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Automatic bridge crack detection - A texture analysis-based approach 6

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6th iapr TC3 International workshop on artificial neural networks for pattern recognition, ANNPR 2014

作者： Chanda, Sukalpa Bu, Guoping Guan, Hong Jo, Jun Pal, Umapada Loo, Yew-Chaye Blumenstein, Michael School of Information and Communication Technology Griffith University QLD4222 Australia School of Engineering Griffith University QLD4222 Australia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata700108 India

ISBN: (纸本)9783319116556

To date, identifying cracks in bridges and determining bridge conditions primarily involve manual labour. Bridge inspection by human experts has some drawbacks such as the inability to physically examine all parts of the bridge, sole dependency on the expert knowledge of the bridge inspector. Moreover it requires proper training of the human resource and overall it is not cost effective. This article proposes an automatic bridge inspection approach exploiting wavelet-based image features along with Support Vector Machines for automatic detection of cracks in bridge images. A two-stage approach is followed, where in the first stage a decision is made as whether an image should undergo a pre-processing step (depending on image characteristics), and later in the second stage, wavelet features are extracted from the image using a sliding window-based technique. We obtained an overall accuracy of 92.11% while conducting experiments even on noisy and complex bridge images. © Springer International Publishing Switzerland 2014.

关键词： Crack detection

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Dynamic weighted fusion of adaptive classifier ensembles based on changing data streams 6

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6th iapr TC3 International workshop on artificial neural networks for pattern recognition, ANNPR 2014

作者： Pagano, Christophe Granger, Eric Sabourin, Robert Marcialis, Gian Luca Roli, Fabio Laboratoire d’imagerie de vision et d’intelligence artificielle École de technologie supérieure Université du Québec Montreal Canada Pattern Recognition and Applications Group Dept. Of Electrical and Electronic Engineering University of Cagliari Cagliari Italy

ISBN: (纸本)9783319116556

Adapting classification systems according to new input data streams raises several challenges in changing environments. Although several adaptive ensemble-based strategies have been proposed to preserve previously-acquired knowledge and reduce knowledge corruption, the fusion of multiple classifiers trained to represent different concepts can increase the uncertainty in prediction level, since only a sub-set of all classifier may be relevant. In this paper, a new score-level fusion technique, called Swavgh, is proposed where each classifier is dynamically weighted according to the similarity between an input pattern and the histogram representation of each concept present in the ensemble. During operations, the Hellinger distance between an input and the histogram representation of every previously-learned concept is computed, and the score of every classifier is weighted dynamically according to the resemblance to the underlying concept distribution. Simulation produced with synthetic problems indicate that the proposed fusion technique is able to increase system performance when input data streams incorporate abrupt concept changes, yet maintains a level of performance that is comparable to the average fusion rule when the changes are more gradual. © Springer International Publishing Switzerland 2014.

关键词： Graphic methods

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Computing upper and lower bounds of graph edit distance in cubic time 6

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6th iapr TC3 International workshop on artificial neural networks for pattern recognition, ANNPR 2014

作者： Riesen, Kaspar Fischer, Andreas Bunke, Horst Institute for Information Systems University of Applied Sciences and Arts Northwestern Switzerland Riggenbachstrasse 16 Olten4600 Switzerland Biomedical Science and Technologies Research Centre Polytechnique Montreal 2500 Chemin de Polytechnique MontrealH3T 1J4 Canada Institute of Computer Science and Applied Mathematics University of Bern Neubrückstrasse 10 Bern3012 Switzerland

ISBN: (纸本)9783319116556

Exact computation of graph edit distance (GED) can be solved in exponential time complexity only. A previously introduced approximation framework reduces the computation of GED to an instance of a linear sum assignment problem. Major benefit of this reduction is that an optimal assignment of nodes (including local structures) can be computed in polynomial time. Given this assignment an approximate value of GED can be immediately derived. Yet, since this approach considers local-rather than the global-structural properties of the graphs only, the GED derived from the optimal assignment is suboptimal. The contribution of the present paper is twofold. First, we give a formal proof that this approximation builds an upper bound of the true graph edit distance. Second, we show how the existing approximation framework can be reformulated such that a lower bound of the edit distance can be additionally derived. Both bounds are simultaneously computed in cubic time. © Springer International Publishing Switzerland 2014.

