Medical Image Analysis, ISSN 1361-8415, 01/2017, Volume 35, pp. 18 - 31
In this paper, we present a fully automatic brain tumor segmentation method based on Deep Neural Networks (DNNs). The proposed networks are tailored to...
Brain tumor segmentation | Deep neural networks | Convolutional neural networks | Cascaded convolutional neural networks | COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS | ENGINEERING, BIOMEDICAL | RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | Glioblastoma - diagnostic imaging | Brain Neoplasms - diagnostic imaging | Humans | Magnetic Resonance Imaging - methods | Image Processing, Computer-Assisted - methods | Machine Learning | Neural Networks (Computer) | Neural networks | Analysis | Brain tumors | Machine learning
Brain tumor segmentation | Deep neural networks | Convolutional neural networks | Cascaded convolutional neural networks | COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS | ENGINEERING, BIOMEDICAL | RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | Glioblastoma - diagnostic imaging | Brain Neoplasms - diagnostic imaging | Humans | Magnetic Resonance Imaging - methods | Image Processing, Computer-Assisted - methods | Machine Learning | Neural Networks (Computer) | Neural networks | Analysis | Brain tumors | Machine learning
Journal Article
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), ISSN 1063-6919, 06/2015, Volume 7-12-, pp. 806 - 814
Deep neural networks have achieved remarkable performance in both image classification and object detection problems, at the cost of a large number of...
Convolutional codes | Accuracy | Neural networks | Redundancy | Sparse matrices | Matrix decomposition | Kernel | Computer vision | Algorithms | Cascades | Mathematical models | Central processing units | Pattern recognition
Convolutional codes | Accuracy | Neural networks | Redundancy | Sparse matrices | Matrix decomposition | Kernel | Computer vision | Algorithms | Cascades | Mathematical models | Central processing units | Pattern recognition
Conference Proceeding
Pattern Recognition, ISSN 0031-3203, 05/2018, Volume 77, pp. 354 - 377
In the last few years, deep learning has led to very good performance on a variety of problems, such as visual recognition, speech recognition and natural...
Deep learning | Convolutional neural network | RECOGNITION | IMAGES | CLASSIFICATION | TERM | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | VISUAL TRACKING | FEATURES | CNNS | ENGINEERING, ELECTRICAL & ELECTRONIC | Computer science | Language processing | Computational linguistics | Machine vision | Neural networks | Natural language interfaces
Deep learning | Convolutional neural network | RECOGNITION | IMAGES | CLASSIFICATION | TERM | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | VISUAL TRACKING | FEATURES | CNNS | ENGINEERING, ELECTRICAL & ELECTRONIC | Computer science | Language processing | Computational linguistics | Machine vision | Neural networks | Natural language interfaces
Journal Article
IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP), ISSN 2329-9290, 10/2014, Volume 22, Issue 10, pp. 1533 - 1545
Recently, the hybrid deep neural network (DNN)- hidden Markov model (HMM) has been shown to significantly improve speech recognition performance over the...
convolution | convolutional neural networks | pooling | limited weight sharing (LWS) scheme | Training | Convolution | Neural networks | Limited Weight Sharing (LWS) scheme | Hidden Markov models | Speech recognition | Speech | Vectors | Convolutional neural networks | Pooling | ACOUSTICS | CONNECTIONIST FEATURE-EXTRACTION | MODEL | FEATURES | ENGINEERING, ELECTRICAL & ELECTRONIC | Voice recognition | Product development
convolution | convolutional neural networks | pooling | limited weight sharing (LWS) scheme | Training | Convolution | Neural networks | Limited Weight Sharing (LWS) scheme | Hidden Markov models | Speech recognition | Speech | Vectors | Convolutional neural networks | Pooling | ACOUSTICS | CONNECTIONIST FEATURE-EXTRACTION | MODEL | FEATURES | ENGINEERING, ELECTRICAL & ELECTRONIC | Voice recognition | Product development
Journal Article
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, ISSN 1063-6919, 12/2018, pp. 7794 - 7803
Conference Proceeding
Remote Sensing, ISSN 2072-4292, 07/2016, Volume 8, Issue 7, p. 594
A new pansharpening method is proposed, based on convolutional neural networks. We adapt a simple and effective three-layer architecture recently proposed for...
Super-resolution | Segmentation | Convolutional neural networks | Multiresolution | Enhancement | Machine learning | DATA-FUSION | QUALITY | super-resolution | SPECTRAL RESOLUTION IMAGES | enhancement | machine learning | PAN-SHARPENING METHOD | REMOTE SENSING | multiresolution | segmentation | CONTRAST | convolutional neural networks | LANDSAT THEMATIC MAPPER
Super-resolution | Segmentation | Convolutional neural networks | Multiresolution | Enhancement | Machine learning | DATA-FUSION | QUALITY | super-resolution | SPECTRAL RESOLUTION IMAGES | enhancement | machine learning | PAN-SHARPENING METHOD | REMOTE SENSING | multiresolution | segmentation | CONTRAST | convolutional neural networks | LANDSAT THEMATIC MAPPER
Journal Article
05/2017, Volume 60, Issue 6, 7
We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000...
