30981403
An Optimization Problem in Virtual Endoscopy
This paper studies a graph optimization problem occurring in virtual endoscopy, which concerns finding the central path of a colon model created from helical computed tomography (CT) image data. The central path is an essential aid for navigating through complex anatomy such as colon. Recently, Ge et al. (1998) devised an efficient method for finding the central path of a colon. The method first generates colon data from a helical CT data volume by image segmentation. It then generates a 3D skeleton of the colon. In the ideal situation, namely, if the skeleton does not contain branches, the skeleton will be the desired central path. However, almost always the skeleton contains extra branches caused by holes in the colon model, which are artifacts produced during image segmentation. To remove false branches, Ge et al. (1998) formulated a graph optimization problem for obtaining the central path. This paper presents a refined formulation and justifies that the solution of the refined optimization problem represents the accurate central path of a colon. We then provide a fast algorithm for solving the problem.
7902129
Efficient Minimization of Numerical Summation Errors
Given a multiset X={x 1,... x n } of real numbers, the floating-point set summation (FPS) problem asks for S n = x 1+...+ x n , and the floating point prefix set summation problem (FPPS) asks for S k =x 1+...+x k for all k = 1, ... n. Let E k * denote the minimum worst-case error over all possible orderings of evaluating S k . We prove that if X has both positive and negative numbers, it is NP-hard to compute S n with the worst-case error equal to E n * . We then give the first known polynomial-time approximation algorithm for computing Sn that has a provably small error for arbitrary X. Our algorithm incurs a worstcase error at most 2([log(n−1)]+1) E n * . After X is sorted, it runs in O(n) time, yielding an O(n 2)-time approximation algorithm for computing S k for all k = 1, ..., n such that the worst-case error for each S k is less than 2(⌈ log(k−1)⌋+ 1)E k * .
38052408
Towards a practical, secure, and very large scale online election
We propose a practical and secure electronic voting protocol for large-scale online elections. Our protocol satisfies a large set of important criteria that has never been put together in a single protocol before. Among all electronic voting schemes in the literature, Sensus, a security-conscious electronic voting protocol proposed by Cranor and Cytron (1997), satisfies most of our criteria. Sensus has been implemented and used in mock elections. However, Sensus suffers from several major drawbacks. For instance, we show that even if all voters follow the Sensus protocol honestly, some voters' votes may still be replaced with different votes without being detected. Our protocol overcomes these drawbacks.
206565969
Immunity of Complete Problems
Abstract Two necessary and sufficient conditions for all E -complete sets to contain dense P subsets are shown. We then prove that every ≤ p m -hard for E set and its complement contain dense E ∩UP subsets. As a corollary, every NE -complete set and its complement contain dense E ∩UP subsets.
15313247
Performance of a MAC protocol for WDM networks with on-line scheduling
This paper investigates the performance of media access protocols based on on-line scheduling for an optically connected star-coupled system with wavelength division multiple access channels. Hybrid access protocols combining reservation and pre-allocation of receiver channels have been previously proposed. Transmission consisted of a reservation phase followed by a data phase. Each node was allowed to place a reservation for one WDM channel. This protocol is extended in this paper by allowing reservations on multiple channels and using simple on-line scheduling algorithms. Existing scheduling algorithms (all are off-line) for similar reservation problems tend to have high implementation and computational complexity. We require that scheduling algorithms be real-time and amenable to hardware implementation. The performance of the extended protocol incorporating two simple on-line scheduling algorithms is presented in this paper. We show that the slight increased computational overhead with scheduling is justified by reduced packet latency and higher utilization, especially for client-server traffic.
