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Jacobi Fiber Surfaces for Bivariate Reeb Space Computation

IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2017年第1期23卷 960-969页

作者： Tierny, Julien Carr, Hamish Univ Paris 06 UPMC Sorbonne Univ CNRSLIP6 UMR 7606 F-75252 Paris 05 France Univ Leeds Leeds LS2 9JT W Yorkshire England

This paper presents an efficient algorithm for the computation of the Reeb space of an input bivariate piecewise linear scalar function f defined on a tetrahedral mesh. By extending and generalizing algorithmic concepts from the univariate case to the bivariate one, we report the first practical, output-sensitive algorithm for the exact computation of such a Reeb space. The algorithm starts by identifying the Jacobi set of f, the bivariate analogs of critical points in the univariate case. Next, the Reeb space is computed by segmenting the input mesh along the new notion of Jacobi Fiber Surfaces, the bivariate analog of critical contours in the univariate case. We additionally present a simplification heuristic that enables the progressive coarsening of the Reeb space. Our algorithm is simple to implement and most of its computations can be trivially parallelized. We report performance numbers demonstrating orders of magnitude speedups over previous approaches, enabling for the first time the tractable computation of bivariate Reeb spaces in practice. Moreover, unlike range-based quantization approaches (such as the Joint Contour Net), our algorithm is parameter-free. We demonstrate the utility of our approach by using the Reeb space as a semi-automatic segmentation tool for bivariate data. In particular, we introduce continuous scatterplot peeling, a technique which enables the reduction of the cluttering in the continuous scatterplot, by interactively selecting the features of the Reeb space to project. We provide a VTK-based C++ implementation of our algorithm that can be used for reproduction purposes or for the development of new Reeb space based visualization techniques.

关键词： Topological data analysis multivariate data data segmentation

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Visually Guided Acquisition of Contact Dynamics and Case Study in data-Driven Haptic Texture Modeling

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IEEE TRANSACTIONS ON HAPTICS 2020年第3期13卷 611-627页

作者： Abdulali, Arsen Atadjanov, Ibragim R. Jeon, Seokhee Kyung Hee Univ Dept Comp Sci & Engn Yongin 17104 South Korea

data-driven modeling of human hand contact dynamics starts with a tedious process of data collection. The data of contact dynamics consist of an input describing an applied action and response stimuli from the environment. The quality and stability of the model mainly depend on how well data points cover the model space. Thus, in order to build a reliable data-driven model, a user usually collects data dozens of times. In this article, we aim to build an interactive system that assists a user in data collection. We develop an online segmentation framework that partitions a multivariate streaming signal. Real-time segmentation allows for tracking the process of how the model space is being populated. We applied the proposed framework for a haptic texture modeling use-case. In order to guide a user in data collection, we designed a user interface mapping applied input to alternative visual modalities based on the theory of direct perception. A combination of the segmentation framework and user interface implements a human-in-loop system, where the user interface assigns the target combination of input variables and the user tries to acquire them. Experimental results show that the proposed data collection schema considerably increases the approximation quality of the model, whereas the proposed user interface considerably reduces mental workload experienced during data collection.

关键词： data collection data models Haptic interfaces Solid modeling Visualization User interfaces Robots data-driven modeling data segmentation visual guidance data collection interface

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Dynamic coding of visual information

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 1997年第3期7卷 489-500页

作者： Reusens, E Ebrahimi, T Kunt, M SWISS FED INST TECHNOL SIGNAL PROC LAB CH-1015 LAUSANNE SWITZERLAND

This paper introduces a novel approach to visual data compression. The approach, named dynamic coding, consists of an effective competition between several representation models used for describing data portions, The image data is represented as the union of several regions each approximated by a representation model locally appropriate, The dynamic coding concept leads to attractive features such as genericness, flexibility, and openness and is therefore particularly suited to a multimedia environment in which many types of applications are involved. Dynamic coding is a general proposal to visual data compression and many variations on the same theme may be designed. They differ by the particular procedure by which the data is segmented into objects and the local representation model selected, As an illustrative example, a video compression scheme based on the principles of dynamic coding is presented, This compression algorithm performs a joint optimization of the segmentation (restricted to a so-called generalized quadtree partition) together with the representation models associated with each data segment. Four representation models are competing namely, fractal, motion compensation, text and graphics, and background modes, Optimality is defined with respect to a rate-distortion tradeoff and the optimization procedure leads to a multicriterion segmentation.

