An analysis of the use of graphics for information retrieval

Several research groups have addressed the problem of retrieving vector graphics. This work has, however, focused either on domain-dependent areas or was based on very simple graphics languages. Here we take a fresh look at the issue of graphics retrieval in general and in particular at the tasks which retrieval systems must support. The paper presents a series of case studies which explored the needs of professionals in the hope that these needs can help direct future graphics IR research. Suggested modelling techniques for some of the graphic collections are also presented

Efficient Analysis of Complex Diagrams using Constraint-Based Parsing

This paper describes substantial advances in the analysis (parsing) of diagrams using constraint grammars. The addition of set types to the grammar and spatial indexing of the data make it possible to efficiently parse real diagrams of substantial complexity. The system is probably the first to demonstrate efficient diagram parsing using grammars that easily be retargeted to other domains. The work assumes that the diagrams are available as a flat collection of graphics primitives: lines, polygons, circles, Bezier curves and text. This is appropriate for future electronic documents or for vectorized diagrams converted from scanned images. The classes of diagrams that we have analyzed include x,y data graphs and genetic diagrams drawn from the biological literature, as well as finite state automata diagrams (states and arcs). As an example, parsing a four-part data graph composed of 133 primitives required 35 sec using Macintosh Common Lisp on a Macintosh Quadra 700.Comment: 9 pages, Postscript, no fonts, compressed, uuencoded. Composed in MSWord 5.1a for the Mac. To appear in ICDAR '95. Other versions at ftp://ftp.ccs.neu.edu/pub/people/futrell

Scalable wavelet-based coding of irregular meshes with interactive region-of-interest support

This paper proposes a novel functionality in wavelet-based irregular mesh coding, which is interactive region-of-interest (ROI) support. The proposed approach enables the user to define the arbitrary ROIs at the decoder side and to prioritize and decode these regions at arbitrarily high-granularity levels. In this context, a novel adaptive wavelet transform for irregular meshes is proposed, which enables: 1) varying the resolution across the surface at arbitrarily fine-granularity levels and 2) dynamic tiling, which adapts the tile sizes to the local sampling densities at each resolution level. The proposed tiling approach enables a rate-distortion-optimal distribution of rate across spatial regions. When limiting the highest resolution ROI to the visible regions, the fine granularity of the proposed adaptive wavelet transform reduces the required amount of graphics memory by up to 50%. Furthermore, the required graphics memory for an arbitrary small ROI becomes negligible compared to rendering without ROI support, independent of any tiling decisions. Random access is provided by a novel dynamic tiling approach, which proves to be particularly beneficial for large models of over 10(6) similar to 10(7) vertices. The experiments show that the dynamic tiling introduces a limited lossless rate penalty compared to an equivalent codec without ROI support. Additionally, rate savings up to 85% are observed while decoding ROIs of tens of thousands of vertices

A Generative Model of People in Clothing

We present the first image-based generative model of people in clothing for the full body. We sidestep the commonly used complex graphics rendering pipeline and the need for high-quality 3D scans of dressed people. Instead, we learn generative models from a large image database. The main challenge is to cope with the high variance in human pose, shape and appearance. For this reason, pure image-based approaches have not been considered so far. We show that this challenge can be overcome by splitting the generating process in two parts. First, we learn to generate a semantic segmentation of the body and clothing. Second, we learn a conditional model on the resulting segments that creates realistic images. The full model is differentiable and can be conditioned on pose, shape or color. The result are samples of people in different clothing items and styles. The proposed model can generate entirely new people with realistic clothing. In several experiments we present encouraging results that suggest an entirely data-driven approach to people generation is possible

Shape: A 3D Modeling Tool for Astrophysics

We present a flexible interactive 3D morpho-kinematical modeling application for astrophysics. Compared to other systems, our application reduces the restrictions on the physical assumptions, data type and amount that is required for a reconstruction of an object's morphology. It is one of the first publicly available tools to apply interactive graphics to astrophysical modeling. The tool allows astrophysicists to provide a-priori knowledge about the object by interactively defining 3D structural elements. By direct comparison of model prediction with observational data, model parameters can then be automatically optimized to fit the observation. The tool has already been successfully used in a number of astrophysical research projects.Comment: 13 pages, 11 figures, accepted for publication in the "IEEE Transactions on Visualization and Computer Graphics