COMPUTER SCIENCE, SOFTWARE ENGINEERING | COMPUTER SCIENCE, HARDWARE & ARCHITECTURE | COMPUTER SCIENCE, THEORY & METHODS | Convolutional codes | Control | Usage | Image processing | Neural networks | Methods
COMPUTER SCIENCE, SOFTWARE ENGINEERING | COMPUTER SCIENCE, HARDWARE & ARCHITECTURE | COMPUTER SCIENCE, THEORY & METHODS | Convolutional codes | Control | Usage | Image processing | Neural networks | Methods
Magazine Article
Neurocomputing, ISSN 0925-2312, 01/2017, Volume 219, pp. 88 - 98
As a powerful visual model, convolutional neural networks (CNNs) have demonstrated remarkable performance in various visual recognition problems, and attracted...
Deep learning | Convolutional neural networks | Hyperspectral image classification | BAND SELECTION | FOREWORD | REPRESENTATION | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | SPECIAL-ISSUE | Computer science | Neural networks | Analysis
Deep learning | Convolutional neural networks | Hyperspectral image classification | BAND SELECTION | FOREWORD | REPRESENTATION | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | SPECIAL-ISSUE | Computer science | Neural networks | Analysis
Journal Article
IEEE Transactions on Pattern Analysis and Machine Intelligence, ISSN 0162-8828, 01/2013, Volume 35, Issue 1, pp. 221 - 231
We consider the automated recognition of human actions in surveillance videos. Most current methods build classifiers based on complex handcrafted features...
Deep learning | Solid modeling | Computational modeling | Computer architecture | action recognition | Feature extraction | Three dimensional displays | 3D convolution | convolutional neural networks | model combination | Kernel | Videos | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | FEATURES | ENGINEERING, ELECTRICAL & ELECTRONIC | Algorithms | Movement - physiology | Image Interpretation, Computer-Assisted - methods | Decision Support Techniques | Imaging, Three-Dimensional - methods | Subtraction Technique | Pattern Recognition, Automated - methods | Neural Networks (Computer) | Three-dimensional display systems | Usage | Neural networks | Innovations | Pattern recognition | Object recognition (Computers) | Simulation methods | Studies | Human | Construction | Surveillance | Mathematical models | Channels | Recognition | Three dimensional
Deep learning | Solid modeling | Computational modeling | Computer architecture | action recognition | Feature extraction | Three dimensional displays | 3D convolution | convolutional neural networks | model combination | Kernel | Videos | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | FEATURES | ENGINEERING, ELECTRICAL & ELECTRONIC | Algorithms | Movement - physiology | Image Interpretation, Computer-Assisted - methods | Decision Support Techniques | Imaging, Three-Dimensional - methods | Subtraction Technique | Pattern Recognition, Automated - methods | Neural Networks (Computer) | Three-dimensional display systems | Usage | Neural networks | Innovations | Pattern recognition | Object recognition (Computers) | Simulation methods | Studies | Human | Construction | Surveillance | Mathematical models | Channels | Recognition | Three dimensional
Journal Article
IEEE Transactions on Medical Imaging, ISSN 0278-0062, 05/2016, Volume 35, Issue 5, pp. 1240 - 1251
Among brain tumors, gliomas are the most common and aggressive, leading to a very short life expectancy in their highest grade. Thus, treatment planning is a...
Training | Context | Brain tumor | Image segmentation | deep learning | Magnetic resonance imaging | brain tumor segmentation | Brain modeling | glioma | convolutional neural networks | Kernel | Tumors | magnetic resonance imaging | ENGINEERING, BIOMEDICAL | CLASSIFICATION | IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY | ENGINEERING, ELECTRICAL & ELECTRONIC | FORESTS | COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS | RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING | Magnetic Resonance Imaging | Glioma - diagnostic imaging | Brain Neoplasms - diagnostic imaging | Glioma - pathology | Humans | Image Interpretation, Computer-Assisted - methods | Brain Neoplasms - pathology | Machine Learning | Neural Networks (Computer) | Usage | Neural networks | Brain tumors | Analysis | Diagnosis | Research | Neurophysiology | Brain cancer | Nuclear magnetic resonance--NMR | Brain | Automation | Segmentation | Similarity | Coefficients
Training | Context | Brain tumor | Image segmentation | deep learning | Magnetic resonance imaging | brain tumor segmentation | Brain modeling | glioma | convolutional neural networks | Kernel | Tumors | magnetic resonance imaging | ENGINEERING, BIOMEDICAL | CLASSIFICATION | IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY | ENGINEERING, ELECTRICAL & ELECTRONIC | FORESTS | COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS | RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING | Magnetic Resonance Imaging | Glioma - diagnostic imaging | Brain Neoplasms - diagnostic imaging | Glioma - pathology | Humans | Image Interpretation, Computer-Assisted - methods | Brain Neoplasms - pathology | Machine Learning | Neural Networks (Computer) | Usage | Neural networks | Brain tumors | Analysis | Diagnosis | Research | Neurophysiology | Brain cancer | Nuclear magnetic resonance--NMR | Brain | Automation | Segmentation | Similarity | Coefficients
Journal Article
Proceedings of the IEEE, ISSN 0018-9219, 12/2017, Volume 105, Issue 12, pp. 2295 - 2329
Deep neural networks (DNNs) are currently widely used for many artificial intelligence (AI) applications including computer vision, speech recognition, and...
