18808395
Stippling and silhouettes rendering in geometry-image space
We present a novel non-photorealistic rendering method that performs all operations in a geometry-image domain. We first apply global conformal parameterization to the input geometry model and generate corresponding geometry images. Strokes and silhouettes are then computed in the geometry-image domain. The geometry-image space provides combined benefits of the existing image space and object space approaches. It allows us to take advantage of the regularity of 2D images and yet still have full access to the object geometry information. A wide range of image processing tools can be leveraged to assist various operations involved in achieving non-photorealistic rendering with coherence.
7259311
Image-Based Rendering of Surfaces from Volume Data
We present an image-based rendering technique to accelerate rendering of surfaces from volume data. We cache the fully volume rendered image (called keyview) and use it to generate novel views without ray-casting every pixel. This is achieved by first constructing an underlying surface model of the volume and then texture mapping the keyview onto the geometry.When the novel view moves slightly away from the keyview, most of the original visible regions in the keyview are still visible in the novel view. Therefore, we only need to cast rays for pixels in the newly visible regions, which usually occupy only a small portion of the whole image, resulting in a substantial speedup. We have applied our technique to a virtual colonoscopy system and have obtained an interactive navigation speed through a 5123 size patient colon. Our experiments demonstrate an average of an order of magnitude speedup over that of traditional volume rendering, compromising very little on image quality.
6763688
Paint Inspired Color Mixing and Compositing for Visualization
Color is often used to convey information, and color compositing is often required while visualizing multiattribute information. This paper proposes an alternative method for color compositing. In order to present understandable color blending to the general public, several techniques are proposed. First, a paint-inspired RYB color space is used. In addition, noise patterns are employed to produce subregions of pure color within an overlapped region. We show examples to demonstrate the effectiveness of our technique for visualization
53234559
Adaptive Perspective Ray Casting
4000958
A Better Heuristic for Preemptive Parallel Machine Scheduling with Batch Setup Times
This paper addresses the problem of scheduling N jobs on M identical parallel machines with the objective of minimizing the makespan. Jobs are divided into B batches. A sequence-independent batch setup time on a machine is incurred whenever the machine starts its processing on or switches it from a job in one batch to a job in another batch. On the basis of a heuristic for this NP-hard problem proposed by Monma and Potts, a modified heuristic that requires the same implementing time $O(N + (M + B)\log (M + B))$ and is asymptotically optimal is presented. Furthermore, for a certain class of problems, which includes the case in which each batch contains a single job, it has the worst-case performance ratio $\tau _M = \max \{ {\frac{{3M}}{{2M + 1}},\frac{{3M - 4}}{{2M - 2}}} \}$.
3424570
Prediction Cubes
In this paper, we introduce a new family of tools for exploratory data analysis, called prediction cubes. As in standard OLAP data cubes, each cell in a prediction cube contains a value that summarizes the data belonging to that cell, and the granularity of cells can be changed via operations such as roll-up and drill-down. In contrast to data cubes, in which each cell value is computed by an aggregate function, e.g., SUM or AVG, each cell value in a prediction cube summarizes a predictive model trained on the data corresponding to that cell, and characterizes its decision behavior or predictiveness. In this paper, we propose and motivate prediction cubes, and show that they can be efficiently computed by exploiting the idea of model decomposition.
11015337
Composite subset measures
Measures are numeric summaries of a collection of data records produced by applying aggregation functions. Summarizing a collection of subsets of a large dataset, by computing a measure for each subset in the (typically, user-specified) collection is a fundamental problem. The multidimensional data model, which treats records as points in a space defined by dimension attributes, offers a natural space of data subsets to be considered as summarization candidates, and traditional SQL and OLAP constructs, such as GROUP BY and CUBE, allow us to compute measures for subsets drawn from this space. However, GROUP BY only allows us to summarize a limited collection of subsets, and CUBE summarizes all subsets in this space. Further, they restrict the measure used to summarize a data subset to be a one-step aggregation, using functions such as SUM, of field-values in the data records.In this paper, we introduce composite subset measures, computed by aggregating not only data records but also the measures of other related subsets. We allow summarization of naturally related regions in the multidimensional space, offering more flexibility than either GROUP BY or CUBE in the choice of what data subsets to summarize. Thus, our framework allows more meaningful summaries to be computed for a targeted collection of data subsets.We propose an algebra called AW-RA and an equivalent pictorial language called aggregation workflows. Aggregation workflows allow for intuitive expression of composite measure queries, and the underlying algebra is designed to facilitate efficient multiscan execution. We describe an evaluation framework based on multiple passes of sorting and scanning over the original dataset. In each pass, several measures are evaluated simultaneously, and dependencies between these measures and containment relationships between the underlying subsets of data are orchestrated to reduce the memory footprint of the computation. We present a performance evaluation that demonstrates the benefits of our approach.
2577652
Bellwether analysis: predicting global aggregates from local regions
Massive datasets are becoming commonplace in a wide range of domains, and mining them is recognized as a challenging problem with great potential value. Motivated by this challenge, much effort has been concentrated on developing scalable versions of machine learning algorithms. An often overlooked issue is that large datasets are rarely labeled with the outputs that we wish to learn to predict, due to the human labor required. We make the key observation that analysts can often use queries to define labels for cases, which leads to the problem of learning to predict such query-produced labels. Of course, if a dataset is available in its entirety, we can simply run the query again to compute labels. The interesting scenarios are those where, after the predictive model is trained, new data is gathered at significant incremental cost and, perhaps, over time. The challenge is to accurately predict the query-labels for the projected completion of new datasets, based only on certain cost-effective subsets, which we call bellwethers.
4000197
Lot-sizing scheduling with batch setup times
This paper is concerned with scheduling independent jobs on m parallel machines in such a way that the makespan is minimized. Each job j is allowed to split arbitrarily into several parts, which can be individually processed on any machine at any time. However, a setup for uninterrupted sj time units is required before any split part of job j can be processed on any machine. The problem is strongly NP-hard if the number m of machines is variable and weakly NP-hard otherwise. We give a polynomial-time $$\frac{5}{3}$$-approximation algorithm for the former case and a fully polynomial-time approximation scheme for the latter.