scholarly journals Convolutional neural network in the images colorization problem

2019 ◽  
pp. 340-353
Author(s):  
M V Bulygin ◽  
M M Gayanova ◽  
A M Vulfin ◽  
A D Kirillova ◽  
R Ch Gayanov
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Image Processing ◽  
Processing System ◽  
Black And White ◽  
Convolutional Networks ◽  
Advantages And Disadvantages ◽  
The Neural Network ◽  
Image Processing Algorithms ◽  
Processing Algorithms

Object of the research are modern structures and architectures of neural networks for image processing. Goal of the work is improving the existing image processing algorithms based on the extraction and compression of features using neural networks using the colorization of black and white images as an example. The subject of the work is the algorithms of neural network image processing using heterogeneous convolutional networks in the colorization problem. The analysis of image processing algorithms with the help of neural networks is carried out, the structure of the neural network processing system for image colorization is developed, colorization algorithms are developed and implemented. To analyze the proposed algorithms, a computational experiment was conducted and conclusions were drawn about the advantages and disadvantages of each of the algorithms.

scholarly journals Convolutional Neural Network for the Semantic Segmentation of Remote Sensing Images

2021 ◽  
Vol 26 (1) ◽  
pp. 200-215
Author(s):  
Muhammad Alam ◽  
Jian-Feng Wang ◽  
Cong Guangpei ◽  
LV Yunrong ◽  
Yuanfang Chen
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Neural Networks ◽  
Image Processing ◽  
Deep Learning ◽  
Semantic Segmentation ◽  
Natural Scene ◽  
Remote Sensing Images ◽  
Advantages And Disadvantages ◽  
Target Segmentation

AbstractIn recent years, the success of deep learning in natural scene image processing boosted its application in the analysis of remote sensing images. In this paper, we applied Convolutional Neural Networks (CNN) on the semantic segmentation of remote sensing images. We improve the Encoder- Decoder CNN structure SegNet with index pooling and U-net to make them suitable for multi-targets semantic segmentation of remote sensing images. The results show that these two models have their own advantages and disadvantages on the segmentation of different objects. In addition, we propose an integrated algorithm that integrates these two models. Experimental results show that the presented integrated algorithm can exploite the advantages of both the models for multi-target segmentation and achieve a better segmentation compared to these two models.

scholarly journals Computer image processing and neural network technology for boiler thermal energy diagnosis

2020 ◽  
Vol 24 (5 Part B) ◽  
pp. 3059-3068
Author(s):  
Qinghong Wu
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Thermal Energy ◽  
Processing System ◽  
Heat Energy ◽  
Image Feature ◽  
The Neural Network ◽  
Computer Image Processing ◽  
On Line ◽  
Parameter Values

The paper uses the flame image processing technology to diagnose the furnace flame combustion achieve the measurement of boiler heat energy. The paper obtains the combustion image of the flame image processing system, and extracts the flame image characteristics of the boiler thermal energy diagnosis, constructs the neural network model of the boiler thermal energy diagnosis, and trains and tests the extracted flame image feature parameter values as the input of the neural network. A rough diagnosis of the boiler?s thermal energy is obtained while predicting the state of combustion. According to the research results, a boiler thermal energy diagnosis system was designed and tested on the boiler of 200 MW unit. The experimental results confirmed the applicability of the system, which can realize on-line monitoring of boiler heat energy and evaluate the combustion situation.

scholarly journals AK-DL: A Shallow Neural Network Model for Diagnosing Actinic Keratosis with Better Performance than Deep Neural Networks

Diagnostics ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 217
Author(s):  
Liyang Wang ◽  
Angxuan Chen ◽  
Yan Zhang ◽  
Xiaoya Wang ◽  
Yu Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Actinic Keratosis ◽  
Medical Image ◽  
Histopathological Examination ◽  
Diagnostic System ◽  
Medical Image Processing ◽  
Deep Cnn ◽  
Image Processing Algorithms ◽  
Processing Algorithms

Actinic keratosis (AK) is one of the most common precancerous skin lesions, which is easily confused with benign keratosis (BK). At present, the diagnosis of AK mainly depends on histopathological examination, and ignorance can easily occur in the early stage, thus missing the opportunity for treatment. In this study, we designed a shallow convolutional neural network (CNN) named actinic keratosis deep learning (AK-DL) and further developed an intelligent diagnostic system for AK based on the iOS platform. After data preprocessing, the AK-DL model was trained and tested with AK and BK images from dataset HAM10000. We further compared it with mainstream deep CNN models, such as AlexNet, GoogLeNet, and ResNet, as well as traditional medical image processing algorithms. Our results showed that the performance of AK-DL was better than the mainstream deep CNN models and traditional medical image processing algorithms based on the AK dataset. The recognition accuracy of AK-DL was 0.925, the area under the receiver operating characteristic curve (AUC) was 0.887, and the training time was only 123.0 s. An iOS app of intelligent diagnostic system was developed based on the AK-DL model for accurate and automatic diagnosis of AK. Our results indicate that it is better to employ a shallow CNN in the recognition of AK.

Bubble Image Processing Algorithm and Application in Vacuum Casting Equipment

2010 ◽  
Vol 426-427 ◽  
pp. 260-264 ◽  
Author(s):  
Yuan Yuan Liu ◽  
Z.F. Chi ◽  
J.W. Wang ◽  
Hai Guang Zhang ◽  
Qing Xi Hu
Keyword(s):  
Image Processing ◽  
Surface Quality ◽  
Manufacturing Process ◽  
Casting Process ◽  
Processing Algorithm ◽  
Image Processing Algorithm ◽  
Vacuum Casting ◽  
Advantages And Disadvantages ◽  
Image Processing Algorithms ◽  
Processing Algorithms

Bubbles in the manufacturing process are common. The bubbles often lead to the decrease of the product’s surface quality and internal performance. This paper summarized the published researches and applications of the detection and processing for bubble images, of which the advantages and disadvantages were also presented. Based on the above mentioned results, this paper then proposed a new bubble image processing algorithm for vacuum casting process, in which the characteristics of the bubbles in vacuum casting process and the problems possibly caused in detail were analyzed. According to the characteristics of bubbles in vacuum casting process, an image processing algorithms was designed using Matlab. The simulation result showed the efficiency of the proposed algorithm.

scholarly journals Using high-performance deep learning platform to accelerate object detection

2019 ◽  
pp. 354-360 ◽  
Author(s):  
S O Stepanenko ◽  
P Y Yakimov
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Image Processing ◽  
Processing Speed ◽  
High Performance ◽  
Object Classification ◽  
High Accuracy ◽  
Learning Platform ◽  
The Neural Network

Object classification with use of neural networks is extremely current today. YOLO is one of the most often used frameworks for object classification. It produces high accuracy but the processing speed is not high enough especially in conditions of limited performance of a computer. This article researches use of a framework called NVIDIA TensorRT to optimize YOLO with the aim of increasing the image processing speed. Saving efficiency and quality of the neural network work TensorRT allows us to increase the processing speed using an optimization of the architecture and an optimization of calculations on a GPU.

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