VLSI | Neurons | energy-efficient accelerators | Tutorials | spatial architectures | Biological neural networks | dataflow processing | deep learning | ASIC | Neural networks | Machine learning | Computer architecture | Benchmark testing | low power | Convolutional neural networks | Artificial intelligence | deep neural networks | computer architecture | convolutional neural networks | machine learning | ENGINEERING, ELECTRICAL & ELECTRONIC | COPROCESSOR | Computer vision | Platforms | State of the art | Task complexity | Algorithms | Speech recognition | Hardware | Trends | Robotics
VLSI | Neurons | energy-efficient accelerators | Tutorials | spatial architectures | Biological neural networks | dataflow processing | deep learning | ASIC | Neural networks | Machine learning | Computer architecture | Benchmark testing | low power | Convolutional neural networks | Artificial intelligence | deep neural networks | computer architecture | convolutional neural networks | machine learning | ENGINEERING, ELECTRICAL & ELECTRONIC | COPROCESSOR | Computer vision | Platforms | State of the art | Task complexity | Algorithms | Speech recognition | Hardware | Trends | Robotics
Journal Article
International Journal of Computer Vision, ISSN 0920-5691, 1/2016, Volume 116, Issue 1, pp. 1 - 20
In this work we present an end-to-end system for text spotting—localising and recognising text in natural scene images—and text based image retrieval. This...
Deep learning | Pattern Recognition | Text spotting | Text recognition | Text detection | Computer Science | Computer Imaging, Vision, Pattern Recognition and Graphics | Image Processing and Computer Vision | Artificial Intelligence (incl. Robotics) | Convolutional neural networks | Text retrieval | Synthetic data | IMAGES | SCENE TEXT | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | Neural networks | Studies | Vision systems | Image processing systems | Retrieval | Filtering | Pipelines | Images | Texts | Proposals | Character recognition
Deep learning | Pattern Recognition | Text spotting | Text recognition | Text detection | Computer Science | Computer Imaging, Vision, Pattern Recognition and Graphics | Image Processing and Computer Vision | Artificial Intelligence (incl. Robotics) | Convolutional neural networks | Text retrieval | Synthetic data | IMAGES | SCENE TEXT | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | Neural networks | Studies | Vision systems | Image processing systems | Retrieval | Filtering | Pipelines | Images | Texts | Proposals | Character recognition
Journal Article
IEEE Signal Processing Letters, ISSN 1070-9908, 09/2014, Volume 21, Issue 9, pp. 1120 - 1124
We investigate convolutional neural networks (CNNs) for large vocabulary distant speech recognition, trained using speech recorded from a single distant...
AMI corpus | Convolution | Neural networks | Hidden Markov models | Speech recognition | Acoustics | Vectors | distant speech recognition | meetings | convolutional neural networks | deep neural networks | Microphones | ENGINEERING, ELECTRICAL & ELECTRONIC
AMI corpus | Convolution | Neural networks | Hidden Markov models | Speech recognition | Acoustics | Vectors | distant speech recognition | meetings | convolutional neural networks | deep neural networks | Microphones | ENGINEERING, ELECTRICAL & ELECTRONIC
Journal Article
IEEE Transactions on Geoscience and Remote Sensing, ISSN 0196-2892, 07/2017, Volume 55, Issue 7, pp. 3639 - 3655
In recent years, vector-based machine learning algorithms, such as random forests, support vector machines, and 1-D convolutional neural networks, have shown...
