6386106
Procedural Image Processing for Visualization
We present a novel Procedural Image Processing (PIP) method and demonstrate its applications in visualization. PIP modulates the sampling positions of a conventional image processing kernel (e.g. edge detection filter) through a procedural perturbation function. When properly designed, PIP can produce a variety of styles for edge depiction, varying on width, solidity, and pattern, etc. In addition to producing artistic stylization, in this paper we demonstrate that PIP can be employed to achieve various visualization tasks, such as contour enhancement, focus+context visualization, importance driven visualization and uncertainty visualization. 
 
PIP produces unique effects that often either cannot be easily achieved through conventional filters or would require multiple pass filtering. PIP perturbation functions are either defined by analytical expressions or encoded in pre-generated images. We leverage the programmable fragment shader of the current graphics hardware for achieving the operations in real-time.
16773627
Illustrating surfaces in volume
This paper presents a novel framework for illustrating surfaces in a volume. Surfaces are illustrated by drawing only feature lines, such as silhouettes, valleys, ridges, and surface hatching strokes, and are embedded in volume renderings. This framework promises effective illustration of both surfaces and volumes without occluding or cluttering each other. A two-step approach has been taken: the first step depicts surfaces; the second step performs volume rendering, at the same time embedding surfaces from the first step.
 We introduce Procedurally Perturbed Image Processing (PIP), a new method for enhancing both feature detection and depiction of surfaces. We also present implementation strategies, especially those leveraging modern graphics hardware, for delivering an interactive rendering system. Our implementation results have shown that this mixed form of rendering improves volume visualization and is efficient.
13005280
High dynamic range volume visualization
High resolution volumes require high precision compositing to preserve detailed structures. This is even more desirable for volumes with high dynamic range values. After the high precision intermediate image has been computed, simply rounding up pixel values to regular display scales loses the computed details. In this paper, we present a novel high dynamic range volume visualization method for rendering volume data with both high spatial and intensity resolutions. Our method performs high precision volume rendering followed by dynamic tone mapping to preserve details on regular display devices. By leveraging available high dynamic range image display algorithms, this dynamic tone mapping can be automatically adjusted to enhance selected features for the final display. We also present a novel transfer function design interface with nonlinear magnification of the density range and logarithmic scaling of the color/opacity range to facilitate high dynamic range volume visualization. By leveraging modern commodity graphics hardware and out-of-core acceleration, our system can produce an effective visualization of huge volume data.
9140729
INSPIRE: an interactive image assisted non-photorealistic rendering system
We present a GPU supported interactive non-photorealistic rendering system, INSPIRE, which performs feature extraction in both image space, on intermediately rendered images, and object space, on models of various representations, e.g., point, polygon, or hybrid models, without needing connectivity information. INSPIRE obtains interactive NPR rendering with most styles of existing NPR systems, but offers more flexibility on model representations and compromises little on rendering speed.
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.
12895532
Volume cutout
We present a novel method for cutting out 3D volumetric structures based on simple strokes that are drawn directly on volume rendered images. The freehand strokes roughly mark out objects of interest and background. Our system then automatically segments the regions of interest and refines their boundaries in the rendered image. These 2D segmentation results provide constraints for 3D segmentation in the volume dataset. The objects of interest are then efficiently cut out from the volume via a combination of our novel two-pass graph cuts algorithm and the pre-computed over-segmentation. Our method improves traditional, fully automatic segmentation by involving human users in the process, yet minimizing user input and providing timely feedback. Our experiments show that our method extracts volumetric structures precisely and efficiently while requiring little skill or effort from the user.
1221771
Multi-scale feature identification using evolution strategies
An image usually contains a number of different features and regions. Many image-related applications, such as content-based image retrieval and MRI-based diagnosis, often require the ability to identify and mark features within the image. For images containing a specific sort of feature (e.g. convective storm) or region (e.g. earthquake debris), that feature or region is always located adjacent to other features and regions on the image. A generic framework for automatically identifying features in images based on evolutionary computation is proposed here. The significant characteristic of the method is that it does not require segmentation. We use evolution strategies as the optimization algorithm to identify features. The system is based on a conjecture that certain filters will give prominent responses to certain features. The identified features are represented as regions enclosed within the chosen search structure-the ellipse. By defining filter response criteria as the fitness function, evolution strategies succeeds in finding the feature in a much more efficient way than, say, segmentation.
223678
Automatic classification of digestive organs in wireless capsule endoscopy videos
Wireless Capsule Endoscopy (WCE) allows a physician to examine the entire small intestine without any surgical operation. With the miniaturization of wireless and camera technologies the ability comes to view the entire gestational track with little effort. Although WCE is a technical break-through that allows us to access the entire intestine without surgery, it is reported that a medical clinician spends one or two hours to assess a WCE video, It limits the number of examinations possible, and incur considerable amount of costs. To reduce the assessment time, it is critical to develop a technique to automatically discriminate digestive organs such as esophagus, stomach, small intestinal (i.e., duodenum, jejunum, and ileum) and colon. In this paper, we propose a novel technique to segment a WCE video into these anatomic parts based on color change pattern analysis. The basic idea is that the each digestive organ has different patterns of intestinal contractions that are quantified as the features. We present the experimental results that demonstrate the effectiveness of the proposed method.
6360330
An empirical study of reliable multicast protocols over Ethernet-connected networks
Recent advances in multicasting over the Internet present new opportunities for improving communication performance in clusters of workstations. The standard IP multicast, however only supports unreliable multicast, which is difficult to use for building high level message passing routines. Thus, reliable multicast primitives must be implemented over the standard IP multicast to facilitate the use of multicast for high performance communication on clusters of workstations. Although many reliable multicast protocols have been proposed for the wide area Internet environment, the impact of architectural features of local area networks (LANs) on the reliable multicast protocols has not been thoroughly studied. Efficient reliable multicast protocols for LANs must exploit these features to achieve the best performance. In this paper we study four types of reliable multicast protocols: the ACK-based protocols, the NAK-based protocols with polling, the ring-based protocols, and the tree-based protocols. We evaluate the performance of the protocols over Ethernet-connected networks, study the impact of architectural features of the Ethernet on the performance of the protocols, and investigate the methods to exploit these features to achieve the best performance.
7026270
Branch elimination by condition merging
Conditional branches are expensive. Branches require a significant percentage of execution cycles since they occur frequently and cause pipeline flushes when mispredicted. In addition, branches result in forks in the control flow, which can prevent other code-improving transformations from being applied. In this paper we describe profile-based techniques for replacing the execution of a set of two or more branches with a single branch on a conventional scalar processor. These sets of branches can include tests of multiple variables. For instance, the test if (p1 ! = 0 && p2 ! = 0), which is testing for NULL pointers, can be replaced with if (p1 & p2 ! = 0). Program profiling is performed to target condition merging along frequently executed paths. The results show that eliminating branches by merging conditions can significantly reduce the number of conditional branches executed in non-numerical applications.
124461239
Subband noise estimation for adaptive wavelet shrinkage
In this article, we present an adaptive image denoising method based on subband noise modeling. For wavelet shrinkage, choosing the threshold depends on correctly estimating the noise variance. By modeling the inter-subband noise variance with a parameterized normalized exponential function, the problem becomes identifying the maximum noise variance. Such a maximum exists in the highest decomposition level and can be estimated by locating the extreme of the first derivative of the subband variance function. The experiments demonstrate that our method outperforms peers, especially in the cases of large noise variance.