关键词： Polynomial approximation

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Combining bipartite graph matching and beam search for graph edit distance approximation 6

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6th iapr TC3 International workshop on artificial neural networks for pattern recognition, ANNPR 2014

ISBN: (纸本)9783319116556

Graph edit distance (GED) is a powerful and flexible graph dissimilarity model. Yet, exact computation of GED is an instance of a quadratic assignment problem and can thus be solved in exponential time complexity only. A previously introduced approximation framework reduces the computation of GED to an instance of a linear sum assignment problem. Major benefit of this reduction is that an optimal assignment of nodes (including local structures) can be computed in polynomial time. Given this assignment an approximate value of GED can be immediately derived. Yet, the primary optimization process of this approximation framework is able to consider local edge structures only, and thus, the observed speed up is at the expense of approximative, rather than exact, distance values. In order to improve the overall approximation quality, the present paper combines the original approximation framework with a fast tree search procedure. More precisely, we regard the assignment from the original approximation as a starting point for a subsequent beam search. In an experimental evaluation on three real world data sets a substantial gain of assignment accuracy can be observed while the run time remains remarkable low. © Springer International Publishing Switzerland 2014.

关键词： Polynomial approximation

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Detection of cognitive impairment in MS based on an EEG P300 paradigm

Detection of cognitive impairment in MS based on an EEG P300...

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3rd International workshop on pattern recognition in NeuroImaging (PRNI)

作者： Van Schependom, Jeroen D'hooge, Mieke Cleynhens, Krista D'hooghe, Marie B. De Keyser, Jacques Nagels, Guy Vrije Univ Brussel Ctr Neurosci Brussels Belgium

ISBN: (纸本)9780769550619

Cognitive impairment affects half of the multiple sclerosis (MS) patient population and is an important factor of quality of life. Cognitive impairment is, however, difficult to detect. Apart from the traditional features used in P300 experiments (e. g. amplitude and latency at different electrodes), we want to investigate the value of network-features on the classification of MS patients as cognitively intact or impaired. We included 305 MS patients, recruited at the National MS Center Melsbroek (Belgium). About half of them was denoted cognitively impaired (143). We divided this patient group in a training set (on which we used 10-fold crossvalidation) and an independent test set. Results are reported on this last group to increase the generalizability. We found the correlations linking electrodes from one hemisphere with the other significantly different between the two groups MS patients. Especially in the parietal region this difference was very significant (1.5e(-12)). Using a simple cutoff on this variable, lead to a Percentage Correctly Classified (PCC) of 0.70 and an Area Under Curve (AUC) of the Receiver Operator Curve (ROC) of 0.76. The network parameters that were calculated showed a comparable result for the total number of edges included in the network. Combining these features in a logistic regression model, artificial neural networks or Naive Bayes resulted in a PCC's of 0.68-0.70. These results support the recent suggestion that cognitive dysfunction in MS is caused by a disconnection mechanism in the cerebellum. We have obtained these results applying graph theoretical analyses on EEG data instead of the more common fMRI-analyses. The classification accuracy obtained is, however, not yet sufficient for application in clinical practice.

关键词： Multiple Sclerosis EEG Network Analysis pattern recognition

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artificial neural networks in pattern recognition - 5th INNS iapr TC 3 GIRPR workshop, ANNPR 2012, Proceedings

Artificial Neural Networks in Pattern Recognition - 5th INNS...

引用

5th INNS iapr TC 3 GIRPR workshop on artificial neural networks for pattern recognition, ANNPR 2012

ISBN: (纸本)9783642332111

The proceedings contain 21 papers. The topics discussed include: how to quantitatively compare data dissimilarities for unsupervised machine learning?;kernel robust soft learning vector quantization;incremental learning by message passing in hierarchical temporal memory;representative prototype sets for data characterization and classification;feature selection by block addition and block deletion;feature selection by block addition and block deletion;towards a novel probabilistic graphical model of sequential data: fundamental notions and a solution to the problem of parameter learning;towards a novel probabilistic graphical model of sequential data: a solution to the problem of structure learning and an empirical evaluation;statistical recognition of a set of patterns using novel probability neural network;and on graph-associated matrices and their eigenvalues for optical character recognition.

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Fusion of fragmentary classifier decisions for affective state recognition

Fusion of fragmentary classifier decisions for affective sta...