Support vector machines | recurrent neural network (RNN) | deep learning | Recurrent neural networks | gated recurrent unit (GRU) | hyperspectral image classification | Logic gates | long short-term memory (LSTM) | Data models | Convolutional neural network (CNN) | Hyperspectral imaging | Deep learning | Gated recurrent unit (GRU) | Long short-term memory (LSTM) | Recurrent neural network (RNN) | Hyperspectral image classification | LSTM | IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY | SPECTRAL-SPATIAL CLASSIFICATION | ENGINEERING, ELECTRICAL & ELECTRONIC | GEOCHEMISTRY & GEOPHYSICS | REMOTE SENSING | FRAMEWORK | Neural networks | Usage | Machine learning | Forests | Divergence | Methodology | Pixels | Risks | Training | Classification | Computer architecture | Sequencing | Framework | Activation analysis | Data analysis | Aversion learning | Parameters | Categories | Data processing | Learning algorithms | Satellites | Algorithms | Serial learning | Reasoning | Image classification
Support vector machines | recurrent neural network (RNN) | deep learning | Recurrent neural networks | gated recurrent unit (GRU) | hyperspectral image classification | Logic gates | long short-term memory (LSTM) | Data models | Convolutional neural network (CNN) | Hyperspectral imaging | Deep learning | Gated recurrent unit (GRU) | Long short-term memory (LSTM) | Recurrent neural network (RNN) | Hyperspectral image classification | LSTM | IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY | SPECTRAL-SPATIAL CLASSIFICATION | ENGINEERING, ELECTRICAL & ELECTRONIC | GEOCHEMISTRY & GEOPHYSICS | REMOTE SENSING | FRAMEWORK | Neural networks | Usage | Machine learning | Forests | Divergence | Methodology | Pixels | Risks | Training | Classification | Computer architecture | Sequencing | Framework | Activation analysis | Data analysis | Aversion learning | Parameters | Categories | Data processing | Learning algorithms | Satellites | Algorithms | Serial learning | Reasoning | Image classification
Journal Article
IEEE Transactions on Industrial Electronics, ISSN 0278-0046, 11/2016, Volume 63, Issue 11, pp. 7067 - 7075
Early detection of the motor faults is essential and artificial neural networks are widely used for this purpose. The typical systems usually encapsulate two...
Induction motors | Convolution | Fault detection | Neural networks | motor current signature analysis (MCSA) | Feature extraction | Real-time systems | Convolutional neural networks (CNNs) | Mathematical model | SIGNAL | DIAGNOSIS | INSTRUMENTS & INSTRUMENTATION | DECOMPOSITION | BEARING DAMAGE DETECTION | SENSORLESS | MODEL | AUTOMATION & CONTROL SYSTEMS | ENGINEERING, ELECTRICAL & ELECTRONIC | Real-time control | Motors | Research
Induction motors | Convolution | Fault detection | Neural networks | motor current signature analysis (MCSA) | Feature extraction | Real-time systems | Convolutional neural networks (CNNs) | Mathematical model | SIGNAL | DIAGNOSIS | INSTRUMENTS & INSTRUMENTATION | DECOMPOSITION | BEARING DAMAGE DETECTION | SENSORLESS | MODEL | AUTOMATION & CONTROL SYSTEMS | ENGINEERING, ELECTRICAL & ELECTRONIC | Real-time control | Motors | Research
Journal Article
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN 0302-9743, 2015, Volume 8925, pp. 572 - 578
Conference Proceeding
IEEE Transactions on Geoscience and Remote Sensing, ISSN 0196-2892, 02/2017, Volume 55, Issue 2, pp. 645 - 657
We propose an end-to-end framework for the dense, pixelwise classification of satellite imagery with convolutional neural networks (CNNs). In our framework,...
Context | Training | convolutional neural networks (CNNs) | deep learning | Satellites | Neurons | Training data | Classification | Biological neural networks | Remote sensing | satellite images | MULTISCALE | IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY | SPECTRAL-SPATIAL CLASSIFICATION | ENGINEERING, ELECTRICAL & ELECTRONIC | GEOCHEMISTRY & GEOPHYSICS | HYPERSPECTRAL IMAGES | REMOTE SENSING | DEEP | SEGMENTATION | FRAMEWORK | Neural networks | Research | Artificial neural networks | Satellite sensing | Data | Imagery | Satellite imagery | Maps | Localization | Framework | Image classification | Computer Vision and Pattern Recognition |
Context | Training | convolutional neural networks (CNNs) | deep learning | Satellites | Neurons | Training data | Classification | Biological neural networks | Remote sensing | satellite images | MULTISCALE | IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY | SPECTRAL-SPATIAL CLASSIFICATION | ENGINEERING, ELECTRICAL & ELECTRONIC | GEOCHEMISTRY & GEOPHYSICS | HYPERSPECTRAL IMAGES | REMOTE SENSING | DEEP | SEGMENTATION | FRAMEWORK | Neural networks | Research | Artificial neural networks | Satellite sensing | Data | Imagery | Satellite imagery | Maps | Localization | Framework | Image classification | Computer Vision and Pattern Recognition |