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检索条件"任意字段=2nd IAPR Workshop on Artificial Neural Networks in Pattern Recognition"

共 135 条记录，以下是31-40 订阅

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Virtual Thin Slice: 3D Conditional GAN-based Super-Resolution for CT Slice Interval 2nd

Virtual Thin Slice: 3D Conditional GAN-based Super-Resolutio...

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2nd International workshop on Machine Learning for Medical Image Reconstruction (MLMIR) as part of the 22nd Medical Image Computing and Computer Assisted Intervention (MICCAI) Conference

作者： Kudo, Akira Kitamura, Yoshiro Li, Yuanzhong Iizuka, Satoshi Simo-Serra, Edgar Fujifilm Corp Imaging Technol Ctr Minato Ku Tokyo Japan Univ Tsukuba Ctr Artificial Intelligence Res Tsukuba Ibaraki Japan Waseda Univ Dept Comp Sci & Engn Shinjuku Ku Tokyo Japan

ISBN: (纸本)9783030338435;9783030338428

Many CT slice images are stored with large slice intervals to reduce storage size in clinical practice. This leads to low resolution perpendicular to the slice images (i.e., z-axis), which is insufficient for 3D visualization or image analysis. In this paper, we present a novel architecture based on conditional Generative Adversarial networks (cGANs) with the goal of generating high resolution images of main body parts including head, chest, abdomen and legs. However, GANs are known to have a difficulty with generating a diversity of patterns due to a phenomena known as mode collapse. To overcome the lack of generated pattern variety, we propose to condition the discriminator on the different body parts. Furthermore, our generator networks are extended to be three dimensional fully convolutional neural networks, allowing for the generation of high resolution images from arbitrary fields of view. In our verification tests, we show that the proposed method obtains the best scores by PSNR/SSIM metrics and Visual Turing Test, allowing for accurate reproduction of the principle anatomy in high resolution. We expect that the proposed method contribute to effective utilization of the existing vast amounts of thick CT images stored in hospitals.

关键词： Deep learning Generative Adversarial Network Super resolution Computer vision Computed tomography

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F-Measure Curves for Visualizing Classifier Performance with Imbalanced Data 8th

F-Measure Curves for Visualizing Classifier Performance with...

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8th iapr TC3 workshop on artificial neural networks in pattern recognition (ANNPR)

作者： Soleymani, Roghayeh Granger, Eric Fumera, Giorgio Univ Quebec Ecole Technol Super Lab Imagerie Vis & Intelligence Artificielle Montreal PQ Canada Univ Cagliari Dept Elect & Elect Engn Pattern Recognit & Applicat Lab Cagliari Italy

ISBN: (纸本)9783319999784;9783319999777

Training classifiers using imbalanced data is a challenging problem in many real-world recognition applications due in part to the bias in performance that occur for: (1) classifiers that are often optimized and compared using unsuitable performance measurements for imbalance problems;(2) classifiers that are trained and tested on a fixed imbalance level of data, which may differ from operational scenarios;(3) cases where the preference of correct classification of classes is application dependent. Specialized performance evaluation metrics and tools are needed for problems that involve class imbalance, including scalar metrics that assume a given operating condition (skew level and relative preference of classes), and global evaluation curves or metrics that consider a range of operating conditions. We propose a global evaluation space for the scalar F-measure metric that is analogous to the cost curves for expected cost. In this space, a classifier is represented as a curve that shows its performance over all of its decision thresholds and a range of imbalance levels for the desired preference of true positive rate to precision. Experiments with synthetic data show the benefits of evaluating and comparing classifiers under different operating conditions in the proposed F-measure space over ROC, precision-recall, and cost spaces.

