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 Aerial Image Segmentation with Clustering Using Fireworks Algorithm

2019 ◽  
Vol 4 (1) ◽  
pp. 19
Author(s):  
Muhammad Hariz Arasy ◽  
Suyanto Suyanto ◽  
Kurniawan Nur Ramadhani
Keyword(s):  
Image Segmentation ◽  
Aerial Images ◽  
Aerial Image ◽  
Segmentation Method ◽  
Unsupervised Segmentation ◽  
Local Optima ◽  
Fireworks Algorithm ◽  
Segmentation Methods ◽  
Segmentation Quality ◽  
Unsupervised Image Segmentation

Aerial images has different data characteristics when compared to other types of images. An aerial image usually contains small insignificant objects that can cause errors in the unsupervised segmentation method. K-means clustering, one of the widely used unsupervised image segmentation methods, is highly vulnerable to local optima. In this study, Adaptive Fireworks Algorithm (AFWA) is proposed as an alternative to the K-means algorithm in optimizing the clustering process in the cluster-based segmentation method. AFWA is then applied to perform aerial image segmentation and the results are compared with K-means. Based on the comparison using Probabilistic Rand Index (PRI) and Variation of Information (VI) evaluation metrics, AFWA produces an overall better segmentation quality.

scholarly journals Hybrid Iris Segmentation Method Based on CNN and Principal Curvatures

2020 ◽  
pp. paper31-1-paper31-10
Author(s):  
Varvara Tikhonova ◽  
Elena Pavelyeva
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Image Segmentation ◽  
Convolutional Neural Networks ◽  
Segmentation Method ◽  
Iris Segmentation ◽  
Principal Curvatures ◽  
Mathematical Methods ◽  
Iris Image ◽  
Segmentation Approach

In this article the new hybrid iris image segmentation method based on convolutional neural networks and mathematical methods is proposed. Iris boundaries are found using modified Daugman’s method. Two UNet-based convolutional neural networks are used for iris mask detection. The first one is used to predict the preliminary iris mask including the areas of the pupil, eyelids and some eyelashes. The second neural network is applied to the enlarged image to specify thin ends of eyelashes. Then the principal curvatures method is used to combine the predicted by neural networks masks and to detect eyelashes correctly. The pro- posed segmentation algorithm is tested using images from CASIA IrisV4 Interval database. The results of the proposed method are evaluated by the Intersection over Union, Recall and Precision metrics. The average metrics values are 0.922, 0.957 and 0.962, respectively. The proposed hy- brid iris image segmentation approach demonstrates an improvement in comparison with the methods that use only neural networks.

scholarly journals Active Learning with Bayesian UNet for Efficient Semantic Image Segmentation

2021 ◽  
Vol 7 (2) ◽  
pp. 37
Author(s):  
Isah Charles Saidu ◽  
Lehel Csató
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Active Learning ◽  
Convolutional Neural Network ◽  
Medical Image ◽  
Segmentation Method ◽  
Semantic Image Segmentation ◽  
Batch Normalization ◽  
Set Up ◽  
Image Datasets

We present a sample-efficient image segmentation method using active learning, we call it Active Bayesian UNet, or AB-UNet. This is a convolutional neural network using batch normalization and max-pool dropout. The Bayesian setup is achieved by exploiting the probabilistic extension of the dropout mechanism, leading to the possibility to use the uncertainty inherently present in the system. We set up our experiments on various medical image datasets and highlight that with a smaller annotation effort our AB-UNet leads to stable training and better generalization. Added to this, we can efficiently choose from an unlabelled dataset.

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