Local Distance Function Research Articles

Rapid and Accurate Registration Method between Intraoperative 2D XA and Preoperative 3D CTA Images for Guidance of Percutaneous Coronary Intervention.

In this paper, we propose a rapid rigid registration method for the fusion visualization of intraoperative 2D X-ray angiogram (XA) and preoperative 3D computed tomography angiography (CTA) images. First, we perform the cardiac cycle alignment of a patient's 2D XA and 3D CTA images obtained from a different apparatus. Subsequently, we perform the initial registration through alignment of the registration space and optimal boundary box. Finally, the two images are registered where the distance between two vascular structures is minimized by using the local distance map, selective distance measure, and optimization of transformation function. To improve the accuracy and robustness of the registration process, the normalized importance value based on the anatomical information of the coronary arteries is utilized. The experimental results showed fast, robust, and accurate registration using 10 cases, each of the left coronary artery (LCA) and right coronary artery (RCA). Our method can be used as a computer-aided technology for percutaneous coronary intervention (PCI). Our method can be applied to the study of other types of vessels.

Voxel-based Haptic Rendering using Adaptive Sampling of a Local Distance Map

Voxel-based Haptic Rendering using Adaptive Sampling of a Local Distance Map

ATLAS: A Geometric Approach to Learning High-Dimensional Stochastic Systems Near Manifolds

When simulating multiscale stochastic differential equations (SDEs) in high-dimensions, separation of timescales, stochastic noise, and high-dimensionality can make simulations prohibitively expensive. The computational cost is dictated by microscale properties and interactions of many variables, while the behavior of interest often occurs at the macroscale level and at large timescales, often characterized by few important, but unknown, degrees of freedom. For many problems bridging the gap between the microscale and macroscale by direct simulation is computationally infeasible. In this work we propose a novel approach to automatically learn a reduced model with an associated fast macroscale simulator. Our unsupervised learning algorithm uses short parallelizable microscale simulations to learn provably accurate macroscale SDE models, which are continuous in space and time. The learning algorithm takes as input the microscale simulator, a local distance function, and a homogenization spatial or temporal ...

Graph-Based Discriminative Learning for Location Recognition

Recognizing the location of a query image by matching it to an image database is an important problem in computer vision, and one for which the representation of the database is a key issue. We explore new ways for exploiting the structure of an image database by representing it as a graph, and show how the rich information embedded in such a graph can improve bag-of-words-based location recognition methods. In particular, starting from a graph based on visual connectivity, we propose a method for selecting a set of overlapping subgraphs and learning a local distance function for each subgraph using discriminative techniques. For a query image, each database image is ranked according to these local distance functions in order to place the image in the right part of the graph. In addition, we propose a probabilistic method for increasing the diversity of these ranked database images, again based on the structure of the image graph. We demonstrate that our methods improve performance over standard bag-of-words methods on several existing location recognition datasets.

An affinity-based new local distance function and similarity measure for kNN algorithm

In this paper, we propose a modified version of the k-nearest neighbor (kNN) algorithm. We first introduce a new affinity function for distance measure between a test point and a training point which is an approach based on local learning. A new similarity function using this affinity function is proposed next for the classification of the test patterns. The widely used convention of k, i.e., k = [√ N] is employed, where N is the number of data used for training purpose. The proposed modified kNN algorithm is applied on fifteen numerical datasets from the UCI machine learning data repository. Both 5-fold and 10-fold cross-validations are used. The average classification accuracy, obtained from our method is found to exceed some well-known clustering algorithms.

An improved specimen generation method for DEM based on local Delaunay tessellation and distance function

SUMMARYDiscretizing a domain of interest into a set of particles for discrete element simulation is the first step to generate a specimen. An improved algorithm, the Seed Expansion Method (SEM), is proposed in this work. A seed is first generated inside a given domain. Then, the domain is filled by the seed expansion based on a local Delaunay tessellation and a distance function. An optional operation, refilling, is suggested to further improve the packing density after the completion of SEM. Polydisperse dense packing can be generated by the proposed method for an arbitrarily shaped domain in both 2D and 3D. A specimen can be obtained that approximately conforms to a specified size distribution and packing density. Multiple subdomains of a domain can be filled by packings with different densities and size distributions. A specimen with higher density can be obtained by comparing it with the existing methods. Mathematically, the features of the method include both simplicity and high efficiency. Copyright © 2011 John Wiley & Sons, Ltd.