关键词： Class imbalance Performance visualization tools F-measure

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Deep Learning in the Wild 8th

Deep Learning in the Wild

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8th iapr TC3 workshop on artificial neural networks in pattern recognition (ANNPR)

作者： Stadelmann, Thilo Amirian, Mohammadreza Arabaci, Ismail Arnold, Marek Duivesteijn, Gilbert Francois Elezi, Ismail Geiger, Melanie Lorwald, Stefan Meier, Benjamin Bruno Rombach, Katharina Tuggener, Lukas ZHAW Datalab Winterthur Switzerland Sch Engn Winterthur Switzerland Ulm Univ Inst Neural Informat Proc Ulm Germany ARGUS DATA INSIGHTS Schweiz AG Zurich Switzerland Deep Impact AG Winterthur Switzerland Ca Foscari Univ Venice DAIS Venice Italy Univ Neuchatel Inst Informat Neuchatel Switzerland PricewaterhouseCoopers AG Zurich Switzerland IDSIA Dalle Molle Inst Artificial Intelligence Manno Switzerland

ISBN: (纸本)9783319999784;9783319999777

Deep learning with neural networks is applied by an increasing number of people outside of classic research environments, due to the vast success of the methodology on a wide range of machine perception tasks. While this interest is fueled by beautiful success stories, practical work in deep learning on novel tasks without existing baselines remains challenging. This paper explores the specific challenges arising in the realm of real world tasks, based on case studies from research & development in conjunction with industry, and extracts lessons learned from them. It thus fills a gap between the publication of latest algorithmic and methodical developments, and the usually omitted nitty- gritty of how to make them work. Specifically, we give insight into deep learning projects on face matching, print media monitoring, industrial quality control, music scanning, strategy game playing, and automated machine learning, thereby providing best practices for deep learning in practice.

关键词： Data availability Deployment Loss & reward shaping Real world tasks

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Satellite Image Classification with Deep Learning

Satellite Image Classification with Deep Learning

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IEEE Applied Imagery pattern recognition workshop (AIPR)

作者： Pritt, Mark Chern, Gary Lockheed Martin Space Gaithersburg MD 20879 USA Lockheed Martin Space Palo Alto CA USA

ISBN: (纸本)9781538612354

Satellite imagery is important for many applications including disaster response, law enforcement, and environmental monitoring. These applications require the manual identification of objects and facilities in the imagery. Because the geographic expanses to be covered are great and the analysts available to conduct the searches are few, automation is required. Yet traditional object detection and classification algorithms are too inaccurate and unreliable to solve the problem. Deep learning is a family of machine learning algorithms that have shown promise for the automation of such tasks. It has achieved success in image understanding by means of convolutional neural networks. In this paper we apply them to the problem of object and facility recognition in high-resolution, multi-spectral satellite imagery. We describe a deep learning system for classifying objects and facilities from the IARPA Functional Map of the World (fMoW) dataset into 63 different classes. The system consists of an ensemble of convolutional neural networks and additional neural networks that integrate satellite metadata with image features. It is implemented in Python using the Keras and TensorFlow deep learning libraries and runs on a Linux server with an NVIDIA Titan X graphics card. At the time of writing the system is in 2nd place in the fMoW TopCoder competition. Its total accuracy is 83% the F-1 score is 0.797, and it classifies 15 of the classes with accuracies of 95% or better.

关键词： artificial intelligence AI deep learning machine learning image understanding recognition classification satellite imagery

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Engineering Applications of neural networks 1st ed. 2017

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丛书名： Communications in Computer and Information Science

2017年

作者： Giacomo Boracchi Lazaros Iliadis Chrisina Jayne Aristidis Likas

ISBN: (数字)9783319651729

ISBN: (纸本)9783319651712

This book constitutes the refereed proceedings of the 18th International Conference on Engineering Applications of neural networks, EANN 2017, held in Athens, Greece, in August 2017. The 40 revised full papers and 5 revised short papers presented were carefully reviewed and selected from 83 submissions. The papers cover the topics of deep learning, convolutional neural networks, image processing, pattern recognition, recommendation systems, machine learning, and applications of artificial neural networks (ANN) applications in engineering, 5G telecommunication networks, and audio signal processing. The volume also includes papers presented at the 6th Mining Humanistic Data workshop (MHDW 2017) and the2nd workshop on 5G-Putting Intelligence to the Network Edge (5G-PINE).

