2599785
On measuring the change in size of pulmonary nodules
The pulmonary nodule is the most common manifestation of lung cancer, the most deadly of all cancers. Most small pulmonary nodules are benign, however, and currently the growth rate of the nodule provides for one of the most accurate noninvasive methods of determining malignancy. In this paper, we present methods for measuring the change in nodule size from two computed tomography image scans recorded at different times; from this size change the growth rate may be established. The impact of partial voxels for small nodules is evaluated and isotropic resampling is shown to improve measurement accuracy. Methods for nodule location and sizing, pleural segmentation, adaptive thresholding, image registration, and knowledge-based shape matching are presented. The latter three techniques provide for a significant improvement in volume change measurement accuracy by considering both image scans simultaneously. Improvements in segmentation are evaluated by measuring volume changes in benign or slow growing nodules. In the analysis of 50 nodules, the variance in percent volume change was reduced from 11.54% to 9.35% (p=0.03) through the use of registration, adaptive thresholding, and knowledge-based shape matching.
2717049
Supervised Learning of Semantic Classes for Image Annotation and Retrieval
A probabilistic formulation for semantic image annotation and retrieval is proposed. Annotation and retrieval are posed as classification problems where each class is defined as the group of database images labeled with a common semantic label. It is shown that, by establishing this one-to-one correspondence between semantic labels and semantic classes, a minimum probability of error annotation and retrieval are feasible with algorithms that are 1) conceptually simple, 2) computationally efficient, and 3) do not require prior semantic segmentation of training images. In particular, images are represented as bags of localized feature vectors, a mixture density estimated for each image, and the mixtures associated with all images annotated with a common semantic label pooled into a density estimate for the corresponding semantic class. This pooling is justified by a multiple instance learning argument and performed efficiently with a hierarchical extension of expectation-maximization. The benefits of the supervised formulation over the more complex, and currently popular, joint modeling of semantic label and visual feature distributions are illustrated through theoretical arguments and extensive experiments. The supervised formulation is shown to achieve higher accuracy than various previously published methods at a fraction of their computational cost. Finally, the proposed method is shown to be fairly robust to parameter tuning
5663
Mixtures of dynamic textures
A dynamic texture is a linear dynamical system used to model a single video as a sample from a spatio-temporal stochastic process. In this work, we introduce the mixture of dynamic textures, which models a collection of videos consisting of different visual processes as samples from a set of dynamic textures. We derive the EM algorithm for learning a mixture of dynamic textures, and relate the learning algorithm and the dynamic texture mixture model to previous works. Finally, we demonstrate the applicability of the proposed model to problems that have traditionally been challenging for computer vision.
17099314
A graph-theoretical analysis of multicast authentication
Message authentication is considered as a serious bottleneck to multicast security, particular for stream-type of traffic. The technique of hash chaining/signature amortization has been proposed in many schemes for stream authentication, with or without multicast settings. However, none of them is optimal. They either have a large packet overhead or are not robust to packet loss. Some even have a large receiver delay or require a large receiver buffer size. These schemes are constructed by trial-and-error methods. There lack tools to evaluate and compare their performances. There is no systematic way to construct these schemes either In this paper, we introduce the notion of dependence-graphs which links these hash-chained authentication schemes to the well-known graph theory, and provides an effective analytical tool. Many important metrics of a hash-chained authentication scheme can be readily and easily determined from its dependence-graph. As well, a dependence-graph demonstrates design tradeoff and provides insights into optimizing hash-chained schemes.
252376
A Note on Coarse Grained Parallel Integer Sorting
We observe that for n=p p, which is usually the case in practice, there exists a very simple, deterministic, optimal coarse grained parallel integer sorting algorithm with 24 communication rounds (6 n p -relations and 18p-relations), O(n=p) memory per processor and O(n=p) local computation. Experimental data indicates that the algorithm has very good performance in practice.
1023852
Coarse grained parallel maximum matching in convex bipartite graphs
We present a coarse grained parallel algorithm for computing a maximum matching in a convex bipartite graph G=(A,B,E). For p processors with N/p memory per processor, N=|A|+|B|,N/p/spl ges/p, the algorithm requires O(log p) communication rounds and O(T/sub sequ/(n/p,m/p)+n/p log p) local computation, where n=|A|,m=|B| and T/sub sequ/(n,m) is the sequential time complexity for the problem. For the BSP model, this implies O(log p) supersteps with O(gN+gn/p log p) communication cost and O(T/sub sequ/(n/p,m/p)+n/p log p) local computation.
2113781
Learning and identifying haptic icons under workload
This work addresses the use of vibrotactile haptic feedback to transmit background information with variable intrusiveness, when recipients are engrossed in a primary visual and/or auditory task. We describe two studies designed to (a) perceptually optimize a set of vibrotactile "icons" and (b) evaluate users' ability to identify them in the presence of varying degrees of workload. Seven icons learned in approximately 3 minutes were each typically identified within 2.5 s and at 95% accuracy in the absence of workload.