Abstract—Most fingerprint-based biometric systems store the minutiae template of a user in the database. It has been traditionally assumed that the minutiae template of a user does not reveal any information about the original fingerprint. In this paper, we challenge this notion and show that three levels of information about the parent fingerprint can be elicited from the minutiae template alone, viz., 1) the orientation field information, 2) the class or type information, and 3) the friction ridge structure. The orientation estimation algorithm determines the direction of local ridges using the evidence of minutiae triplets. The estimated orientation field, along with the given minutiae distribution, is then used to predict the class of the fingerprint. Finally, the ridge structure of the parent fingerprint is generated using streamlines that are based on the estimated orientation field. Line Integral Convolution is used to impart texture to the ensuing ridges, resulting in a ridge map resembling the parent fingerprint. The salient feature of this noniterative method to generate ridges is its ability to preserve the minutiae at specified locations in the reconstructed ridge map. Experiments using a commercial fingerprint matcher suggest that the reconstructed ridge structure bears close resemblance to the parent fingerprint.

This paper lays the groundwork for comparing flow visualizations using streamlines and line integral convolution (LIC). Our approach is to identify and define relevant parameters in each of these flow visualization techniques. Mapping strategies are then designed to generate LIC-like images from streamlines and streamline-like images from LIC. The result is a technique which we call pseudo-LIC or PLIC. The main contribution being reported in this paper is a methodology for flexibly generating flow visualizations that span the spectrum of streamline-like to LIC-like. Among the advantages are: performance speedups over LIC, applicability to time varying data sets, and variable-speed animation. Key Words and Phrases: unsteady flow, variable speed animation, jitter, texture mapping, comparative visualization. 1

A new method for the simplification of flow fields is presented. It is based on continuous clustering. A well-known physical clustering model, the Cahn Hilliard model which describes phase separation, is modified to reflect the properties of the data to be visualized. Clusters are defined implicitly as connected components of the positivity set of a density function. An evolution equation for this function is obtained as a suitable gradient flow of an underlying anisotropic energy functional. Here, time serves as the scale parameter. The evolution is characterized by a successive coarsening of patterns --- the actual clustering --- during which the underlying simulation data specifies preferable pattern boundaries. We introduce specific physical quantities in the simulation to control the shape, orientation and distribution of the clusters, as a function of the underlying flow field. In addition the model is expanded involving elastic effects. Thereby in early stages of the evolution shear layer type representation of the flow field can be generated, whereas for later stages the distribution of clusters can be influenced. Furthermore, we incorporate upwind ideas to give the clusters an oriented drop--shaped appearance. Here we discuss the applicability of this new type of approach mainly for flow fields, where the cluster energy penalizes cross streamline boundaries. However, the method also carries provisions for other fields as well. The clusters can be displayed directly as a flow texture. Alternatively, the clusters can be visualized by iconic representations, which are positioned by using a skeletonization algorithm.

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Abstract—Design and control of vector fields is critical for many visualization and graphics tasks such as vector field visualization, fluid simulation, and texture synthesis. The fundamental qualitative structures associated with vector fields are fixed points, periodic orbits, and separatrices. In this paper, we provide a new technique that allows for the systematic creation and cancellation of fixed points and periodic orbits. This technique enables vector field design and editing on the plane and surfaces with desired qualitative properties. The technique is based on Conley theory, which provides a unified framework that supports the cancellation of fixed points and periodic orbits. We also introduce a novel periodic orbit extraction and visualization algorithm that detects, for the first time, periodic orbits on surfaces. Furthermore, we describe the application of our periodic orbit detection and vector field simplification algorithms to engine simulation data demonstrating the utility of the approach. We apply our design system to vector field visualization by creating data sets containing periodic orbits. This helps us understand the effectiveness of existing visualization techniques. Finally, we propose a new streamline-based technique that allows vector field topology to be easily identified. Index Terms—Vector field design, vector field visualization, vector field topology, vector field simplification, Morse decomposition, Conley index, periodic orbit detection, connection graphs. 1

Visualization of vector data produced from application areas such as computational fluid dynamics (CFD), environmental sciences, and material engineering is a challenging task. Traditional visualization approaches, such as vector plot, particle tracing, stream surfaces, volume rendering, and so on, often provide a rather coarse spatial resolution. This problem is tackled by texture-based algorithms, which can provide the flow visualization by high resolution output texture. Texturebased methods reveal to be effective, versatile, and suitable for a large spectrum of application. In this paper we review, outlining advantages and drawbacks, the most important texture-based techniques known in the literature for: 2D and 3D, steady and unsteady, regular and curvilinear vector grids. 1 INTRODUCTION Visualization of vector data has always been one of the most attractive challenges in the scientific visualization field; Figure 1 shows the classification of the main methodologies known in t...

Flow visualization has been a very attractive field within visualization research for a long time already. Usually huge datasets need to be processed, which often consist of multi-variate data with a really large number of sample locations, often arranged in multiple time-steps. Recently, the ever increasing performance of computers again has become a driving factor for a new boom in flow visualization (FlowViz), especially in FlowViz based on additional computation such as feature extraction, vector field clustering, and topology extraction. In this state-of-the-art report, an attempt was made to (1) provide a useful categorization of FlowViz solutions, (2) give a survey-like overview about existing solutions, and (3) focus on recent work, especially in the field of FlowViz based on derived data. We give careful consideration as to how these topics are best organized for such a presentation. In separate sections we describe (a) direct FlowViz techniques such as using arrows, (b) FlowViz using integral object such as stream lines, (c) space-filling FlowViz, including, spot noise or line integral convolution, and (d) FlowViz based on derived data such as flow topology. Within those sections, the discussion of FlowViz literature is sub-structured accoring to the dimensionality of the flow data (from 2D to 3D).

This paper presents several strategies to interactively explore 3D flow. Based on a fast illuminated streamlines algorithm, standard graphics hardware is sufficient to gain interactive rendering rates. Our approach does not require the user to have any prior knowledge of flow features. After the streamlines are computed in a short preprocessing time, the user can interactively change appearance and density of the streamlines to further explore the flow. Most important flow features like velocity or pressure not only can be mapped to all available streamline appearance properties like streamline width, material, opacity, but also to streamline density. To improve spatial perception of the 3D flow we apply techniques based on animation, depth cueing, and halos along a streamline if it is crossed by another streamline in the foreground. Finally, we make intense use of focus+context methods like magic volumes, region of interest driven streamline placing, and spotlights to solve the occlusion problem.

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This paper presents two new hardware-assisted rendering techniques developed for interactive visualization of the terascale data generated from numerical modeling of nextgeneration accelerator designs. The first technique, based on a hybrid rendering approach, makes possible interactive exploration of large-scale particle data from particle beam dynamics modeling. The second technique, based on a compact texture-enhanced representation, exploits the advanced features of commodity graphics cards to achieve perceptually effective visualization of the very dense and complex electromagnetic fields produced from the modeling of reflection and transmission properties of open structures in an accelerator design. Because of the collaborative nature of the overall accelerator modeling project, the visualization technology developed is for both desktop and remote visualization settings. We have tested the techniques using both timevarying particle data sets containing up to one billion particles per time step and electromagnetic field data sets with millions of mesh elements.