scholarly journals Regression in Convolutional Neural Networks applied to Plant Leaf Counting

2019 ◽  
Author(s):  
Neemias Bucéli Da Silva ◽  
Wesley Nunes Gonçalves
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Convolutional Neural Networks ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Plant Leaves ◽  
Training Process ◽  
Plant Leaf ◽  
Image Dataset ◽  
Regression Problems

Recent studies have shown that computer vision techniques developed to boost the count of plant leaves brings significant improvements. In this paper, a proposal was presented for plant leaf counting using Convolutional Neural Networks (CNNs). To accomplish the training process, CNNs architectures were adapted to solve regression problems. To evaluate the proposed method, an image dataset with 810 images of three species (Arabidopsis, Tobacco and one mutation) was used. The results showed that Xception architecture obtained the best results with R2 of 0.96 and MAE (mean absolute error) of 0.46.

scholarly journals Extrapolation of Ventricular Activation Times From Sparse Electroanatomical Data Using Graph Convolutional Neural Networks

2021 ◽  
Vol 12 ◽  
Author(s):  
Felix Meister ◽  
Tiziano Passerini ◽  
Chloé Audigier ◽  
Èric Lluch ◽  
Viorel Mihalef ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Mean Absolute Error ◽  
Sampling Strategy ◽  
Absolute Error ◽  
Statistical Shape Model ◽  
Border Zone ◽  
Statistical Shape ◽  
Sparse Measurements ◽  
Activation Times

Electroanatomic mapping is the gold standard for the assessment of ventricular tachycardia. Acquiring high resolution electroanatomic maps is technically challenging and may require interpolation methods to obtain dense measurements. These methods, however, cannot recover activation times in the entire biventricular domain. This work investigates the use of graph convolutional neural networks to estimate biventricular activation times from sparse measurements. Our method is trained on more than 15,000 synthetic examples of realistic ventricular depolarization patterns generated by a computational electrophysiology model. Using geometries sampled from a statistical shape model of biventricular anatomy, diverse wave dynamics are induced by randomly sampling scar and border zone distributions, locations of initial activation, and tissue conduction velocities. Once trained, the method accurately reconstructs biventricular activation times in left-out synthetic simulations with a mean absolute error of 3.9 ms ± 4.2 ms at a sampling density of one measurement sample per cm2. The total activation time is matched with a mean error of 1.4 ms ± 1.4 ms. A significant decrease in errors is observed in all heart zones with an increased number of samples. Without re-training, the network is further evaluated on two datasets: (1) an in-house dataset comprising four ischemic porcine hearts with dense endocardial activation maps; (2) the CRT-EPIGGY19 challenge data comprising endo- and epicardial measurements of 5 infarcted and 6 non-infarcted swines. In both setups the neural network recovers biventricular activation times with a mean absolute error of less than 10 ms even when providing only a subset of endocardial measurements as input. Furthermore, we present a simple approach to suggest new measurement locations in real-time based on the estimated uncertainty of the graph network predictions. The model-guided selection of measurement locations allows to reduce by 40% the number of measurements required in a random sampling strategy, while achieving the same prediction error. In all the tested scenarios, the proposed approach estimates biventricular activation times with comparable or better performance than a personalized computational model and significant runtime advantages.

scholarly journals An Analysis on Very Deep Convolutional Neural Networks: Problems and Solutions

2021 ◽  
Vol 66 (1) ◽  
pp. 5
Author(s):  
T.-V. Pricope
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Convolutional Neural Networks ◽  
Computation Time ◽  
Deep Convolutional Neural Networks ◽  
Batch Normalization ◽  
Image Dataset ◽  
Problems And Solutions ◽  
Hidden Patterns

Neural Networks have become a powerful tool in computer vision because of the recent breakthroughs in computation time and model architecture. Very deep models allow for better deciphering of the hidden patterns in the data; however, training them successfully is not a trivial problem, because of the notorious vanishing/exploding gradient problem. We illustrate this problem on VGG models, with 8 and 38 hidden layers, on the CIFAR100 image dataset, where we visualize how the gradients evolve during training. We explore known solutions to this problem like Batch Normalization (BatchNorm) or Residual Networks (ResNets), explaining the theory behind them. Our experiments show that the deeper model suffers from the vanishing gradient problem, but BatchNorm and ResNets do solve it. The employed solutions slighly improve the performance of shallower models as well, yet, the fixed deeper models outperform them.  

scholarly journals Spatial Detection of Vehicles in Images using Convolutional Neural Networks and Stereo Matching

2016 ◽  
Vol 2 (1) ◽  
Author(s):  
Jeremy Pinto ◽  
Nolan Lunscher ◽  
Georges Younes ◽  
David Abou Chacra ◽  
Henry Leopold ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Stereo Matching ◽  
Mean Absolute Error ◽  
Depth Estimation ◽  
Absolute Error ◽  
Stereo Images ◽  
Matching Method ◽  
Spatial Detection ◽  
State Of Art

