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检索条件"任意字段=1st IAPR TC3 Workshop on Artificial Neural Networks in Pattern Recognition"

共 81 条记录，以下是21-30 订阅

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Objectness Scoring and Detection Proposals in Forward-Looking Sonar Images with Convolutional neural networks 7th

Objectness Scoring and Detection Proposals in Forward-Lookin...

引用

7th iapr tc3 workshop on artificial neural networks in pattern recognition (ANNPR)

作者： Valdenegro-Toro, Matias Heriot Watt Univ Sch Engn & Phys Sci Ocean Syst Lab Edinburgh EH14 4AS Midlothian Scotland

ISBN: (纸本)9783319461823;9783319461816

Forward-looking sonar can capture high resolution images of underwater scenes, but their interpretation is complex. Generic object detection in such images has not been solved, specially in cases of small and unknown objects. In comparison, detection proposal algorithms have produced top performing object detectors in real-world color images. In this work we develop a Convolutional neural Network that can reliably score objectness of image windows in forward-looking sonar images and by thresholding objectness, we generate detection proposals. In our dataset of marine garbage objects, we obtain 94% recall, generating around 60 proposals per image. The biggest strength of our method is that it can generalize to previously unseen objects. We show this by detecting chain links, walls and a wrench without previous training in such objects. We strongly believe our method can be used for class-independent object detection, with many real-world applications such as chain following and mine detection.

关键词： Object detection Detection proposals Sonar image processing Forward-looking sonar

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Monte Carlo Based Importance Estimation of Localized Feature Descriptors for the recognition of Facial Expressions 3

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3rd iapr tc3 workshop on pattern recognition of Social Signals in Human-Computer-Interaction (MPRSS)

作者： Kaechele, Markus Palm, Guenther Schwenker, Friedhelm Univ Ulm Inst Neural Informat Proc D-89069 Ulm Germany

ISBN: (纸本)9783319148991;9783319148984

The automated and exact identification of facial expressions in human computer interaction scenarios is a challenging but necessary task to recognize human emotions by a machine learning system. The human face consists of regions whose elements contribute to single expressions in a different manner. This work aims to shed light onto the importance of specific facial regions to provide information which can be used to discriminate between different facial expressions from a statistical pattern recognition perspective. A sampling based classification approach is used to reveal informative locations in the face. The results are expression-sensitive importance maps that indicate regions of high discriminative power which can be used for various applications.

关键词： Human computer interaction

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Audio-Visual User Identification in HCI Scenarios 3

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3rd iapr tc3 workshop on pattern recognition of Social Signals in Human-Computer-Interaction (MPRSS)

作者： Kaechele, Markus Meudt, Sascha Schwarz, Andrej Schwenker, Friedhelm Univ Ulm Inst Neural Informat Proc D-89069 Ulm Germany

ISBN: (纸本)9783319148991;9783319148984

Modern computing systems are usually equipped with various input devices such as microphones or cameras, and hence the user of such a system can easily be identified. User identification is important in many human computer interaction (HCI) scenarios, such as speech recognition, activity recognition, transcription of meeting room data or affective computing. Here personalized models may significantly improve the performance of the overall recognition system. This paper deals with audio-visual user identification. The main processing steps are segmentation of the relevant parts from video and audio streams, extraction of meaningful features and construction of the overall classifier and fusion architectures. The proposed system has been evaluated on the MOBIO dataset, a benchmark database consisting of real-world recordings collected from mobile devices, e.g. cell-phones. recognition rates of up to 92% could be achieved for the proposed audio-visual classifier system.

关键词： Person identification Classifier fusion Human computer interaction Multi modal data processing

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uulmMAD - A Human Action recognition Dataset for Ground-Truth Evaluation and Investigation of View Invariances 3

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3rd iapr tc3 workshop on pattern recognition of Social Signals in Human-Computer-Interaction (MPRSS)

作者： Glodek, Michael Layher, Georg Heilemann, Felix Gawrilowicz, Florian Palm, Guenther Schwenker, Friedhelm Neumann, Heiko Univ Ulm Inst Neural Informat Proc D-89069 Ulm Germany

ISBN: (纸本)9783319148991;9783319148984

In recent time, human action recognition has gained increasing attention in pattern recognition. However, many datasets in the literature focus on a limited number of target-oriented properties. Within this work, we present a novel dataset, named uulmMAD, which has been created to benchmark state-of-the-art action recognition architectures addressing multiple properties, e.g. high-resolutions cameras, perspective changes, realistic cluttered background and noise, overlap of action classes, different execution speeds, variability in subjects and their clothing, and the availability of a pose ground-truth. The uulmMAD was recorded using three synchronized high-resolution cameras and an inertial motion capturing system. Each subject performed fourteen actions at least three times in front of a green screen. Selected actions in four variants were recorded, i.e. normal, pausing, fast and deceleration. The data has been post-processed in order to separate the subject from the background. Furthermore, the camera and the motion capturing data have been mapped onto each other and 3D-avatars have been generated to further extend the dataset. The avatars have also been used to emulate the self-occlusion in pose recognition when using a time-of-flight camera. In this work, we analyze the uulmMAD using a state-of-the-art action recognition architecture to provide first baseline results. The results emphasize the unique characteristics of the dataset. The dataset will be made publicity available upon publication of the paper.