32837558
On average P vs. average NP
Structures of polynomial-time computable distributions and polynomial-time many-one reductions on randomized decision problems are investigated. The scope is widened from the most studied class DNP (distributional-NP) to the class ANP (average-NP), which consists of randomized decision problems accepted by nondeterministic Turing machines in average polynomial time. Results indicate that the structures of average-case complexity classes are very different from their counterparts in worst-case complexity due to the complex structures of probability distributions and distribution functions.<<ETX>>
1265628
Image Categorization by Learning and Reasoning with Regions
Designing computer programs to automatically categorize images using low-level features is a challenging research topic in computer vision. In this paper, we present a new learning technique, which extends Multiple-Instance Learning (MIL), and its application to the problem of region-based image categorization. Images are viewed as bags, each of which contains a number of instances corresponding to regions obtained from image segmentation. The standard MIL problem assumes that a bag is labeled positive if at least one of its instances is positive; otherwise, the bag is negative. In the proposed MIL framework, DD-SVM, a bag label is determined by some number of instances satisfying various properties. DD-SVM first learns a collection of instance prototypes according to a Diverse Density (DD) function. Each instance prototype represents a class of instances that is more likely to appear in bags with the specific label than in the other bags. A nonlinear mapping is then defined using the instance prototypes and maps every bag to a point in a new feature space, named the bag feature space. Finally, standard support vector machines are trained in the bag feature space. We provide experimental results on an image categorization problem and a drug activity prediction problem.
2016479
Shape-Based Curve Growing Model and Adaptive Regularization for Pulmonary Fissure Segmentation in CT
This paper presents a shape-based curve-growing algorithm for object recognition in the field of medical imaging. The proposed curve growing process, modeled by a Bayesian network, is influenced by both image data and prior knowledge of the shape of the curve. A maximum a posteriori (MAP) solution is derived using an energy-minimizing mechanism. It is implemented in an adaptive regularization framework that balances the influence of image data and shape prior in estimating the curve, and reflects the causal dependencies in the Bayesian network. The method effectively alleviates over-smoothing, an effect that can occur with other regularization methods. Moreover, the proposed framework also addresses initialization and local minima problems. Robustness and performance of the proposed method are demonstrated by segmentation of pulmonary fissures in computed tomography (CT) images.
2749844
Image and Video Segmentation by Anisotropic Kernel Mean Shift
Mean shift is a nonparametric estimator of density which has been applied to image and video segmentation. Traditional mean shift based segmentation uses a radially symmetric kernel to estimate local density, which is not optimal in view of the often structured nature of image and more particularly video data. In this paper we present an anisotropic kernel mean shift in which the shape, scale, and orientation of the kernels adapt to the local structure of the image or video. We decompose the anisotropic kernel to provide handles for modifying the segmentation based on simple heuristics. Experimental results show that the anisotropic kernel mean shift outperforms the original mean shift on image and video segmentation in the following aspects: 1) it gets better results on general images and video in a smoothness sense; 2) the segmented results are more consistent with human visual saliency; 3) the algorithm is robust to initial parameters.
9039594
TinyMotion: camera phone based interaction methods
This paper presents TinyMotion, a pure software approach that detects the movements of cell phones in real time by analyzing image sequences captured by the built-in camera. Typical movements that TinyMotion detects include - horizontal and vertical movements, rotational movements and tilt movements. In contrast to earlier work, TinyMotion does not require additional sensors, special scenes or backgrounds and can run on today's main-stream camera phones without hardware modification. We describe the design and implementation of TinyMotion and analyze the potential interactions that can leverage TinyMotion. Three applications and two games were created to test TinyMotion. Benchmarking results and usability study show that TinyMotion can detect camera movement reliably under most background and illumination conditions.
41575427
Ranking Web News Via Homepage Visual Layout and Cross-Site Voting
Reading news is one of the most popular activities when people surf the internet. As too many news sources provide independent news information and each has its own preference, detecting unbiased important news might be very useful for users to keep up to date with what are happening in the world. In this paper we present a novel method to identify important news in web environment which consists of diversified online news sites. We observe that a piece of important news generally occupies visually significant place in some homepage of a news site and import news event will be reported by many news sites. To explore these two properties, we model the relationship between homepages, news and latent events by a tripartite graph, and present an algorithm to identify important news in this model. Based on this algorithm, we implement a system TOPSTORY to dynamically generate homepages for users to browse important news reports. Our experimental study indicates the effectiveness of proposed approach.