Local Distance Functions: A Taxonomy, New Algorithms, and an Evaluation

We present a taxonomy for local distance functions where most existing algorithms can be regarded as approximations of the geodesic distance defined by a metric tensor. We categorize existing algorithms by how, where, and when they estimate the metric tensor. We also extend the taxonomy along each axis. How: We introduce hybrid algorithms that use a combination of techniques to ameliorate overfitting. Where: We present an exact polynomial-time algorithm to integrate the metric tensor along the lines between the test and training points under the assumption that the metric tensor is piecewise constant. When: We propose an interpolation algorithm where the metric tensor is sampled at a number of references points during the offline phase. The reference points are then interpolated during the online classification phase. We also present a comprehensive evaluation on tasks in face recognition, object recognition, and digit recognition.

ChromA: signal-based retention time alignment for chromatography–mass spectrometry data

Summary: We describe ChromA, a web-based alignment tool for chromatography–mass spectrometry data from the metabolomics and proteomics domains. Users can supply their data in open and standardized file formats for retention time alignment using dynamic time warping with different configurable local distance and similarity functions. Additionally, user-defined anchors can be used to constrain and speedup the alignment. A neighborhood around each anchor can be added to increase the flexibility of the constrained alignment. ChromA offers different visualizations of the alignment for easier qualitative interpretation and comparison of the data. For the multiple alignment of more than two data files, the center-star approximation is applied to select a reference among input files to align to.Availability: ChromA is available at http://bibiserv.techfak.uni-bielefeld.de/chroma. Executables and source code under the L-GPL v3 license are provided for download at the same location.Contact: stoye@techfak.uni-bielefeld.deSupplementary information: Supplementary data are available at Bioinformatics online.

Implementation and Validation of the G-equation Model Coupled with Flamelet Libraries for Simulating Premixed Combustion in I.C. Engines

The G-equation model was implemented in the commercial code ANSYS CFX and validated against experimental data in order to successfully simulate turbulent premixed combustion in internal combustion engines. The model is based on the level-set approach. Two transport equations are solved respectively for the G-scalar mean value, representing the local distance function from the time-averaged mean flame front, and its variance, correlated to the turbulent flame brush thickness. The model closure for tracking the flame front is based on an algebraic expression for the turbulent burning velocity. The composition of the reacted mixture is evaluated by coupling the code with flamelet libraries generated with the ANSYS CFX-RIF package by means of a reaction progress variable computed as a function of the G-related quantities. An innovative technique for periodically re-initializing the G-scalar field, in order to enforce geometrical consistency and avoid numerical instabilities, was developed, consisting of a least-square-based interface reconstruction and a minimization of the distance from the discretized flame front. In order to make the code suitable for spark-ignition engine applications the combustion model was coupled with a spark kernel model that simulates the early stages of the ignition process at sub-grid scales. The robustness and accuracy of the level-set approach with moving structured and unstructured meshes were assessed and the parallel performance of the combustion model was optimized in order to deal with large meshes in industrial applications. Validation was carried out by comparing simulations against experimental data for three different test-cases: a steady flame in a slot-burner, a transient spark-ignited premixed combustion in a cylinder with fixed walls and optical access and a spark-ignition research engine with flat head and flat piston. The results show good numerical properties and other attractive features in terms of geometrical description of the flame front and coupling with sub-models for detailed chemistry and spark-ignition. 2009 SAE International.

능동 전방향 거리 측정 시스템을 이용한 이동로봇의 위치 추정

An active omni-directional raging system using an omni-directional vision with structured light has many advantages compared to the conventional ranging systems: robustness against external illumination noise because of the laser structured light and computational efficiency because of one shot image containing <TEX>$360^{\circ}$</TEX> environment information from the omni-directional vision. The omni-directional range data represents a local distance map at a certain position in the workspace. In this paper, we propose a matching algorithm for the local distance map with the given global map database, thereby to localize a mobile robot in the global workspace. Since the global map database consists of line segments representing edges of environment object in general, the matching algorithm is based on relative position and orientation of line segments in the local map and the global map. The effectiveness of the proposed omni-directional ranging system and the matching are verified through experiments.

