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Computer simulation based parameter selection for resistance exercise

Computer simulation based parameter selection for resistance...

引用

24th IASTED International Conference on Modelling and Simulation, MS 2013

作者： Arandjelović, Ognjen Centre for Pattern Recognition and Data Analytics Deakin University Geelong 3216 VIC Australia

ISBN: (纸本)9780889869561

In contrast to most scientific disciplines, sports science research has been characterized by comparatively little effort investment in the development of relevant phenomenologi-cal models. Scarcer yet is the application of said models in practice. We present a framework which allows resistance training practitioners to employ a recently proposed neu-romuscular model in actual training program design. The first novelty concerns the monitoring aspect of coaching. A method for extracting training performance characteristics from loosely constrained video sequences, effortlessly and with minimal human input, using computer vision is described. The extracted data is subsequently used to fit the underlying neuromuscular model. This is achieved by solving an inverse dynamics problem corresponding to a particular exercise. Lastly, a computer simulation of hypothetical training bouts, using athlete-specific capability parameters, is used to predict the effected adaptation and changes in performance. The software described here allows the practitioner to manipulate hypothetical training parameters and immediately see their effect on predicted adaptation for a specific athlete. Thus, this work presents a holistic view of the monitoring-assessment-adjustment loop.

关键词： Sports

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Extraction of Latent patterns and Contexts from Social Honest Signals Using Hierarchical Dirichlet Processes

Extraction of Latent Patterns and Contexts from Social Hones...

引用

IEEE International Conference on Pervasive Computing and Communications

作者： Thuong Nguyen Dinh Phung Sunil Gupta S. Venkatesh Centre for Pattern Recognition and Data Analytics Deakin University

ISBN: (纸本)9781467345736

A fundamental task in pervasive computing is reliable acquisition of contexts from sensor data. This is crucial to the operation of smart pervasive systems and services so that they might behave efficiently and appropriately upon a given context. Simple forms of context can often be extracted directly from raw data. Equally important, or more, is the hidden context and pattern buried inside the data, which is more challenging to discover. Most of existing approaches borrow methods and techniques from machine learning, dominantly employ parametric unsupervised learning and clustering techniques. Being parametric, a severe drawback of these methods is the requirement to specify the number of latent patterns in advance. In this paper, we explore the use of Bayesian nonparametric methods, a recent data modelling framework in machine learning, to infer latent patterns from sensor data acquired in a pervasive setting. Under this formalism, nonparametric prior distributions are used for data generative process, and thus, they allow the number of latent patterns to be learned automatically and grow with the data - as more data comes in, the model complexity can grow to explain new and unseen patterns. In particular, we make use of the hierarchical Dirichlet processes (HDP) to infer atomic activities and interaction patterns from honest signals collected from sociometric badges. We show how data from these sensors can be represented and learned with HDP. We illustrate insights into atomic patterns learned by the model and use them to achieve high-performance clustering. We also demonstrate the framework on the popular Reality Mining dataset, illustrating the ability of the model to automatically infer typical social groups in this dataset. Finally, our framework is generic and applicable to a much wider range of problems in pervasive computing where one needs to infer high-level, latent patterns and contexts from sensor data.

关键词： Sensor data Ubiquitous computing HIGH DELTA PRESSURE Frameworks AUTOMATIC

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Discriminative k-Means Clustering

Discriminative k-Means Clustering

引用

International Joint Conference on Neural Networks

作者： Ognjen Arandjelovi? Centre for Pattern Recognition and Data Analytics at Deakin University.

ISBN: (纸本)9781467361279

The k-means algorithm is a partitional clustering method. Over 60 years old, it has been successfully used for a variety of problems. The popularity of k-means is in large part a consequence of its simplicity and efficiency. In this paper we are inspired by these appealing properties of k-means in the development of a clustering algorithm which accepts the notion of "positively" and "negatively" labelled data. The goal is to discover the cluster structure of both positive and negative data in a manner which allows for the discrimination between the two sets. The usefulness of this idea is demonstrated practically on the problem of face recognition, where the task of learning the scope of a person's appearance should be done in a manner which allows this face to be differentiated from others.

