Gradient vector flow: a new external force for snakes
Snakes, or active contours, are used extensively in computer vision and image processing applications, particularly to locate object boundaries. Problems associated with initialization and poor convergence to concave boundaries, however, have limited their utility. This paper develops a new external force for active contours, largely solving both problems. This external force, which we call gradient vector flow (GVF) is computed as a diffusion of the gradient vectors of a gray-level or binary edge map derived from the image. The resultant field has a large capture range and forces active contours into concave regions. Examples on simulated images and one real image are presented.

title: A topology preserving deformable model using level sets
abstract: Active contour and surface models, also known as deformable models, constitute a class of powerful segmentation techniques. Geometric deformable models implemented via level-set methods have advantages over parametric ones due to their intrinsic behavior, parameterization independence, and ease of implementation. However, a long claimed advantage of geometric deformable models, the ability to automatically handle topology changes, turns out to be a liability in applications where the objects to be segmented have a known topology that must be preserved. In this paper, we present a geometric deformable model that preserves topology using the simple point concept from digital topology. This algorithm maintains the other advantages of standard geometric deformable models including sub-pixel accuracy and production of nonintersecting curves (or surfaces). Several experiments on simulated and real data are provided to demonstrate the performance of the proposed algorithm.

title: Reconstruction of the human cerebral cortex from magnetic resonance images
abstract: Reconstructing the geometry of the human cerebral cortex from MR images is an important step in both brain mapping and surgical path planning applications. Difficulties with imaging noise, partial volume averaging, image intensity inhomogeneities, convoluted cortical structures, and the requirement to preserve anatomical topology make the development of accurate automated algorithms particularly challenging. Here the authors address each of these problems and describe a systematic method for obtaining a surface representation of the geometric central layer of the human cerebral cortex. Using fuzzy segmentation, an isosurface algorithm, and a deformable surface model, the method reconstructs the entire cortex with the correct topology, including deep convoluted sulci and gyri. The method is largely automated and its results are robust to imaging noise, partial volume averaging, and image intensity inhomogeneities. The performance of this method is demonstrated, both qualitatively and quantitatively, and the results of its application to six subjects and one simulated MR brain volume are presented.

title: Snakes, shapes, and gradient vector flow
abstract: Snakes, or active contours, are used extensively in computer vision and image processing applications, particularly to locate object boundaries. Problems associated with initialization and poor convergence to boundary concavities, however, have limited their utility. This paper presents a new external force for active contours, largely solving both problems. This external force, which we call gradient vector flow (GVF), is computed as a diffusion of the gradient vectors of a gray-level or binary edge map derived from the image. It differs fundamentally from traditional snake external forces in that it cannot be written as the negative gradient of a potential function, and the corresponding snake is formulated directly from a force balance condition rather than a variational formulation. Using several two-dimensional (2-D) examples and one three-dimensional (3-D) example, we show that GVF has a large capture range and is able to move snakes into boundary concavities.

title: A 2D moving grid geometric deformable model
abstract: Geometric deformable models based on the level set method have become very popular. To overcome an inherent limitation in accuracy while maintaining computational efficiency, adaptive grid techniques using local grid refinement have been developed for use with these models. However, this strategy requires a very complex data structure, yields large numbers of contour points, and is inconsistent with our previously presented topology-preserving geometric deformable model (TGDM). In this paper, we incorporate an alternative adaptive grid technique called the moving grid method into the geometric deformable model framework. We find that it is simpler to implement than grid refinement, requiring no large, complex, hierarchical data structures. It also limits the number of contour vertices in the final contour and supports the incorporation of the topology-preserving constraint of TGDM. After presenting the algorithm, we demonstrate its performance using both simulated and real images.

title: Reconstruction of the Central Layer of the Human Cerebral Cortex from MR Images
abstract: Reconstruction of the human cerebral cortex from MR images is a fundamental step in human brain mapping and in applications such as surgical path planning. In a previous paper, we described a method for obtaining a surface representation of the central layer of the human cerebral cortex using fuzzy segmentation and a deformable surface model. This method, however, suffers from several problems. In this paper, we significantly improve upon the previous method by using a fuzzy segmentation algorithm robust to intensity inhomogeneities, and using a deformable surface model specifically designed for capturing convoluted sulci or gyri. We demonstrate the improvement over the previous method both qualitatively and quantitatively, and show the result of its application to six subjects. We also experimentally validate the convergence of the deformable surface initialization algorithm.