关键词： Cameras

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Face recognition based on discriminative dictionary with multilevel feature fusion 6

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6th iapr tc3 International workshop on artificial neural networks for pattern recognition, ANNPR 2014

作者： Li, Hongjun Nobile, Nicola Suen, Ching Y. School of Electronic Information Engineering Nantong University Nantong226019 China Centre for Pattern Recognition and Machine Intelligence Concordia University MontrealQCH3G 1M8 Canada

ISBN: (纸本)9783319116556

In order to alleviate the influence of illumination, pose, expression and occlusion variations in face recognition, in this paper, an effective face recognition method based on discriminative sparse representation is proposed. To solve the problem of these variations, we extract discriminative features which represent for each of the training images, and propose a novel dictionary by learning discriminative features. Firstly, we decompose a test image by using nonsubsampled contourlet transform (NSCT), and then fuse the information according to the features from each subband and their contributions. Finally, we obtain the discriminative features of training images and construct a discriminative dictionary. Fuse these multiple features can improve the efficiency and effectiveness of face recognition, especially when training samples are limited and the dimension of feature vector is low. Experimental results on two widely used face databases are presented to demonstrate the efficiency of the proposed approach. © Springer International Publishing Switzerland 2014.

关键词： Face recognition

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Bio-inspired optic flow from event-based neuromorphic sensor input 6

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6th iapr tc3 International workshop on artificial neural networks for pattern recognition, ANNPR 2014

作者： Tschechne, stephan Sailer, Roman Neumann, Heiko Inst. for Neural Information Processing Ulm University Albert-Einstein-Allee 1 Ulm89069 Germany

ISBN: (纸本)9783319116556

Computational models of visual processing often use framebased image acquisition techniques to process a temporally changing stimulus. This approach is unlike biological mechanisms that are spikebased and independent of individual frames. The neuromorphic Dynamic Vision Sensor (DVS) [Lichtsteiner et al., 2008] provides a stream of independent visual events that indicate local illumination changes, resembling spiking neurons at a retinal level. We introduce a new approach for the modelling of cortical mechanisms of motion detection along the dorsal pathway using this type of representation. Our model combines filters with spatio-temporal tunings also found in visual cortex to yield spatiotemporal and direction specificity. We probe our model with recordings of test stimuli, articulated motion and ego-motion. We show how our approach robustly estimates optic flow and also demonstrate how this output can be used for classification purposes. © Springer International Publishing Switzerland 2014.

关键词： Optical flows

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Part-based high accuracy recognition of serial numbers in bank notes 6

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6th iapr tc3 International workshop on artificial neural networks for pattern recognition, ANNPR 2014

作者： Feng, Bo-Yuan Ren, Mingwu Zhang, Xu-Yao Suen, Ching Y. School of Computer Science Nanjing University of Science and Technology Nanjing210094 China National Laboratory of Pattern Recognition Institute of Automation of Chinese Academy of Sciences Beijing100190 China Centre for Pattern Recognition and Machine Intelligence Concordia University MontrealH3G 1M8 Canada

ISBN: (纸本)9783319116556

This paper proposes a novel part-based character recognition method for a new topic of RMB (renminbi bank note, the paper currency used in China) serial number recognition, which is important for reducing financial crime and improving financial market stability and social security. Given an input sample, we first generate a set of local image parts using the Difference-of-Gaussians (DoG) keypoint detector. Then, all of the local parts are classified by an SVM classifier to provide a confidence vector for each part. Finally, three methods are introduced to combine the recognition results of all parts. Since the serial number samples suffer from complex background, occlusion, and degradation, our part-based method takes advantage of both global and local character structure features, and offers an overall increase in robustness and reliability to the entire recognition system. Experiments conducted on a RMB serial number character database show that the test accuracy boosted from 98.90% to 99.33% by utilizing the proposed method with multiple voting based combination strategy. The part-based recognition method can also be extended to other types of banknotes, such as Euro, U.S. and Canadian dollars, or in character recognition applications with complex backgrounds. © Springer International Publishing Switzerland 2014.

关键词： Character recognition

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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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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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