Convolutional Neural Networks combined with a state of the artstereo-matching method are used to find and estimate the 3D positionof vehicles in pairs of stereo images. Pixel positions of vehiclesare first estimated separately in pairs of stereo images usinga Convolutional Neural Network for regression. These coordinatesare then combined with a state-of-art stereo-matching method todetermine the depth, and thus the 3D location, of the vehicles. Weshow in this paper that cars can be detected with a combined accuracyof approximately 90% with a tolerated radius error of 5%,and a Mean Absolute Error of 5.25m on depth estimation for carsup to 50m away.

scholarly journals Image classification using Deep learning

2018 ◽  
Vol 7 (2.7) ◽  
pp. 614 ◽  
Author(s):  
M Manoj krishna ◽  
M Neelima ◽  
M Harshali ◽  
M Venu Gopala Rao
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Image Processing ◽  
Computer Vision ◽  
Deep Learning ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Classical Problem

The image classification is a classical problem of image processing, computer vision and machine learning fields. In this paper we study the image classification using deep learning. We use AlexNet architecture with convolutional neural networks for this purpose. Four test images are selected from the ImageNet database for the classification purpose. We cropped the images for various portion areas and conducted experiments. The results show the effectiveness of deep learning based image classification using AlexNet.  

scholarly journals A Hybrid GA-PSO Method for Evolving Architecture and Short Connections of Deep Convolutional Neural Networks

2020 ◽  
Author(s):  
B Wang ◽  
Y Sun ◽  
Bing Xue ◽  
Mengjie Zhang
Keyword(s):  
Neural Networks ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Network Architecture ◽  
Learning Task ◽  
Fixed Number ◽  
Learning Rate ◽  
Current Layer ◽  
Training Process ◽  
Deep Convolutional Neural Networks

© 2019, Springer Nature Switzerland AG. Image classification is a difficult machine learning task, where Convolutional Neural Networks (CNNs) have been applied for over 20 years in order to solve the problem. In recent years, instead of the traditional way of only connecting the current layer with its next layer, shortcut connections have been proposed to connect the current layer with its forward layers apart from its next layer, which has been proved to be able to facilitate the training process of deep CNNs. However, there are various ways to build the shortcut connections, it is hard to manually design the best shortcut connections when solving a particular problem, especially given the design of the network architecture is already very challenging. In this paper, a hybrid evolutionary computation (EC) method is proposed to automatically evolve both the architecture of deep CNNs and the shortcut connections. Three major contributions of this work are: Firstly, a new encoding strategy is proposed to encode a CNN, where the architecture and the shortcut connections are encoded separately; Secondly, a hybrid two-level EC method, which combines particle swarm optimisation and genetic algorithms, is developed to search for the optimal CNNs; Lastly, an adjustable learning rate is introduced for the fitness evaluations, which provides a better learning rate for the training process given a fixed number of epochs. The proposed algorithm is evaluated on three widely used benchmark datasets of image classification and compared with 12 peer Non-EC based competitors and one EC based competitor. The experimental results demonstrate that the proposed method outperforms all of the peer competitors in terms of classification accuracy.

Optimasi Akurasi Metode Convolutional Neural Network untuk Identifikasi Jenis Sampah

2021 ◽  
Vol 5 (2) ◽  
pp. 312-318
Author(s):  
Rima Dias Ramadhani ◽  
Afandi Nur Aziz Thohari ◽  
Condro Kartiko ◽  
Apri Junaidi ◽  
Tri Ginanjar Laksana ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Computer Science ◽  
No Value ◽  
Convolutional Neural Networks ◽  
Training Process ◽  
Indonesian Government ◽  
Speed Up ◽  
Model Training

Waste is goods / materials that have no value in the scope of production, where in some cases the waste is disposed of carelessly and can damage the environment. The Indonesian government in 2019 recorded waste reaching 66-67 million tons, which is higher than the previous year, which was 64 million tons. Waste is differentiated based on its type, namely organic and anorganic waste. In the field of computer science, the process of sensing the type waste can be done using a camera and the Convolutional Neural Networks (CNN) method, which is a type of neural network that works by receiving input in the form of images. The input will be trained using CNN architecture so that it will produce output that can recognize the object being inputted. This study optimizes the use of the CNN method to obtain accurate results in identifying types of waste. Optimization is done by adding several hyperparameters to the CNN architecture. By adding hyperparameters, the accuracy value is 91.2%. Meanwhile, if the hyperparameter is not used, the accuracy value is only 67.6%. There are three hyperparameters used to increase the accuracy value of the model. They are dropout, padding, and stride. 20% increase in dropout to increase training overfit. Whereas padding and stride are used to speed up the model training process.

A Guide to Convolutional Neural Networks for Computer Vision

2018 ◽  
Vol 8 (1) ◽  
pp. 1-207 ◽  
Author(s):  
Salman Khan ◽  
Hossein Rahmani ◽  
Syed Afaq Ali Shah ◽  
Mohammed Bennamoun
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Convolutional Neural Networks
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