title: Design and evaluation of an environment APE for automatic parallelization of programs
abstract: In this paper, we present the design and evaluation of a compiler system, called APE, for automatic parallelization of scientific and engineering applications on distributed memory computers. APE is built on top of SUIF compiler. It extends SUIF with capabilities in parallelizing loops with non-uniform cross-iteration dependencies, and in handling loops that have indirect access patterns. We have evaluated the effectiveness of SUIF with several CFD test codes, and found that SUIF handles uniform loops over dense and regular data structures very well. For non-uniform loops, an innovative and efficient parallelization approach based on convex theory have been proposed and is being implemented. We have also presented a class of scalable algorithms for parallel distribution and redistribution of unstructured data structures during parallelizing irregular loops.

title: sMonitor: A Non-Intrusive Client-Perceived End-to-End Performance Monitor of Secured Internet Services
abstract: End-to-end performance measurement is fundamental to building high-performance Internet services. While many Internet services often operate using HTTP over SSL/TLS, current monitors are limited to plaintext HTTP services. This paper presents sMonitor, a non-intrusive server-side end-to-end performance monitor that can monitor HTTPS services. The monitor passively collects live packet traces from a server site. It then uses a size-based analysis method on HTTP requests to infer characteristics of client accesses and measures client-perceived pageview response time in real time. We designed and implemented a prototype of sMonitor. Preliminary evaluations show measurement error of less than 5%.

title: Energy-aware modeling and scheduling of real-time tasks for dynamic voltage scaling
abstract: Dynamic voltage scaling (DVS) is a promising technique for battery-powered systems to conserve energy consumption. Most existing DVS algorithms assume information about task periodicity or a priori knowledge about the task set to be scheduled. This paper presents an analytical model of general tasks for DVS assuming job timing information is known only after a task release. It models the voltage scaling process as a transfer function-based filter system, which facilitates the design of two efficient scaling algorithms. The first is a time-invariant scaling policy based on a voltage scaling function independent of input jobs over time. It is proved to be a generalization of several existing DVS algorithms. A more energy efficient policy is a time-variant scaling algorithm. The algorithm turns out to be a water-filling process of information theory with a low time complexity. It can not only be applied to scheduling based on worst case execution times, but also to online slack distribution when jobs complete earlier. We further establish two relationships between computation capacity and deadline misses. The relationships make it possible to the provisioning of statistical real-time guarantees

title: Frequency-aware energy optimization for real-time periodic and aperiodic tasks
abstract: Energy efficiency is an important factor in embedded systems design. We consider an embedded system with a dynamic voltage scaling (DVS) capable processor and its system-wide power consumption is dominated by the processor and memory. We present speed assignment polices for a set of periodic/aperiodic tasks that minimize the overall system energy consumption including active and idle power of CPU and other components. A limitation of most DVS-based system-wide energy optimization techniques is that they assume the number of worst-case execution cycles (WCEC) of a task is a constant, independent of CPU frequency. This is not the case when other system components such as memory are taken into account. In this paper, we decompose task execution time into two parts: on-chip inside CPU and off-chip outside the CPU. We propose a frequency-aware system-wide energy minimization approach and establish necessary and sufficient conditions for the optimality. By exploiting properties of the conditions, we derive a bisection algorithm that finds the optimal solution to offline periodict asks in a linear time complexity. We apply a similar analytical approach to online aperiodic tasks scheduling and devise an iterative speed assignment algorithm in the complexity of O(n2). We prove it is optimal among all online algorithms without assumptions about future task releases.

title: Optimal periodic remapping of dynamic bulk synchronous computations
abstract: Dynamic remapping is critical to the performance of bulk synchronous computations that have non-deterministic behaviors because of the need of barrier synchronization between phases. There are two basic issues in remapping: when and how to remap. This paper presents a formal analysis of the issue of when to remap for dynamic computations with a priori known statistical behaviors, with an objective of finding optimal remapping frequencies for a given tolerance of load imbalance. The problem is formulated as two complement sequential stochastic optimization. Since general optimization techniques tend to reveal stationary properties of the workload process, they are not readily applicable to the analysis of the effect of periodic remapping. Instead, this paper develops new analytical approaches to precisely characterize the transient statistical behaviors of the workload process on both homogeneous and heterogeneous machines. Optimal remapping frequencies are derived for various random workload change processes with known or unknown probabilistic distributions. They are shown accurate via simulations.