A method that achieves closed boundary finding in images (including slice images) with sub-pixel precision while enabling expression of sharp corners in that boundary is described. The method is a new extension to the well-known Marching Squares (MS) 2D isocontouring method that recovers sharp corner features that MS usually recovers as chamfered. The method has two major components: (1) detection of areas in the input image likely to contain sharp corner features, and (2) examination of image locations directly adjacent to the area with likely corners. Results of applying the new method, as well as its performance analysis, are also shown.
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Three new techniques for synthesizing realistic renderings of cumulus clouds are introduced and evaluated. The techniques utilize variations of the Perlin Noise and Koch fractals to achieve a reasonable cloud-like shape and texture. To evaluate the quality of renderings produced by the techniques, three classes of texture features are considered using cluster quality measures. Rendering quality is also evaluated versus real images using shape and texture features.
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Two alternatives to the standard (central differencing) method for estimating normals of Marching Cubes isosurfaces are considered. The methods are based on higher order approximations of dataset gradients. Of primary concern here are the effects of these methods on rendering quality, which is evaluated here through pixel-by-pixel comparisons of typical-fidelity isosurfaces versus high-fidelity rendering achievable from analytically derived formulae. The evaluations also consider effects of noise on isosurface rendering quality for renderings based on standard versus higher order gradients.
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