An aerial image segmentation approach based on enhanced multi-scale convolutional neural network

2019 ◽  
Author(s):  
Xiang Li ◽  
Yuchen Jiang ◽  
Hu Peng ◽  
Shen Yin
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Convolutional Neural Network ◽  
Aerial Image ◽  
Multi Scale ◽  
Segmentation Approach

scholarly journals Multi-Scale and Multi-Branch Convolutional Neural Network for Retinal Image Segmentation

Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 365
Author(s):  
Yun Jiang ◽  
Wenhuan Liu ◽  
Chao Wu ◽  
Huixiao Yao
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Convolutional Neural Network ◽  
Retinal Image ◽  
Spatial Information ◽  
Multi Scale ◽  
Segmentation Methods ◽  
Feature Information ◽  
The Impact ◽  
Retinal Image Segmentation

The accurate segmentation of retinal images is a basic step in screening for retinopathy and glaucoma. Most existing retinal image segmentation methods have insufficient feature information extraction. They are susceptible to the impact of the lesion area and poor image quality, resulting in the poor recovery of contextual information. This also causes the segmentation results of the model to be noisy and low in accuracy. Therefore, this paper proposes a multi-scale and multi-branch convolutional neural network model (multi-scale and multi-branch network (MSMB-Net)) for retinal image segmentation. The model uses atrous convolution with different expansion rates and skip connections to reduce the loss of feature information. Receiving domains of different sizes captures global context information. The model fully integrates shallow and deep semantic information and retains rich spatial information. The network embeds an improved attention mechanism to obtain more detailed information, which can improve the accuracy of segmentation. Finally, the method of this paper was validated on the fundus vascular datasets, DRIVE, STARE and CHASE datasets, with accuracies/F1 of 0.9708/0.8320, 0.9753/0.8469 and 0.9767/0.8190, respectively. The effectiveness of the method in this paper was further validated on the optic disc visual cup DRISHTI-GS1 dataset with an accuracy/F1 of 0.9985/0.9770. Experimental results show that, compared with existing retinal image segmentation methods, our proposed method has good segmentation performance in all four benchmark tests.

scholarly journals An Advanced Road Structure Image Segmentation Method using Swt in Cnn

2019 ◽  
Vol 9 (2S3) ◽  
pp. 114-118
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
High Speed ◽  
Learning Phase ◽  
Convolution Neural Network ◽  
Aerial Images ◽  
Aerial Image ◽  
Segmentation Method ◽  
Stroke Width ◽  
Segmentation Approach

In this paper, the design of advanced road structure image segmentation approach using stroke width transformation (SWT) in convolution neural network (CNN) is proposed. The main intent of the proposed system is to acquire the aerial images for the vehicle. Basically, this image segmentation performs its operation in two forms they are operating phase and learning phase. Here the aerial image has enhanced by using the SWT transformation. Hence the main advantage of this proposes system is that it processes the entire operation in simple way with high speed. The SWT will capture the images of road areas in effective way. Hence the propose system has various features which will determine the color, width and many other.

scholarly journals A Dual-Path and Lightweight Convolutional Neural Network for High-Resolution Aerial Image Segmentation

2019 ◽  
Vol 8 (12) ◽  
pp. 582 ◽  
Author(s):  
Gang Zhang ◽  
Tao Lei ◽  
Yi Cui ◽  
Ping Jiang
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
High Resolution ◽  
Convolutional Neural Network ◽  
Feature Learning ◽  
Semantic Segmentation ◽  
Aerial Images ◽  
Aerial Image ◽  
Sensing Applications ◽  
Edge Path

Semantic segmentation on high-resolution aerial images plays a significant role in many remote sensing applications. Although the Deep Convolutional Neural Network (DCNN) has shown great performance in this task, it still faces the following two challenges: intra-class heterogeneity and inter-class homogeneity. To overcome these two problems, a novel dual-path DCNN, which contains a spatial path and an edge path, is proposed for high-resolution aerial image segmentation. The spatial path, which combines the multi-level and global context features to encode the local and global information, is used to address the intra-class heterogeneity challenge. For inter-class homogeneity problem, a Holistically-nested Edge Detection (HED)-like edge path is employed to detect the semantic boundaries for the guidance of feature learning. Furthermore, we improve the computational efficiency of the network by employing the backbone of MobileNetV2. We enhance the performance of MobileNetV2 with two modifications: (1) replacing the standard convolution in the last four Bottleneck Residual Blocks (BRBs) with atrous convolution; and (2) removing the convolution stride of 2 in the first layer of BRBs 4 and 6. Experimental results on the ISPRS Vaihingen and Potsdam 2D labeling dataset show that the proposed DCNN achieved real-time inference speed on a single GPU card with better performance, compared with the state-of-the-art baselines.

scholarly journals Unsupervised Palm Vein Image Segmentation

2020 ◽  
pp. paper40-1-paper40-12
Author(s):  
Ekaterina Safronova ◽  
Elena Pavelyeva
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Convolutional Neural Network ◽  
Network Architectures ◽  
Test Results ◽  
Principal Curvatures ◽  
Segmentation Approach ◽  
Vein Recognition ◽  
Palm Vein ◽  
Unsupervised Image Segmentation

In this article the new hybrid algorithm for palm vein image segmentation using convolutional neural network and principal curvatures is proposed. After palm vein image preprocessing vein structure is detected using unsupervised learning approach based on W-Net architecture, that ties together into a single autoencoder two fully convolutional neural network architectures, each simi-lar to the U-Net. Then segmentation results are improved using principal cur-vatures technique. Some vein points with highest maximum principal curva-ture values are selected, and the other vein points are found by moving from starting points along the direction of minimum principal curvature. To obtain the final vein image segmentation the result of intersection of the principal curvatures-based and neural network-based segmentations is taken. The evaluation of the proposed unsupervised image segmentation method based on palm vein recognition results using multilobe differential filters is given. Test results using CASIA multi-spectral palmprint image database show the effectiveness of the proposed segmentation approach.

Sign in / Sign up

Export Citation Format

Share Document