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Spatio-temporal pain recognition in CNN-based super-resolved facial images 1

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3rd workshop on Video Analytics for Audience Measurement, VAAM 2016 and 2nd International workshop on Face and Facial Expression recognition, FFER 2016

作者： Bellantonio, Marco Haque, Mohammad A. Rodriguez, Pau Nasrollahi, Kamal Telve, Taisi Escarela, Sergio Gonzalez, Jordi Moeslund, Thomas B. Rasti, Pejman Anbarjafari, Gholamreza University of Barcelona Barcelona Spain Laboratory Aalborg University Aalborg Denmark iCV Research Group Institute of Technology University of Tartu Tartu Estonia

ISBN: (数字)9783319566870

ISBN: (纸本)9783319566863

Automatic pain detection is a long expected solution to a prevalent medical problem of pain management. This is more relevant when the subject of pain is young children or patients with limited ability to communicate about their pain experience. Computer vision-based analysis of facial pain expression provides a way of efficient pain detection. When deep machine learning methods came into the scene, automatic pain detection exhibited even better performance. In this paper, we figured out three important factors to exploit in automatic pain detection: spatial information available regarding to pain in each of the facial video frames, temporal axis information regarding to pain expression pattern in a subject video sequence, and variation of face resolution. We employed a combination of convolutional neural network and recurrent neural network to setup a deep hybrid pain detection framework that is able to exploit both spatial and temporal pain information from facial video. In order to analyze the effect of different facial resolutions, we introduce a super-resolution algorithm to generate facial video frames with different resolution setups. We investigated the performance on the publicly available UNBC-McMaster Shoulder Pain database. As a contribution, the paper provides novel and important information regarding to the performance of a hybrid deep learning framework for pain detection in facial images of different resolution. © Springer International Publishing AG 2017.

关键词： Recurrent neural networks

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A Spiking neural Network for Personalised Modelling of Electrogastrography (EGG) 7th

A Spiking Neural Network for Personalised Modelling of Elect...

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7th iapr TC3 workshop on artificial neural networks in pattern recognition (ANNPR)

作者： Breen, Vivienne Kasabov, Nikola Du, Peng Calder, Stefan Auckland Univ Technol Knowledge Engn & Discovery Res Inst Auckland New Zealand Univ Auckland Auckland Bioengn Inst Auckland New Zealand

ISBN: (纸本)9783319461823;9783319461816

EGG records the resultant body surface potential of gastric slow waves (electrical activity);while slow waves regulate contractions of gastric muscles, it is the electrical activity we are recording, not movement (like ECG records the cardiac electrical activity, but not the contractions of the heart, even the two are essentially related).

关键词： Spiking neural network Personalised modelling Electrogastrography Functional dyspepsia Slow wave dysrhythmia

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Finding Small Sets of Random Fourier Features for Shift-Invariant Kernel Approximation 7th

Finding Small Sets of Random Fourier Features for Shift-Inva...

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7th iapr TC3 workshop on artificial neural networks in pattern recognition (ANNPR)

作者： Schleif, Frank-M. Kaban, Ata Tino, Peter Univ Appl Sci Wurzburg Schweinfurt Sch Comp Sci D-97074 Wurzburg Germany Univ Appl Sci Mittweida Computat Intelligence Grp Tech Pl 17 D-09648 Mittweida Germany Univ Birmingham Sch Comp Sci Birmingham B15 2TT W Midlands England

ISBN: (纸本)9783319461823;9783319461816

Kernel based learning is very popular in machine learning, but many classical methods have at least quadratic runtime complexity. Random fourier features are very effective to approximate shift-invariant kernels by an explicit kernel expansion. This permits to use efficient linear models with much lower runtime complexity. As one key approach to kernelize algorithms with linear models they are successfully used in different methods. However, the number of features needed to approximate the kernel is in general still quite large with substantial memory and runtime costs. Here, we propose a simple test to identify a small set of random fourier features with linear costs, substantially reducing the number of generated features for low rank kernel matrices, while widely keeping the same representation accuracy. We also provide generalization bounds for the proposed approach.

