1,721,127 research outputs found
Length-based attacks for certain group based encryption rewriting systems
No full textHughes, James; Tannenbaum, Allen. (2000). Length-based attacks for certain group based encryption rewriting systems. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/3443
Affine invariant edge maps and active contours
No full textOlver, P.J.; Sapiro, Guillermo; Tannenbaum, Allen. (1995). Affine invariant edge maps and active contours. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/2868
Phase transitions curve evolution, and the control of semiconductor manufacturing processes
No full textBerg, Jordan M.; Yezzi, A.; Tannenbaum, Allen. (1997). Phase transitions curve evolution, and the control of semiconductor manufacturing processes. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/3034
Statistical analysis of RNA backbone
No full textSapiro, Guillermo; Hershkovitz, Eli; Tannenbaum, Allen; Williams, Loren Dean. (2004). Statistical analysis of RNA backbone. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/4017
Statistical analysis of RNA backbone
No full textHershkovitz, Eli; Sapiro, Guillermo; Tannenbaum, Allen; Williams, Loren Dean. (2004). Statistical analysis of RNA backbone. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/4036
Conformal surface parameterization for texture mapping
No full textHaker, Steven; Angenent, Sigurd; Tannenbaum, Allen; Kikinis, Ron; Sapiro, Guillermo; Halle, Michael. (1999). Conformal surface parameterization for texture mapping. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/3298
Wasserstein-based texture analysis in radiomic studies
No full textThe emerging field of radiomics that transforms standard-of-care images to quantifiable scalar statistics endeavors to reveal the information hidden in these macroscopic images. The concept of texture is widely used and essential in many radiomic-based studies. Practice usually reduces spatial multidimensional texture matrices, e.g., gray-level co-occurrence matrices (GLCMs), to summary scalar features. These statistical features have been demonstrated to be strongly correlated and tend to contribute redundant information; and does not account for the spatial information hidden in the multivariate texture matrices. This study proposes a novel pipeline to deal with spatial texture features in radiomic studies. A new set of textural features that preserve the spatial information inherent in GLCMs is proposed and used for classification purposes. The set of the new features uses the Wasserstein metric from optimal mass transport theory (OMT) to quantify the spatial similarity between samples within a given label class. In particular, based on a selected subset of texture GLCMs from the training cohort, we propose new representative spatial texture features, which we incorporate into a supervised image classification pipeline. The pipeline relies on the support vector machine (SVM) algorithm along with Bayesian optimization and the Wasserstein metric. The selection of the best GLCM references is considered for each classification label and is performed during the training phase of the SVM classifier using a Bayesian optimizer. We assume that sample fitness is defined based on closeness (in the sense of the Wasserstein metric) and high correlation (Spearman’s rank sense) with other samples in the same class. Moreover, the newly defined spatial texture features consist of the Wasserstein distance between the optimally selected references and the remaining samples. We assessed the performance of the proposed classification pipeline in diagnosing the coronavirus disease 2019 (COVID-19) from computed tomographic (CT) images. To evaluate the proposed spatial features’ added value, we compared the performance of the proposed classification pipeline with other SVM-based classifiers that account for different texture features, namely: statistical features only, optimized spatial features using Euclidean metric, non-optimized spatial features with Wasserstein metric. The proposed technique, which accounts for the optimized spatial texture feature with Wasserstein metric, shows great potential in classifying new COVID CT images that the algorithm has not seen in the training step. The MATLAB code of the proposed classification pipeline is made available. It can be used to find the best reference samples in other data cohorts, which can then be employed to build different prediction models.<br/
Robust Transport over Networks
Full text linkWe consider transportation over a strongly connected, directed graph.
The scheduling amounts to selecting transition probabilities for a discrete-time Markov evolution which is designed to be consistent with initial and final marginal constraints on mass transport. We address the situation where initially the mass is concentrated on certain nodes and needs to be transported over a certain time period to another set of nodes, possibly disjoint from the first. The evolution is selected to be closest to a {\em prior} measure on paths in the relative entropy sense--such a construction is known as a Schroedinger bridge between the two given marginals. It may be viewed as an atypical stochastic control problem where the control consists in suitably modifying the prior transition mechanism. The prior can be chosen to incorporate constraints and costs for traversing specific edges of the graph, but it can also be selected to allocate equal probability to all paths of equal length connecting any two nodes (i.e., a uniform distribution on paths). This latter choice for prior transitions relies on the so-called Ruelle-Bowen random walker and gives rise to scheduling that tends to utilize all paths as uniformly as the topology allows. Thus, this Ruelle-Bowen law () taken as prior, leads to a transportation plan that tends to lessen congestion and ensures a level of robustness. We also show that the distribution on paths, which attains the maximum entropy rate for the random walker given by the topological entropy, can itself be obtained as the time-homogeneous solution of a maximum entropy problem for measures on paths (also a Schr\"{o}dinger bridge problem, albeit with prior that is not a probability measure). Finally we show that the paradigm of Schr\"odinger bridges as a mechanism for scheduling transport on networks can be adapted to graphs that are not strongly connected, as well as to weighted graphs. In the latter case, our approach may be used to design a transportation plan which effectively compromises between robustness and other criteria such as cost. Indeed, we explicitly provide a robust transportation plan which assigns {\em maximum probability} to {\em minimum cost paths} and therefore compares favorably with Optimal Mass Transportation strategies
A method for denoising textured surfaces
No full textIn this note, we present a simple method to denoise triangulated and implicit surfaces in a manner which preserves the 3D shape texture. The technique is based upon the synthesis of partial differential equations (PDE's), implicit surfaces, and Wiener filtering. The basic idea is to apply a computationally efficient local Wiener filter to an implicit representation of the surface. Such a representation can be directly given as the algorithm input or explicitly obtained via partial differential equation based implicitation techniques applied to the triangulated data. The proposed method has a computational complexity O(N log N).Betelu, Santiago; Tannenbaum, Allen; Sapiro, Guillermo. (2001). A method for denoising textured surfaces. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/3594
Optical Flow: A Curve Evolution Approach
No full text©1996 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.A novel approach for the computation of optical flow based on an L1 type minimization is presented. It is shown that the approach has inherent advantages since it does not smooth the flow-velocity across the edges and hence preserves edge information. A numerical approach based on computation of evolving curves is proposed for computing the optical flow field. Computations are carried out on a number of real image sequences in order to illustrate the theory as well as the numerical approach.Kumar, Arun; Tannenbaum, Allen; Balas, Gary J.. (1996). Optical Flow: A Curve Evolution Approach. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/37271
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