Adaptive Interferometric Radar Image Processing by Using Complex-Valued Neural Network

2003 ◽  
pp. 277-301 ◽  
Author(s):  
Andriyan Bayu Suksmono ◽  
Akira Hirose
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Radar Image ◽  
Complex Valued

Hailstone Classifier Based on Back Propagation Neural Network

2013 ◽  
Vol 433-435 ◽  
pp. 685-690
Author(s):  
Xiang Yang Liu ◽  
Hui Song Wan ◽  
Yuan Yuan Zhang ◽  
Shu Ming Jiang
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Image Feature ◽  
Radar Image ◽  
Data Support ◽  
Color Texture

The Back Propagation (BP) neural network was used for the construction of the hailstone classifier. Firstly, the database of the radar image feature was constructed. Through the image processing, the color, texture, shape and other dimensional features should be extracted and saved as the characteristic database to provide data support for the follow-up work. Secondly, Through the BP neural network, a machine for hail classifications can be built to achieve the hail samples auto-classification.

Qubit Neural Network

2009 ◽  
pp. 325-351 ◽  
Author(s):  
Nobuyuki Matsui ◽  
Haruhiko Nishimura ◽  
Teijiro Isokawa
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Image Processing ◽  
Pattern Recognition ◽  
Quantum Computing ◽  
Computational Efficiency ◽  
Bench Mark ◽  
Computational Power ◽  
Quantum Bit ◽  
Complex Valued

Recently, quantum neural networks have been explored as one of the candidates for improving the computational efficiency of neural networks. In this chapter, after giving a brief review of quantum computing, the authors introduce our qubit neural network, which is a multi-layered neural network composed of quantum bit neurons. In this description, it is indispensable to use the complex-valued representation, which is based on the concept of quantum bit (qubit). By means of the simulations in solving the parity check problems as a bench mark examination, we show that the computational power of the qubit neural network is superior to that of the conventional complex-valued and real-valued neural networks. Furthermore, the authors explore its applications such as image processing and pattern recognition. Thus they clarify that this model outperforms the conventional neural networks.

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 Diabetic Retinal Grading Using Attention-Based Bilinear Convolutional Neural Network and Complement Cross Entropy

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 816
Author(s):  
Pingping Liu ◽  
Xiaokang Yang ◽  
Baixin Jin ◽  
Qiuzhan Zhou
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Diabetic Retinopathy ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Network Model ◽  
Rapid Development ◽  
Image Data ◽  
Lesion Detection ◽  
Great Success

Diabetic retinopathy (DR) is a common complication of diabetes mellitus (DM), and it is necessary to diagnose DR in the early stages of treatment. With the rapid development of convolutional neural networks in the field of image processing, deep learning methods have achieved great success in the field of medical image processing. Various medical lesion detection systems have been proposed to detect fundus lesions. At present, in the image classification process of diabetic retinopathy, the fine-grained properties of the diseased image are ignored and most of the retinopathy image data sets have serious uneven distribution problems, which limits the ability of the network to predict the classification of lesions to a large extent. We propose a new non-homologous bilinear pooling convolutional neural network model and combine it with the attention mechanism to further improve the network’s ability to extract specific features of the image. The experimental results show that, compared with the most popular fundus image classification models, the network model we proposed can greatly improve the prediction accuracy of the network while maintaining computational efficiency.

Sign in / Sign up

Export Citation Format

Share Document