关键词： data compression fractals image representation image segmentation image sequences motion compensation multimedia communication optimisation quadtrees rate distortion theory video coding background modes compression algorithm data segmentation dynamic cod Video coding Rate distortion theory quaternary tree Compression algorithms image representation dynamic encoding image sequences data compression image segmentation Fractals data partitioning Motion compensation background mode visual information

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An Analysis of segmentation Approaches and Window Sizes in Wearable-Based Critical Fall Detection Systems With Machine Learning Models

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IEEE SENSORS JOURNAL 2020年第6期20卷 3303-3313页

作者： Liu, Kai-Chun Hsieh, Chia-Yeh Huang, Hsiang-Yun Hsu, Steen Jun-Ping Chan, Chia-Tai Natl Yang Ming Univ Dept Biomed Engn Taipei 11221 Taiwan Minghsin Univ Sci & Technol Dept Informat Management Hsinchu 30401 Taiwan

Falls are the major risks and threats among elderly population. Various studies have developed automatic critical fall detection systems for emergency alarms and medical services. Window sizes and segmentation approaches would affect the system performance in terms of power consumption, computational speed and reliability that are essential considerations in the real-world implementation. Intuitively, employing shorter window sizes and simpler segmentation approaches leads to a faster detection speed and better power efficiency. However, few works explore the effects of windowing methods on detection performance, especially for machine learning models. This paper investigates the impact of segmentation approaches and window sizes in wearable-based critical fall detection systems with machine learning models. Two generally used segmentation approaches, including sliding windows and impact-defined windows, are explored with a range of window sizes from 0.5 s to 5.0 s and four machine learning models: Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), Classification and Regression Tree (CART) and Naive Bayes (NB). The results show that sliding windows are more sensitive to the changes of window sizes compared to impact-defined windows. However, the differences between the best performance of both segmentation approaches are within 1% (99.66% in sliding windows and 99.78% in impact-defined windows). For systems with SVM and k-NN models, using 0.5 s window sizes in sliding windows and combinations of 1.5 s backward and forward windows in impact-defined windows are sufficient to achieve at least 94% accuracy and 98% accuracy.

关键词： Microsoft Windows Support vector machines Sensor systems Machine learning Hidden Markov models Legged locomotion Keywords Fall detection data segmentation sliding windows impact-defined windows window sizes machine learning wearable sensors

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High accuracy human activity recognition using machine learning and wearable devices' raw signals

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JOURNAL OF INFORMATION AND TELECOMMUNICATION 2022年第3期6卷 237-253页

作者： Papaleonidas, Antonios Psathas, Anastasios Panagiotis Iliadis, Lazaros Democritus Univ Thrace Sch Engn Dept Civil Engn Lab Math & Informat Univ Campus Xanthi 67100 Greece

Human activity recognition (HAR) is vital in a wide range of real-life applications such as health monitoring of olderly people, abnormal behaviour detection and smart home management. HAR systems can employ smart human-computer interfaces and be parts of active, intelligent surveillance systems. The increasing use of high-tech mobile and wearable devices, such as smart phones, smart watches and smart bands, can be the key elements in building high accuracy models, as they can provide a tremendous number of signals. This research aims to develop and test a machine learning (ML) model, which can successfully recognize a performed activity using raw signals obtained by wearable devices. Photoplethysmography - Daily Life Activities (PPG-DaLiA) dataset contains data related to 15 individuals wearing physiological and motion sensors. PPG-DaLiA was used as an input to a custom data segmentation model to obtain the respective training and testing dataset. Overall, 23 ML well-established models were employed. The weighted and the fine k-nearest neighbours, the fine Gaussian support vector machines and the bagged trees were the algorithms that achieved the best performance with a very high accuracy level.

关键词： Human activity recognition machine learning multi-class classification wearables data segmentation

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Advantages of Nonlinear over segmentation Analyses in Path Models

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INTERNATIONAL JOURNAL OF E-COLLABORATION 2016年第4期12卷 1-6页

作者： Kock, Ned Texas A&M Int Univ Div Int Business & Technol Studies Laredo TX 78041 USA

The recent availability of software tools for nonlinear path analyses, such as WarpPLS, enables e-collaboration researchers to take nonlinearity into consideration when estimating coefficients of association among linked variables. Nonlinear path analyses can be applied to models with or without latent variables, and provide advantages over data segmentation analyses, including those employing finite mixture segmentation techniques ( a. k. a. FIMIX). The latter assume that data can be successfully segmented into subsamples, which are then analyzed with linear algorithms. Nonlinear analyses employing WarpPLS also allow for the identification of linear segments mirroring underlying nonlinear relationships, but without the need to generate subsamples. The author demonstrates the advantages of nonlinear over data segmentation analyses.

