Raghu Machiraju

The transfer function bake-off

A liquid fuel pumping apparatus for supplying fuel to an internal combustion engine includes a member which is movable against the action of a spring system by means of centrifugally operable weights. The member is coupled to a fuel control rod of the pumping apparatus, and the spring system includes first and second springs. The first spring is preloaded so as to be deflected by the weights only when the speed of the engine attains a predetermined value. The other spring is deflected by the weights at lower engine speeds and also acts to transmit the force exerted by the weights to the first spring.

Evaluation and design of filters using a Taylor series expansion

We describe a new method for analyzing, classifying, and evaluating filters that can be applied to interpolation filters as well as to arbitrary derivative filters of any order. Our analysis is based on the Taylor series expansion of the convolution sum. Our analysis shows the need and derives the method for the normalization of derivative filter weights. Under certain minimal restrictions of the underlying function, we are able to compute tight absolute error bounds of the reconstruction process. We demonstrate the utilization of our methods to the analysis of the class of cubic BC-spline filters. As our technique is not restricted to interpolation filters, we are able to show that the Catmull-Rom spline filter and its derivative are the most accurate reconstruction and derivative filters, respectively, among the class of BC-spline filters. We also present a new derivative filter which features better spatial accuracy than any derivative BC-spline filter, and is optimal within our framework. We conclude by demonstrating the use of these optimal filters for accurate interpolation and gradient estimation in volume rendering.

Coverage optimization to support security monitoring

The placement of sensors to support security monitoring is obviously critical as it will directly impact the efficacy of allocated resources and system performance. It is critical to be able to observe and monitor the greatest total area possible. In addition, it is necessary to be able to spatially track the movement of people and activities in support of security. It is shown that important aspects of the security sensor placement problem can be modeled using the maximal covering location problem (MCLP) and/or the backup coverage location problem (BCLP) combined with visibility analysis. Thus, an approach is detailed for supporting security monitoring. The approach is applied in the context of video sensor placement in an urban area, illustrating the various tradeoffs that can be identified using optimization-based techniques.

A comparison of normal estimation schemes

The task of reconstructing the derivative of a discrete function is essential for its shading and rendering as well as being widely used in image processing and analysis. We survey the possible methods for normal estimation in volume rendering and divide them into two classes based on the delivered numerical accuracy. The three members of the first class determine the normal in two steps by employing both interpolation and derivative filters. Among these is a new method which has never been realized. The members of the first class are all equally accurate. The second class has only one member and employs a continuous derivative filter obtained through the analytic derivation of an interpolation filter. We use the new method to analytically compare the accuracy of the first class with that of the second. As a result of our analysis we show that even inexpensive schemes can in fact be more accurate than high order methods. We describe the theoretical computational cost of applying the schemes in a volume rendering application and provide guidelines for helping one choose a scheme for estimating derivatives. In particular we find that the new method can be very inexpensive and can compete with the normal estimations which pre-shade and pre-classify the volume (M. Levoy, 1988).

Design of accurate and smooth filters for function and derivative reconstruction

The correct choice of function and derivative reconstruction filters is paramount to obtaining highly accurate renderings. Most filter choices are limited to a set of commonly used functions, and the visualization practitioner has so far no way to state his preferences in a convenient fashion. Much work has been done towards the design and specification of filters using frequency based methods. However for visualization algorithms it is more natural to specify a filter in terms of the smoothness of the resulting reconstructed function and the spatial reconstruction error. Hence, the authors present a methodology for designing filters based on spatial smoothness and accuracy criteria. They first state their design criteria and then provide an example of a filter design exercise. They also use the filters so designed for volume rendering of sampled data sets and a synthetic test function. They demonstrate that their results compare favorably with existing methods.

A bilinear illumination model for robust face recognition

We present a technique to generate an illumination subspace for arbitrary 3D faces based on the statistics of measured illuminations under variable lighting conditions from many subjects. A bilinear model based on the higher-order singular value decomposition is used to create a compact illumination subspace given arbitrary shape parameters from a parametric 3D face model. Using a fitting procedure based on minimizing the distance of the input image to the dynamically changing illumination subspace, we reconstruct a shape-specific illumination subspace from a single photograph. We use the reconstructed illumination subspace in various face recognition experiments with variable lighting conditions and obtain accuracies which are very competitive with previous methods that require specific training sessions or multiple images of the subject

Salient iso-surface detection with model-independent statistical signatures

Volume graphics has not been accepted for widespread use. One of the inhibiting reasons is the lack of general methods for data-analysis and simple interfaces for data exploration. An error-and-trial iterative procedure is often used to select a desirable transfer function or mine the dataset for salient iso-values. New semi-automatic methods that are also data-centric have shown much promise. However, general and robust methods are still needed for data-exploration and analysis. In this paper, we propose general model-independent statistical methods based on central moments of data. Using these techniques we show how salient iso-surfaces at material boundaries can be determined. We provide examples from the medical and computational domain to demonstrate the effectiveness of our methods.

Atypical E2F Repressors and Activators Coordinate Placental Development

The evolutionarily ancient arm of the E2f family of transcription factors consisting of the two atypical members E2f7 and E2f8 is essential for murine embryonic development. However, the critical tissues, cellular processes, and molecular pathways regulated by these two factors remain unknown. Using a series of fetal and placental lineage-specific cre mice, we show that E2F7/E2F8 functions in extraembryonic trophoblast lineages are both necessary and sufficient to carry fetuses to term. Expression profiling and biochemical approaches exposed the canonical E2F3a activator as a key family member that antagonizes E2F7/E2F8 functions. Remarkably, the concomitant loss of E2f3a normalized placental gene expression programs, corrected placental defects, and fostered the survival of E2f7/E2f8-deficient embryos to birth. In summary, we identified a placental transcriptional network tightly coordinated by activation and repression through two distinct arms of the E2F family that is essential for extraembryonic cell proliferation, placental development, and fetal viability.

14 – Detection and Visualization of Vortices

A vortex is characterized by the swirling motion of fluid around a central region. This characterization stems from the visual perception of swirling phenomena that are pervasive throughout the natural world. However, translating this intuitive description of a vortex into a formal definition has been quite a challenge. Despite the lack of a formal definition, various detection algorithms have been implemented that can adequately identify vortices in most computational datasets. This chapter presents an overview of existing detection methods; in particular, the focus is on nine methods that are representative of the state of the art. The chapter begins by presenting three taxonomies for classifying these nine detection methods. It then describes each algorithm, along with pseudo-code where appropriate. Next, the chapter describes a recently developed verification algorithm for swirling flows. The chapter also discusses the different visualization techniques for vortices.

Geometric verification of swirling features in flow fields

In this paper, we present a verification algorithm for swirling features in flow fields, based on the geometry of streamlines. The features of interest in this case are vortices. Without a formal definition, existing detection algorithms lack the ability to accurately identify these features, and the current method for verifying the accuracy of their results is by human visual inspection. Our verification algorithm addresses this issue by automating the visual inspection process. It is based on identifying the swirling streamlines that surround the candidate vortex cores. We apply our algorithm to both numerically simulated and procedurally generated datasets to illustrate the efficacy of our approach.