关键词： variety problems k-means

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Interactive browsing system for anomaly video surveillance

Interactive browsing system for anomaly video surveillance

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Intelligent Sensors, Sensor Networks and Information Processing Conference (ISSNIP)

作者： Tien-Vu Nguyen Dinh Phung Sunil Gupta Svetha Venkatesh Centre for Pattern Recognition and Data Analytics (PRaDA) Deakin University Australia

Existing anomaly detection methods in video surveillance exhibit lack of congruence between rare events detected by algorithms and what is considered anomalous by users. This paper introduces a novel browsing model to address this issue, allowing users to interactively examine rare events in an intuitive manner. Introducing a novel way to compute rare motion patterns, we estimate latent factors of foreground motion patterns through Bayesian Nonparametric Factor analysis. Each factor corresponds to a typical motion pattern. A rarity score for each factor is computed, and ordered in decreasing order of rarity, permitting users to browse events using any proportion of rare factors. Rare events correspond to frames that contain the rare factors chosen. We present the user with an interface to inspect events that incorporate these rarest factors in a spatial-temporal manner. We demonstrate the system on a public video data set, showing key aspects of the browsing paradigm.

关键词： Robustness Principal component analysis Feature extraction Matrix decomposition Cameras data models Hidden Markov models

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Making the most of the self-quotient image in face recognition

Making the most of the self-quotient image in face recogniti...

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International Conference on Automatic Face and Gesture recognition

作者： Ognjen Arandjelović Centre for Pattern Recognition and Data Analytics Deakin University Geelong VIC Australia

ISBN: (纸本)9781467355452

The self-quotient image is a biologically inspired representation which has been proposed as an illumination invariant feature for automatic face recognition. Owing to the lack of strong domain specific assumptions underlying this representation, it can be readily extracted from raw images irrespective of the persons's pose, facial expression etc. What makes the self-quotient image additionally attractive is that it can be computed quickly and in a closed form using simple low-level image operations. However, it is generally accepted that the self-quotient is insufficiently robust to large illumination changes which is why it is mainly used in applications in which low precision is an acceptable compromise for high recall (e.g. retrieval systems). Yet, in this paper we demonstrate that the performance of this representation in challenging illuminations has been greatly underestimated. We show that its error rate can be reduced by over an order of magnitude, without any changes to the representation itself. Rather, we focus on the manner in which the dissimilarity between two self-quotient images is computed. By modelling the dominant sources of noise affecting the representation, we propose and evaluate a series of different dissimilarity measures, the best of which reduces the initial error rate of 63.0% down to only 5.7% on the notoriously challenging YaleB data set.

关键词： Noise Lighting Face Face recognition Robustness Quantization (signal) Error analysis

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Exploiting side information in distance dependent Chinese restaurant processes for data clustering

Exploiting side information in distance dependent Chinese re...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Cheng Li Dinh Phung Santu Rana Svetha Venkatesh Centre for Pattern Recognition and Data Analytics (PRaDA) Deakin University Geelong Australia

ISBN: (纸本)9781479900145

Multimedia contents often possess weakly annotated data such as tags, links and interactions. The weakly annotated data is called side information. It is the auxiliary information of data and provides hints for exploring the link structure of data. Most clustering algorithms utilize pure data for clustering. A model that combines pure data and side information, such as images and tags, documents and keywords, can perform better at understanding the underlying structure of data. We demonstrate how to incorporate different types of side information into a recently proposed Bayesian nonparametric model, the distance dependent Chinese restaurant process (DD-CRP). Our algorithm embeds the affinity of this information into the decay function of the DD-CRP when side information is in the form of subsets of discrete labels. It is flexible to measure distance based on arbitrary side information instead of only the spatial layout or time stamp of observations. At the same time, for noisy and incomplete side information, we set the decay function so that the DD-CRP reduces to the traditional Chinese restaurant process, thus not inducing side effects of noisy and incomplete side information. Experimental evaluations on two real-world datasets NUS WIDE and 20 Newsgroups show exploiting side information in DD-CRP significantly improves the clustering performance.

