Trees, bushes, and other plants are ubiquitous in urban environments, and realistic models of trees can add a great deal of realism to a digital urban scene. There has been much research on modeling tree structures, but limited work on reconstructing the geometry of real-world trees -even then, most works have focused on reconstruction from photographs aided by significant user interaction. In this paper, we perform active laser scanning of real-world vegetation and present an automatic approach that robustly reconstructs skeletal structures of trees, from which full geometry can be generated. The core of our method is a series of global optimizations that fit skeletal structures to the often sparse, incomplete, and noisy point data. A significant benefit of our approach is its ability to reconstruct multiple overlapping trees simultaneously without segmentation. We demonstrate the effectiveness and robustness of our approach on many raw scans of different tree varieties.

Abstract-We present an algorithm for performing adaptive real-time level-of-detail-based rendering for triangulated polygonal models. The simplifications are dependent on viewing direction, lighting, and visibility and are performed by taking advantage of image-space, object-space, and frame-to-frame coherences. In contrast to the traditional approaches of precomputing a fixed number of level-of-detail representations for a given object our approach involves statically generating a continuous level-ofdetail representation for the object. This representation is then used at run-time to guide the selection of appropriate triangles for display. The list of displayed triangles is updated incrementally from one frame to the next. Our approach is more effective than the current level-of-detail-based rendering approaches for most scientific visualization applications where there are a limited number of highly complex objects that stay relatively close to the viewer. Our approach is applicable for scalar (such as distance from the viewer) as well as vector (such as normal direction) attributes.

We propose a technique for performing view-dependent geometry and topology simplifications for level-of-detail

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In this paper we present a novel approach for interactive rendering of large terrain datasets which is based on subdividing the terrain into rectangular patches at different resolutions. Each patch is represented by four triangular tiles which can be at different resolutions; and four strips which are used to stitch the four tiles in a seamless manner. As a result, our scheme maintains resolution changes within patches and not across patches. At runtime, the terrain patches are used to construct a level of detail based on view-parameters. The selected level of detail only includes the layout of the patches and the resolutions at boundary edges. Since adjacent patches agree on the resolution of common edges, the resulted mesh does not include any cracks or degenerate triangles. The GPU generates the meshes of the patches by using scaled instances of cached tiles and assigning elevation for each vertex from the cached textures. Our algorithm manages to achieve quality images at high frame rates while providing seamless transition between different levels of detail. Keywords:

Page layout analysis is a fundamental step of any document image understanding system. We introduce an approach that segments text appearing in page margins (a.k.a side-notes text) from manuscripts with complex layout format. Simple and discriminative features are extracted in a connected-component level and subsequently robust feature vectors are generated. Multilayer perception classifier is exploited to classify connected components to the relevant class of text. A voting scheme is then applied to refine the resulting segmentation and produce the final classification. In contrast to state-of-the-art segmentation approaches, this method is independent of block segmentation, as well as pixel level analysis. The proposed method has been trained and tested on a dataset that contains a variety of complex side-notes layout formats, achieving a segmentation accuracy of about 95%.

In this paper, we propose a novel external-memory algorithm to support view-dependent

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