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Numerical Coordinate Regression with Convolutional Neural Networks [article]

Aiden Nibali, Zhen He, Stuart Morgan, Luke Prendergast
2018 arXiv   pre-print
Existing convolutional neural network-based solutions to this problem either take a heatmap matching approach or regress to coordinates with a fully connected output layer.  ...  We study deep learning approaches to inferring numerical coordinates for points of interest in an input image.  ...  Main idea We introduce a new differentiable layer for adapting fully convolutional networks (FCNs) to coordinate regression.  ... 
arXiv:1801.07372v2 fatcat:apj6baocjzgghlard5bmj6esvi

Semantic Recognition of Signed Language Using Convolutional Neural Network

Lanzhong Wang
2017 Innovative Computing Information and Control Express Letters, Part B: Applications  
Second, skin color filtered images are used to train a fast convolutional neural network for hand region detection.  ...  The detection is converted into a regression problem which leads to more efficient detection results.  ...  Conclusion.In this paper we treat the image detection as a regression problem. Convolutional neural network is used to solve the regression problem with reliable performance  ... 
doi:10.24507/icicelb.08.08.1211 fatcat:4tjgcsthgbeuxb3rwg4nnii63u

Predicting Impact of COVID-19 on Crude Oil Price Image with Directed Acyclic Graph Deep Convolution Neural Network

David O. Oyewola, Akomolafe Femi Augustine
2021 Journal of Robotics and Control (JRC)  
In this study, we propose image transformation of time series crude oil price by incorporating Directed Acyclic Graph to Convolutional Neural Network (DAG) based on image processing characteristics.  ...  The results show that integrating DAG with CNN improves the prediction accuracy by 14.18%. DAG perform best with an accuracy of 99.16%, sensitivity of 100% and specificity of 99.19%.  ...  The coordinated paths every convolution layer on regression follow is demonstrated in Figure 5 , begin with an input image layer and end with regression layer.  ... 
doi:10.18196/jrc.2261 fatcat:xesnmo6tcbedjhwfbvn72mxxym

Deep neural networks for human pose estimation from a very low resolution depth image

Piotr Szczuko
2019 Multimedia tools and applications  
deep convolutional neural networks.  ...  The work presented in the paper is dedicated to determining and evaluating the most efficient neural network architecture applied as a multiple regression network localizing human body joints in 3D space  ...  Publisher's note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.  ... 
doi:10.1007/s11042-019-7433-7 fatcat:qomz7yc25rfpvjr76iks67lahi

A deep learning approach replacing the finite difference method for in situ stress prediction

Wenli Gao, Xinming Lu, Yanjun Peng, Liang Wu
2020 IEEE Access  
Compared with some classical prediction methods including linear regression analysis and a deep neural network, the mean squared error of our proposed algorithm is as low as 0.059866%, which is lower than  ...  In this paper, we propose a deep learning (DL) architecture called the enhance-and-split feature capsule network embedded in fully convolutional neural networks (ES-Caps-FCN) to predict the in situ stress  ...  THE DESCRIPTION OF THE NEURAL NETWORK STRUCTURE USED IN OUR DL ARCHITECTURE 1) THREE-DIMENSIONAL CONVOLUTIONAL NEURAL NETWORK(3D CNN) A convolutional neural network [38] is a standard solution for regression  ... 
doi:10.1109/access.2020.2977880 fatcat:td6eoa37mrawvlqkthlbrqg3ga

Fully Convolutional Neural Network Prediction Method for Aerostatic Performance of Bluff Bodies Based on Consistent Shape Description

Ke Li, Hai Li, Shaopeng Li, Zengshun Chen
2022 Applied Sciences  
This paper proposes a novel fully convolutional neural network model that enables rapid prediction from shape to aerostatic performance.  ...  Its main innovations are: (1) The proposal of a new shape description method in which the shape is described by the combination of the wall distance field and the space coordinate field, which can efficiently  ...  A fully convolutional network, with some improvements, is selected as the neural network.  ... 
doi:10.3390/app12063147 fatcat:feh25owncjgc7plqed5s56jb7u

A Single Target Grasp Detection Network Based on Convolutional Neural Network

Longzhi Zhang, Dongmei Wu, Nian Zhang
2021 Computational Intelligence and Neuroscience  
Grasp detection based on convolutional neural network has gained some achievements. However, overfitting of multilayer convolutional neural network still exists and leads to poor detection precision.  ...  To acquire high detection accuracy, a single target grasp detection network that generalizes the fitting of angle and position, based on the convolution neural network, is put forward here.  ...  However, with some success of grasp detection based on convolutional neural network in theories and applications, for grasp detection network inherence itself, overfitting in multilayer convolutional neural  ... 
doi:10.1155/2021/5512728 fatcat:vnuj5fhzqbemtcepkjoq6s6hdm

