Network visualization allows a quick glance at how nodes (or actors) are connected by edges (or ties). A conventional network diagram of "contact tree" maps out a root and branches that represent the structure of nodes and edges, often without further specifying leaves or fruits that would have grown from small branches. By furnishing such a network structure with leaves and fruits, we reveal details about "contacts" in our ContactTrees that underline ties and relationships. Our elegant design employs a bottom-up approach that resembles a recent attempt to understand subjective well-being by means of a series of emotions. Such a bottom-up approach to social-network studies decomposes each tie into a series of interactions or contacts, which help deepen our understanding of the complexity embedded in a network structure. Unlike previous network visualizations, ContactTrees can highlight how relationships form and change based upon interactions among actors, and how relationships and networks vary by contact attributes. Based on a botanical tree metaphor, the design is easy to construct and the resulting tree-like visualization can display many properties at both tie and contact levels, a key ingredient missing from conventional techniques of network visualization. We first demonstrate ContactTrees using a dataset consisting of three waves of 3-month contact diaries over the 2004-2012 period, then compare ContactTrees with alternative tools and discuss how this tool can be applied to other types of datasets.
Following the seminal papers of Ulam @cite_39 and Hondal @cite_26 , computer modelling of trees has been an active area of research. The Previous Work section of @cite_24 gives a good overview of previous results. These methods attempt to characterize the way real-world botanical trees grow. Therefore, they do not reflect a structure defined a priori. Moreover, most of them incorporate random settings, which make them incompatible with our purpose: Our visualization must incorporate a pre-defined structure and be based on a deterministic algorithm to facilitate comparisons.
@cite_24 @cite_26 @cite_39
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A method is proposed for photo-realistic modeling and visualization of a growing tree. Recent visualization methods have focused on producing smoothly blending branching structures, however, these methods fail to account for the inclusion of non-smooth features such as branch bark ridges and bud scale scars. These features constitute an important visual aspect of a living tree, and are also observed to vary over time. The proposed method incorporates these features by using an hierarchical implicit modeling system, which provides a variety of tools for combining surface components in both smooth and non smooth configurations. A procedural interface to this system supports the use of inverse modeling, which is a global-to-local methodology, where the local properties of plant organs are described in terms of their global position within the tree architecture. Inverse modeling is used to define both the tree structure at any time, and a continuous developmental sequence for the tree from a seedling. These techniques provide an intuitive paradigm for the definition of complex tree growth sequences and their subsequent visualization, based solely on observed phenomena. Thus, a key advantage is that they do not require any knowledge of, or simulation of, the underlying biological processes. Categories and Subject Descriptors (according to ACM CCS): I.3.5 [Computer Graphics]: Curve, surface, solid, and object representations I.3.7 [Computer Graphics]: Animation Because of their complexity, plant models used in computer graphics are commonly created with proceduralmethods. A difficult problem is the user control of these models: a small number of parameters is insufficient tospecify plant characteristics in detail, while large numbers of parameters are tedious to manipulate and difficultto comprehend. To address this problem, we propose a method for managing parameters involved in plant modelmanipulation. Specifically, we introduce decomposition graphs as multiscale representations of plant structuresand present interactive tools for designing trees that operate on decomposition graphs. The supported operationsinclude browsing of the parameter space, editing of generalized parameters (scalars, functions, and branchingsystem silhouettes), and the definition of dependencies between parameters. We illustrate our method by creatingmodels of bonsai trees. 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 skelet al structures of trees, from which full geometry can be generated. The core of our method is a series of global optimizations that fit skelet al 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. We present a novel image-based technique for modeling complex unfoliaged trees. Existing tree modeling tools either require capturing a large number of views for dense 3D reconstruction or rely on user inputs and botanic rules to synthesize natural-looking tree geometry. In this paper, we focus on faithfully recovering real instead of realistically-looking tree geometry from a sparse set of images. Our solution directly integrates 2D 3D tree topology as shape priors into the modeling process. For each input view, we first estimate a 2D skeleton graph from its matte image and then find a 2D skeleton tree from the graph by imposing tree topology. We develop a simple but effective technique for computing the optimal 3D skeleton tree most consistent with the 2D skeletons. For each edge in the 3D skeleton tree, we further apply volumetric reconstruction to recover its corresponding curved branch. Finally, we use piecewise cylinders to approximate each branch from the volumetric results. We demonstrate our framework on a variety of trees to illustrate the robustness and usefulness of our technique.