关键词： Fourier transforms

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Modelling the multi-layer artificial neural network for internet traffic forecasting: The model selection design issues 2

Modelling the multi-layer artificial neural network for inte...

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2nd International Conference on Computing Research and Innovations, CoRI 2016

作者： Odim, Mba O. Gbadeyan, Jacob A. Sadiku, Joseph S. Computer Science Department Redeemer's University Ede Nigeria Mathematics Department University of Ilorin Ilorin Nigeria Computer Science Department University of Ilorin Ilorin Nigeria

Internet traffic forecasting models with learning ability, such as the artificial neural network (ANN), have been growing in popularity in recent time due to their impressive performance in modelling the high degree of variability and nonlinearity of internet traffic. This study examined the impacts of some design issues on performance of the multi-layer artificial neural network for internet traffic forecasting. The traffic forecasting was modelled as a standard time series problem and the multilayer artificial neural network designed to performs the time series function mapping. The input lags were varied from 1 to 24. The training epoch values of 200, 500, and 1000 on one and two hidden layered networks were used. The learning algorithm was backpropagation with 0.1 learning rate and 0.9 momentum on logistic sigmoid activation function. The model was implemented in Visual Basic and validated with four categories of classified time series internet traffic of a branch residential network of one of a firm in Nigeria. Various predictive performances without consistent pattern were observed on the issues considered, however, input lag one gave the worst performance in all cases for the HOURLY traffic;three of the four traffic categories demonstrated the superiority of two hidden layers to one hidden layer. Although the epoch values of 200, 500 and 1000 showed no consistent performance variations, epoch value 200 outperformed the others on the model selections. The study revealed that input lags, number of hidden layers and epoch values could impact on the traffic forecasting performance of multilayer perceptron and that performance could be considerably improved by careful selection of those parameters through experimentations. Copyright © 2016 Ibadan ACM.

关键词： Time series

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2nd International workshop on Machine Learning, Optimization and Big Data, MOD 2016

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2nd International workshop on Machine Learning, Optimization and Big Data, MOD 2016

ISBN: (纸本)9783319514680

The proceedings contain 40 papers. The special focus in this conference is on Machine Learning, Optimization and Big Data. The topics include: Data-based forest management with uncertainties and multiple objectives;metabolic circuit design automation by multi objective BioCAD;a Nash equilibrium approach to metabolic network analysisa blocking strategy for ranking features according to probabilistic relevance;a scalable biclustering method for heterogeneous medical data;neural learning of heuristic functions for general game playing;comparing hidden Markov models and long short term memory neural networks for learning action representations;feature selection via co-regularized sparse-group lasso;a branch-and-cut algorithm for a multi-item inventory distribution problem;models based on relative influence of factors;design of acoustic metamaterials through nonlinear programming;driver maneuvers inference through machine learning;a systems biology approach for unsupervised clustering of high-dimensional data;large scale bandit recommender system;automatic generation of sitemaps based on navigation systems;a customer relationship management case study based on banking data;lagrangian relaxation bounds for a production-inventory-routing problem;convergence rate evaluation of derivative-free optimization techniques;the learnability of business rules;dynamic programming with approximation function for nurse scheduling;pattern discovery using nonnegative matrix factorizations;optimizing the location of helicopter emergency medical service operating sites;an enhanced infra chromatic bound for the maximum clique problem;cultural ant colony optimization on GPUs for travelling salesman problem and combining genetic algorithm with the multilevel paradigm for the maximum constraint satisfaction problem.

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