关键词： Clustering algorithms Noise measurement Mutual information Bayes methods Multimedia communication Time measurement Layout

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A nonparametric Bayesian Poisson gamma model for count data

A nonparametric Bayesian Poisson gamma model for count data

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21st International Conference on pattern recognition, ICPR 2012

作者： Gupta, Sunil Kumar Phung, Dinh Venkatesh, Svetha Centre for Pattern Recognition and Data Analytics Deakin University Australia

ISBN: (纸本)9784990644109

We propose a nonparametric Bayesian, linear Poisson gamma model for count data and use it for dictionary learning. A key property of this model is that it captures the parts-based representation similar to nonnegative matrix factorization. We present an auxiliary variable Gibbs sampler, which turns the intractable inference into a tractable one. Combining this inference procedure with the slice sampler of Indian buffet process, we show that our model can learn the number of factors automatically. Using synthetic and real-world datasets, we show that the proposed model outperforms other state-of-the-art nonparametric factor models. © 2012 ICPR Org Committee.

关键词： pattern recognition

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Matching objects across the textured-smooth continuum

Matching objects across the textured-smooth continuum

引用

2012 Australasian Conference on Robotics and Automation, ACRA 2012

作者： Arandjelović, Ognjen Centre for Pattern Recognition and Data Analytics Deakin University Geelong VIC 3220 Australia

ISBN: (纸本)9780980740431

The problem of 3D object recognition is of immense practical importance, with the last decade witnessing a number of breakthroughs in the state of the art. Most of the previous work has focused on the matching of textured objects using local appearance descriptors extracted around salient image points. The recently proposed bag of boundaries method was the first to address directly the problem of matching smooth objects using boundary features. However, no previous work has attempted to achieve a holistic treatment of the problem by jointly using textural and shape features which is what we describe herein. Due to the complementarity of the two modalities, we fuse the corresponding matching scores and learn their relative weighting in a data specific manner by optimizing discriminative performance on synthetically distorted data. For the textural description of an object we adopt a representation in the form of a histogram of SIFT based visual words. Similarly the apparent shape of an object is represented by a histogram of discretized features capturing local shape. On a large public database of a diverse set of objects, the proposed method is shown to outperform significantly both purely textural and purely shape based approaches for matching across viewpoint variation.

关键词： Graphic methods

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Multi-modal abnormality detection in video with unknown data segmentation

Multi-modal abnormality detection in video with unknown data...

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21st International Conference on pattern recognition, ICPR 2012

作者： Nguyen, Tien Vu Phung, Dinh Rana, Santu Pham, Duc Son Venkatesh, Svetha Centre for Pattern Recognition and Data Analytics Deakin University VIC Australia Department of Computing Curtin University WA Australia

ISBN: (纸本)9784990644109

This paper examines a new problem in large scale stream data: abnormality detection which is localized to a data segmentation process. Unlike traditional abnormality detection methods which typically build one unified model across data stream, we propose that building multiple detection models focused on different coherent sections of the video stream would result in better detection performance. One key challenge is to segment the data into coherent sections as the number of segments is not known in advance and can vary greatly across cameras;and a principled way approach is required. To this end, we first employ the recently proposed infinite HMM and collapsed Gibbs inference to automatically infer data segmentation followed by constructing abnormality detection models which are localized to each segmentation. We demonstrate the superior performance of the proposed framework in a real-world surveillance camera data over 14 days. © 2012 ICPR Org Committee.

关键词： pattern recognition

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A nonparametric Bayesian Poisson gamma model for count data

A nonparametric Bayesian Poisson gamma model for count data

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International Conference on pattern recognition

作者： Sunil Kumar Gupta Dinh Phung Svetha Venkatesh Centre for Pattern Recognition and Data Analytics Deakin University Australia

ISBN: (纸本)9781467322164

关键词： data models Analytical models Bayesian methods Dictionaries Face Load modeling Indexes

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