> <p>The mean MAD was 0.87 mm (sigma = 0.36 mm), RMSD was 1.1 mm (sigma = 0.47 mm), and HD was 3.4 mm (sigma = 2.0 mm) indicating that, on average, boundaries were accurate within 1–2 mm, however, deviations in excess of 3 mm from the ground truth were observed indicating under- or over-expansion of the contour. Mean sensitivity and specificity were 0.814 (sigma = 0.171) and 0.990 (sigma = 0.00786), respectively, indicating that CLs were consistently undersegmented but rarely did the contour interior include pixels that were judged by the human expert not to be part of the CL. It was observed that in localities where gradient magnitudes within the CL were strong due to high contrast speckle, contour expansion stopped too early.</p> <p>Conclusion</p> <p>The hypothesis that level set segmentation can be accurate to within 1–2 mm on average was supported, although there can be some greater deviation. The method was robust to boundary leakage as evidenced by the high specificity. It was concluded that the technique is promising and that a suitable data set of human ovarian images should be obtained to conduct further studies.</p

Water Drops on Surfaces

Water motion is a highly complex phenomenon. From ocean waves to raindrops, water is described by a wide range of shapes and is affected by a wide range of forces. This paper presents a physically-based method of modeling the latter phenomenon- raindrops. This model can handle bodies of water with varying underlying terrain, as well as many different fluids, but cannot represent spray systems, which are commonly seen when waves collide with solid masses. However, in the context of this research, this model is used strictly for water drops on surfaces. At the heart of this model is an underlying graph (or grid), as proposed in [Kass and Miller 1990] and later refined in [Mould and Yang 1997]. Representing a volume of water via this method allows for a quick and accurate representation of various water phenomena. With this model intact, I can model each node (or grid point) in the graph as a column of water which is connected to its neighbours. In other words, this is a grid based simulation. Because I limit the context of this research to water drops on surfaces, I do not break the columns up into cells. This paper intends to use the preceding model to simulate water drops on surfaces. It extends the model to account for uneven distributions of forces on a surface as well as the strength between water molecules

Feature-rich distance-based terrain synthesis

This paper describes a novel terrain synthesis method based on distances in a weighted graph. A height field is determined by least-cost paths in a weighted graph from a set of generator nodes. The shapes of individual terrain features, such as mountains, hills, and craters, are specified by a monotonically decreasing profile describing the cross-sectional shape of a feature. The locations of features in the terrain are specified by placing the generators; secondary ridges are placed by pathing. We show the method to be robust and easy to control, even making it possible to embed images in terrain shadows. The method can produce a wide range of realistic synthetic terrains such as mountain ranges, craters, cinder cones, and hills. The ability to manually place terrain features that incorporate multiple profiles produces heterogeneous terrains that compare favorably to existing methods