Spatial point-process statistics: concepts and application to the analysis of lead contamination in urban soil

This article explores the use of spatial point-process analysis as an aid to describe topsoil lead distribution in urban environments. The data used were collected in Glebe, an inner suburb of Sydney. The approach focuses on the locations of punctual events defining a point pattern, which can be statistically described through local intensity estimates and between-point distance functions. F-, G- and K-surfaces of a marked spatial point pattern were described and used to estimate nearest distance functions over a sliding band of quantiles belonging to the marking variable. This provided a continuous view of the point pattern properties as a function of the marking variable. Several random fields were simulated by selecting points from random, clustered or regular point processes and diffusing them. Recognition of the underlying point process using variograms derived from dense sampling was difficult because, structurally, the variograms were very similar. Point-event distance functions were useful complimentary tools that, in most cases, enabled clear recognition of the clustered processes. Spatial sampling quantile point pattern analysis was defined and applied to the Glebe data set. The analysis showed that the highest lead concentrations were strongly clustered. The comparison of this data set with the simulation confidence limits of a Poisson process, a short-radius clustered point process and a geostatistical simulation showed a random process for the third quartile of lead concentrations but strong clustering for the data in the upper quartile. Thus the distribution of topsoil lead concentrations over Glebe may have resulted from several contamination processes, mainly from regular or random processes with large diffusion ranges and short-range clustered processes for the hot spots. Point patterns with the same characteristics as the Glebe experimental pattern could be generated by separate additive geostatistical simulation. Spatial sampling quantile point patterns statistics can, in an easy and accurate way, be used complementarily with geostatistical methods. Copyright © 2005 John Wiley & Sons, Ltd.

Normal Forms of Germs of Contact Sub-Lorentzian Structures on R 3 . Differentiability of the Sub-Lorentzian Distance Function

The aim of this paper is to construct certain normal forms for germs of contract sub-Lorentzian metrics in R3. Using them we show that there are regions in which longest curves are necessarily timelike, and where the local sub-Lorentzian distance function is smooth. By the way, we compute the null conjugate locus of a point.

Quality image metrics for synthetic images based on perceptual color differences

Due to the improvement of image rendering processes, and the increasing importance of quantitative comparisons among synthetic color images, it is essential to define perceptually based metrics which enable to objectively assess the visual quality of digital simulations. In response to this need, this paper proposes a new methodology for the determination of an objective image quality metric, and gives an answer to this problem through three metrics. This methodology is based on the LLAB color space for perception of color in complex images, a modification of the CIELab1976 color space. The first metric proposed is a pixel by pixel metric which introduces a local distance map between two images. The second metric associates, to a pair of images, a global value. Finally, the third metric uses a recursive subdivision of the images to obtain an adaptative distance map, rougher but less expensive to compute than the first method.

Acoustic backing-off as an implementation of missing feature theory

In this paper, we discuss acoustic backing-off as a method to improve automatic speech recognition robustness. Acoustic backing-off aims to achieve the same objective as the marginalization approach of missing feature theory: the detrimental influence of outlier values is effectively removed from the local distance computation in the Viterbi algorithm. The proposed method is based on one of the principles of robust statistical pattern matching: during recognition the local distance function (LDF) is modeled using a mixture of the distribution observed during training and a distribution describing observations not previously seen. In order to assess the effectiveness of the new method, we used artificial distortions of the acoustic vectors in connected digit recognition over telephone lines. We found that acoustic backing-off is capable of restoring recognition performance almost to the level observed for the undisturbed features, even in cases where a conventional LDF completely fails. These results show that recognition robustness can be improved using a marginalization approach, where making the distinction between reliable and corrupted feature values is wired into the recognition process. In addition, the results show that application of acoustic backing-off is not limited to feature representations based on filter bank outputs. Finally, the results indicate that acoustic backing-off is much less effective when local distortions are smeared over all vector elements. Therefore, the acoustic pre-processing steps should be chosen with care, so that the dispersion of distortions over all acoustic vector elements as a result of within-vector feature transformations is minimal.