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1st iapr TC3 workshop on Multimodal pattern recognition of Social Signals in Human-Computer-Interaction, MPRSS 2012

作者： Krell, Gerald Glodek, Michael Panning, Axel Siegert, Ingo Michaelis, Bernd Wendemuth, Andreas Schwenker, Friedhelm Institute for Electronics Signal Processing and Communications Otto-von-Guericke University Magdeburg Germany Institute of Neural Information Processing Ulm University Germany

ISBN: (纸本)9783642370809

Real human-computer interaction systems based on different modalities face the problem that not all information channels are always available at regular time steps. Nevertheless an estimation of the current user state is required at anytime to enable the system to interact instantaneously based on the available modalities. A novel approach to decision fusion of fragmentary classifications is therefore proposed and empirically evaluated for audio and video signals of a corpus of non-acted user behavior. It is shown that visual and prosodic analysis successfully complement each other leading to an outstanding performance of the fusion architecture. © 2013 Springer-Verlag.

关键词： Emotion recognition

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Multimodal pattern recognition of Social Signals in Human-Computer- Interaction - First iapr TC3 workshop, MPRSS 2012, Revised Selected Papers

Multimodal Pattern Recognition of Social Signals in Human-Co...

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1st iapr TC3 workshop on Multimodal pattern recognition of Social Signals in Human-Computer-Interaction, MPRSS 2012

ISBN: (纸本)9783642370809

The proceedings contain 13 papers. The topics discussed include: generative modeling of dyadic conversations: characterization of pragmatic skills during development age;social coordination assessment: distinguishing between shape and timing;eye localization from infrared thermal images;the effect of fuzzy training targets on voice quality classification;a non-invasive multi-sensor capturing system for human physiological and behavioral responses analysis;motion history of skeletal volumes and temporal change in bounding volume fusion for human action recognition;multi-view multi-modal gait based human identity recognition from surveillance videos;using the transferable belief model for multimodal input fusion in companion systems;and fusion of fragmentary classifier decisions for affective state recognition.

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The effect of fuzzy training targets on voice quality classification

The effect of fuzzy training targets on voice quality classi...

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1st iapr TC3 workshop on Multimodal pattern recognition of Social Signals in Human-Computer-Interaction, MPRSS 2012

作者： Scherer, Stefan Kane, John Gobl, Christer Schwenker, Friedhelm Institute of Creative Technologies University of Southern California United States Phonetics and Speech Laboratory Trinity College Dublin Ireland Institute of Neural Information Processing Ulm University Germany

ISBN: (纸本)9783642370809

The dynamic use of voice qualities in spoken language can reveal useful information on a speaker's attitude, mood and affective states. This information may be desirable for a range of speech technology applications. However, annotation of voice quality may frequently be inconsistent across raters. But whom should one trust or is the truth somewhere in between? The current study looks first to describe a voice quality feature set that is suitable for differentiating voice qualities on a tense to breathy dimension. These features are used as inputs to a fuzzy-input fuzzy-output support vector machine (F 2SVM) algorithm, to automatically classify the voice qualities. The F2SVM is compared to standard approaches and shows promising results. Performances for cross validation, leave one speaker out, and cross corpus experiments of around 90% are achieved. © 2013 Springer-Verlag.

关键词： artificial intelligence

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3D motion estimation of human body from video with dynamic camera work

3D motion estimation of human body from video with dynamic c...

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1st iapr TC3 workshop on Multimodal pattern recognition of Social Signals in Human-Computer-Interaction, MPRSS 2012

作者： Ayumi, Matsumoto Wu, Xiaojun Harumi, Kawamura Akira, Kojima Nippon Telegraph and Telephone Corporation Japan NTT Communications Corporation Japan

ISBN: (纸本)9783642370809

Occlusion or camera setting produces a high degree of ambiguity when estimating human body motion from monocular video sequences. Good human motion models are an important means of addressing this problem. In this work, we propose a hierarchical motion model and a motion estimation for it to estimate human motion without camera calibration and with free camera operation. The model is able to generate particles in multi-spaces and thus is able to estimate both camera view and human motion at one time. We showed the possibility of achieving 3D motion estimation for simple movements such as "walking" without camera calibration and with dynamic camera operation. © 2013 Springer-Verlag.

关键词： Motion estimation

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