Markerless Rat Behavior Quantification With Cascade Neural Network

Tianlei Jin, Feng Duan, Zhenyu Yang, Shifan Yin, Xuyi Chen, Yu Liu, Qingyu Yao, Fengzeng Jian
2020 Frontiers in Neurorobotics  
Secondly, we designed the cascade convolution network (CCN) and cascade hourglass network (CHN), which are two structures to extract features of the images.  ...  Three coordinate calculation methods-fully connected regression (FCR), heatmap maximum position (HMP), and heatmap integral regression (HIR)-were used to locate the coordinates of the landmark points.  ...  FIGURE 6 | 6 The results of the landmark point estimation with three coordinate calculation methods (cascade convolution network, CCN).  ... 
doi:10.3389/fnbot.2020.570313 pmid:33192436 pmcid:PMC7652788 fatcat:r4vvojzngvhallfma6zcsfregu

Machine Learning Models for Corn Yield Prediction: A Survey of Literature

Guiping Hu
2020 International Journal of Environmental Sciences & Natural Resources  
) [8] and convolutional neural network (CNN) (Khaki et al., 2020) models.  ...  memory (LSTM)*, LASSO, random forest 870 8.20% Khaki et al. [9] 1980-2018 County 2018 Convolutional neural network + recurrent neural network (CNN- RNN)*, random forest, deep fully connected  ... 
doi:10.19080/ijesnr.2020.25.556161 fatcat:2xumtu4rcvbrjhcpgvzih5dsda

Recognition of handwritten characters using deep convolution neural network

S. Arivazhagan, M. Arun, D. Rathina
2021 Journal of the National Science Foundation of Sri Lanka  
In this research work, Deep Convolution Neural Network (DCNN) has been used instead of hand-crafted features from the handwritten characters, to automatically learn the best features for this task.  ...  The recognition rate for the proposed DCNN provides better results when compared with the other schemes.  ...  Convolution operation in convolution neural network Batch normalization is the technique to coordinate the update of multiple layers in the CNN.  ... 
doi:10.4038/jnsfsr.v49i4.9825 fatcat:dlsu55i7wndifn7axw7j3kkacm

Real-Time Grasp Detection Using Convolutional Neural Networks [article]

Joseph Redmon, Anelia Angelova
2015 arXiv   pre-print
We present an accurate, real-time approach to robotic grasp detection based on convolutional neural networks.  ...  Our network performs single-stage regression to graspable bounding boxes without using standard sliding window or region proposal techniques.  ...  ACKNOWLEDGEMENTS We would like to thank Alex Krizevsky for helping us with model construction and pretraining, and for helping us customize his cuda-convnet2 code.  ... 
arXiv:1412.3128v2 fatcat:mle5tftsbbg55a5uaqu7lnklm4

Automatic Feature Learning Method for Detection of Retinal Landmarks

Baidaa Al-Bander, Waleed Al-Nuaimy, Majid A. Al-Taee, Ali Al-Ataby, Yalin Zheng
2016 2016 9th International Conference on Developments in eSystems Engineering (DeSE)  
The proposed method, which is based on deep convolutional neural networks (CNN) does not depend the visual appearance or anatomical features of the retinal landmarks.  ...  The CNN is trained using an existing dataset images along with their annotated locations of the foveal and OD centres. Performance of the network is evaluated using Root Mean Square Error (RMSE).  ...  Convolutional neural network architecture  ... 
doi:10.1109/dese.2016.4 fatcat:mzgjghvyyjhrpmh53u37zvnkda

Paediatric Bone Age Assessment Using Deep Convolutional Neural Networks [chapter]

Vladimir I. Iglovikov, Alexander Rakhlin, Alexandr A. Kalinin, Alexey A. Shvets
2018 Lecture Notes in Computer Science  
Our approach utilizes several deep learning architectures: U-Net, ResNet-50, and custom VGG-style neural networks trained end-to-end.  ...  Key points model is implemented as a deep convolutional neural network, inspired by a popular VGG family of models [ ], with a regression output.  ...  VGG-style neural network architectures for regression (top) and classification (bottom) tasks. Fig. . .  ... 
doi:10.1007/978-3-030-00889-5_34 fatcat:tijufgifvbdjvbaa4zcl767dle

Leveraging Temporal Information for 3D Trajectory Estimation of Space Objects

Mohamed Adel Musallam, Miguel Ortiz Del Castillo, Kassem Al Ismaeil, Marcos Damian Perez, Djamila Aouada
2021 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)  
The first stage estimates the 2D location of the space object using a convolution neural network.  ...  In the next stage, the 2D locations are lifted to 3D space, using temporal convolution neural network that enforces the temporal coherence over the estimated 3D locations.  ...  We note that TCN is a variation of convolutional neural network for sequence modelling tasks.  ... 
doi:10.1109/iccvw54120.2021.00425 fatcat:shl43q2zcfctnlfzy4lwj7lruu

Real-time grasp detection using convolutional neural networks

Joseph Redmon, Anelia Angelova
2015 2015 IEEE International Conference on Robotics and Automation (ICRA)  
We present an accurate, real-time approach to robotic grasp detection based on convolutional neural networks.  ...  Our network performs single-stage regression to graspable bounding boxes without using standard sliding window or region proposal techniques.  ...  ACKNOWLEDGEMENTS We would like to thank Alex Krizevsky for helping us with model construction and pretraining, and for helping us customize his cuda-convnet2 code.  ... 
doi:10.1109/icra.2015.7139361 dblp:conf/icra/RedmonA15 fatcat:e5dfse7vrnf77kl5f4357wtopm
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