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[PDF] Top 20 Deep Background Subtraction with Scene-Specific Convolutional Neural Networks

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Deep Background Subtraction with Scene-Specific Convolutional Neural Networks

Deep Background Subtraction with Scene-Specific Convolutional Neural Networks

... Abstract—Background subtraction is usually based on low- level or hand-crafted features such as raw color components, gradients, or local binary ...a background subtraction algorithm based on ... Voir le document complet

5

Predicting the Propagation of Acoustic Waves using Deep Convolutional Neural Networks

Predicting the Propagation of Acoustic Waves using Deep Convolutional Neural Networks

... of neural networks, in particular CNN, has never been reported for the propagation of acoustic ...theoretical background on the errors made by CNNs, in particular when applied to a recurrent task as ... Voir le document complet

19

Semantic Background Subtraction

Semantic Background Subtraction

... of deep neural networks within the computer vision community and the access to large labeled training datasets have dramati- cally improved the performance of semantic segmentation al- gorithms [11, ... Voir le document complet

6

Graphlet Count Estimation via Convolutional Neural Networks

Graphlet Count Estimation via Convolutional Neural Networks

... However, enumerating exact graphlet count is inherently difficult and computational expensive because the number of graphlets grows exponentially large as the graph size and/or graphlet size k grow [3]. To deal ... Voir le document complet

4

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af en Deep Learning in Spiking Neural Networks Deep learning in spiking neural networks

... years, deep learning has revolutionized the field of machine learning, for computer vision in ...a deep (multilayer) artificial neural network (ANN) is trained in a supervised manner using ...Spiking ... Voir le document complet

24

Deep Convolutional Networks are Hierarchical Kernel Machines

Deep Convolutional Networks are Hierarchical Kernel Machines

... layer with the transformations of a few templates under a ...the convolutional layers of Deep Convolutional Networks (DCNs) as well as the non-convolutional layers (when the ... Voir le document complet

17

Real-Time Semantic Background Subtraction

Real-Time Semantic Background Subtraction

... Semantic background subtraction (SBS) has been shown to improve the performance of most background subtraction algorithms by combining them with semantic information, derived from a ... Voir le document complet

5

Deep Captioning with Multimodal Recurrent Neural Networks (m-RNN)

Deep Captioning with Multimodal Recurrent Neural Networks (m-RNN)

... very deep structure in computer vision field (Nair & Hinton (2010); Krizhevsky et ...RNN with sigmoid function, the vanishing or exploding gradient problem appears since even the simplest RNN model can ... Voir le document complet

16

Singing voice detection with deep recurrent neural networks

Singing voice detection with deep recurrent neural networks

... Artificial Neural Networks (ANNs) ...signal with vocal components enhanced by a Harmonic/Percussive Source Separation (HPSS) technique proposed by Ono et ... Voir le document complet

6

Linear and Deformable Image Registration with 3D Convolutional Neural Networks

Linear and Deformable Image Registration with 3D Convolutional Neural Networks

... to generate sampling coordinates for each output voxel (G N (p)). We can let the network calculate these sampling points directly. Such a choice would however require the network to produce feature maps with large ... Voir le document complet

11

Multichannel audio source separation with deep neural networks

Multichannel audio source separation with deep neural networks

... where deep neural networks (DNNs) are used to model the source spectra and combined with the classical multichannel Gaussian model to exploit the spatial ... Voir le document complet

14

Music feature maps with convolutional neural networks for music genre classification

Music feature maps with convolutional neural networks for music genre classification

... The recent papers show that spectrograms obtained from audio signal have been successfully applied to MGC [2], [22]. As texture is the main visual content found in spectrograms, different types of texture have been used ... Voir le document complet

6

Background subtraction by disparity warping

Background subtraction by disparity warping

... Using two or more cameras, the method models the background by using disparity maps, which warp the primary image to each of the additional auxiliary images.. Duri[r] ... Voir le document complet

44

Detection of Cardiac Arrhythmias From Varied Length Multichannel Electrocardiogram Recordings Using Deep Convolutional Neural Networks

Detection of Cardiac Arrhythmias From Varied Length Multichannel Electrocardiogram Recordings Using Deep Convolutional Neural Networks

... mean squared error that gives poor results. Random seeds are fixed in order to make experiments reproducible when training on GPUs. Implementation: DL learning experiments are con- ducted with the use of Python ... Voir le document complet

5

On Recurrent and Deep Neural Networks

On Recurrent and Deep Neural Networks

... overlaps with seven di↵erent papers that I published while doing my studies, and, some of the content of the thesis has been borrowed directly from these ...collaborations with di↵erent ...training, ... Voir le document complet

267

Clinical event prediction and understanding with deep neural networks

Clinical event prediction and understanding with deep neural networks

... In addition, we compare these representations along with both long short-term memory networks (LSTM) and convolutional neural networks (CNN) for prediction of five i[r] ... Voir le document complet

56

On Deep Multiscale Recurrent Neural Networks

On Deep Multiscale Recurrent Neural Networks

... 5.3 Contributions In this work, we proposed to replace only the decoder to use characters instead of subword units. The motivation of this setting was to provide a transparent comparison between two different types of ... Voir le document complet

144

Spatio-temporal convolutional neural networks for failure prediction

Spatio-temporal convolutional neural networks for failure prediction

... 2.1 Features The information collected for our use case is in the form of log messages in full text, similar to syslog messages of linux systems and collected by each subsystem. As text is not a suitable input for the ... Voir le document complet

5

Creating Synthetic Radar Imagery Using Convolutional Neural Networks

Creating Synthetic Radar Imagery Using Convolutional Neural Networks

... intensity, with lightning flashes as white plus symbols in the area outside the coverage of weather ...using convolutional neural networks and the methods described in this year’s Laboratory ... Voir le document complet

2

Fine-grained breast cancer classification with bilinear convolutional neural networks (BCNNS)

Fine-grained breast cancer classification with bilinear convolutional neural networks (BCNNS)

... acquired with a resolution of 700 × 460 using different magnification factors of 40×, 100×, 200×, and 400× (Table ...framework with a Tesla K40C in an Ubuntu ...dataset. With pretraining, we could ... Voir le document complet

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