关键词： data segmentation E-collaboration Monte Carlo Simulation Nonlinear Analysis Path Modeling

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Co-word analysis using the Chinese character set

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JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY 2008年第9期59卷 1528-1530页

作者： Leydesdorff, Loet Zhou, Ping Univ Amsterdam Amsterdam Sch Commun Res NL-1012 CX Amsterdam Netherlands Katholieke Univ Leuven B-3000 Louvain Belgium Inst Sci & Tech Informat China Beijing 100038 Peoples R China

Until recently, Chinese texts could not be studied using co-word analysis because the words are not separated by spaces in Chinese (and Japanese). A word can be composed of one or more characters. The online availability of programs that separate Chinese texts makes it possible to analyze them using semantic maps. Chinese characters contain not only information but also meaning. This may enhance the readability of semantic maps. In this study, we analyze 58 words which occur 10 or more times in the 1,652 journal titles of the China Scientific and Technical Papers and Citations database. The word-occurrence matrix is visualized and factor-analyzed.

关键词： words data segmentation semantic networks

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HIL-Tree: A hierarchical structure for guiding search into test and measurement data archives

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data MINING AND KNOWLEDGE DISCOVERY 2005年第3期10卷 229-250页

作者： Artail, HA Amer Univ Beirut Dept Elect & Comp Engn Beirut 11072020 Lebanon

This paper describes a novel algorithm that uses discontinuity detection to discover index vectors in test and measurement data archives containing multidimensional data. The index vectors are generated from individual data series in the archive and hold location information about jumps and changes in trends (discontinuities). They are related in a hierarchical manner to form a tree-like structure based on the alignment of the location information across the vectors. We call such trees Hierarchical Index Locations trees (HIL-trees), which are useful in guiding navigation into the raw data and in speeding up the process of retrieving data subsets based on given criteria. To demonstrate the practical value of the algorithm, we present a case study through which the algorithm is applied to real automotive emission test data archives, and show how it works. We also compare the HIL-tree to the well-known R-tree index structure and show how HIL-trees are advantageous in many aspects.

关键词： index trees multilevel queries discontinuity detection data segmentation

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Driver profiling: The pathway to deeper personalization

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JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES 2022年第10期34卷 9088-9101页

作者： Zahraoui, Yassine Errajraji, Khalid Ramah, Salaheddine Bouhoute, Afaf Berrada, Ismail Sidi Mohamed Ben Abdellah Univ Fac Sci Fes Morocco Mohammed VI Polytech Univ Benguerir Morocco

Defining driver behavior profiles is an essential step to create unique and tailored driving experience. Depending on the driver information used, two types of profiles can be distinguished. The driver's preferences in terms of car settings constitute the static profile while his driving behavior constitutes the dynamic profile. In this paper, instead of profiling the driving behavior as one of classical behavior classes (e.g. aggressive, normal, and drowsy), we propose a more personalized approach, where the dynamic profile is represented as a graph, automatically built using the driver's data. The graph states are learned from the driver data using clustering techniques, namely map matching clustering for GPS data and kmeans clustering for kinematics data. To avoid duplicate and overlapping clusters, rules for cluster harmonization are defined. The feasibility of our approach was demonstrated, by applying our framework to two driving datasets, UAH-Driveset and Hcilab. The quality of clustering is later validated using the leave one out cross validation method and two newly defined metrics, namely FDR and RoC, measuring the rate of undiscovered behaviors and the stability of the clusters, respectively. The obtained results show that the proposed approach has a low FDR (an average of 8% for Hcilab dataset and 6% for UAH-driverset).

关键词： Driver profile data segmentation Driving behavior Map matching k-means clustering

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Spatially optimised retrieval of 3D point cloud data from a geospatial database for road median extraction

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JOURNAL OF SPATIAL SCIENCE 2022年第1期67卷 3-20页

作者： Kumar, Pankaj Lewis, Paul Cahalane, Conor Peters, Stefan Univ South Australia Sch Nat & Built Environm Mawson Lakes Australia Maynooth Univ Natl Ctr Geocomp Maynooth Kildare Ireland Maynooth Univ Dept Geog Maynooth Kildare Ireland

We present the GLIMPSE system that provides a framework for storage, management, accessibility and integration of 3D LiDAR data acquired from multiple platforms. We detail a point cloud retrieval approach, which provides spatially optimised access to point cloud data from the system for a particular geographic area based on user specifications. We tested our point cloud retrieval approach to facilitate the extraction of road medians from large volumes of ALS data stored in the GLIMPSE system. The integrated use of a geospatial database, the GLIMPSE system and the point cloud retrieval approach improved the efficiency of road median extraction.

关键词： Geospatial database spatial hierarchy data segmentation spatial optimisation point cloud retrieval road median extraction

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