Acoustic features and a distance measure that reduce the impact of training–test mismatch in ASR

For improved recognition robustness in mismatched training–test conditions, the application of key ideas from missing feature theory and robust statistical pattern recognition in the framework of an otherwise conventional automatic speech recognition (ASR) system were investigated. To this end, both the type of features used to represent the speech signals and the algorithm used to compute the distance measure between an observed feature vector and a previously trained parametric model were studied. Two different types of feature representations were used: a type in which spectrally local distortions are smeared over the entire feature vector and a type in which distortions are only smeared over part of the feature vector. In addition, two different distance measures were investigated, viz., a conventional distance measure and a robust local distance function in the form of acoustic backing-off. The effects on recognition performance were studied for artificially created, band-limited noise and NOISEX noise added to the speech signals. The results for artificial band-limited noise indicate that a partially smearing feature transform is to be preferred over a fully smearing transform. In addition, for artificial, band-limited noise, a robust local distance function is to be preferred over the conventional distance measure as long as the distorted feature values are outliers with respect to the feature distribution observed during training. The experiments with NOISEX noise show that the combination of feature type and distance measure that is optimal for artificial, band-limited noise is also capable of improving recognition robustness for NOISEX noise, provided that it is band-limited.

A characterization for isometries and conformal mappings of pseudo-Riemannian manifolds

Generalizing theorems of Myers-Steenrod and of Hawking, we obtain characterizations for isometries and conformal mappings of pseudo-Riemannian spaces (M, g): Define a local distance function on convex normal neighbourhoods by Φ(p, q) =g(expp−1q, expp−1q). Then every homeomorphismf locally preserving these functions is an isometry. If (M, g) has indefinite signature andf locally preserves distance zero, it is a conformal diffeomorphism.

Expenditure Functions, Local Duality, and Second Order Approximations

This paper provides a complete set of local duality results for a utility maximizing consumer (or single output cost minimizing firm). Given a continuous local expenditure function defined on a compact, convex set of positive prices we establish the existence of continuous local direct, indirect utility and distance functions. This procedure avoids troublesome continuity problems at the boundary of IR N. In addition it is shown that if two utility functions are second order approximations at some point, then their respective expenditure, distance, and indirect utility functions are also second-order approximations to each other at some point. This latter result provides additional impetus for using duality theory and substantial justification for the use of flexible functional forms which can provide second-order differential approximations to any twice continuously differentiable function at a point.

Evaluation of acoustic parameters for monosyllabic word identification

Several recent investigations have hypothesized that syllable-sized segments may be more appropriate units than phoneme-sized segments for use in continuous speech recognition systems. The significant acoustic information in these segments may be represented by a variety of parameters, some obtained by linear predictive techniques [e.g., linear prediction coefficients (LPC), reflection coefficients (RC), and cepstral coefficients (LPCC)], others by spectral techniques [e.g., linear-frequency cepstral coefficients (LFCC) and mel-frequency cepstral coefficients (MFCC)]. This study compared the performance of these parameters in a word identification test. Two male speakers produced 57 sentences in each of two sessions, and 676 tokens of 52 CVC words in a variety of syntactic positions were manually segmented. For each speaker, a symmetric dynamic warping technique was used for time registration of half of the data to form composite templates and for comparisons of the remaining data against the templates. The local distance functions were the Euclidian metric for the RC, LPCC, LFCC, and MFCC, and the Itakura metric for the LPC. The best parameter for word identification was the MFCC (96.5% and 95.0% for each speaker), followed by the LFCC (94.7% and 87.6%), LPCC (91.7% and 86.4%), LPC (85.2% and 84.3%), and RC (83.1% and 77.3%). We suggest that the superior performance of the reel-frequency cepstral coefficients is due to the fact that they better represent the perceptually-relevant aspects of the short-term speech spectrum. [Research supported by NSF Grant MSC-76-81034 and the USAECOM Contract MDA 904-77-C-0157.]