scholarly journals Towards resource-frugal deep convolutional neural networks for hyperspectral image segmentation

2020 ◽  
Vol 73 ◽  
pp. 102994 ◽  
Author(s):  
Jakub Nalepa ◽  
Marek Antoniak ◽  
Michal Myller ◽  
Pablo Ribalta Lorenzo ◽  
Michal Marcinkiewicz
Keyword(s):  
Neural Networks ◽  
Image Segmentation ◽  
Convolutional Neural Networks ◽  
Hyperspectral Image ◽  
Deep Convolutional Neural Networks

scholarly journals Semantic Image Segmentation with Deep Convolutional Neural Networks and Quick Shift

Symmetry ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 427 ◽  
Author(s):  
Sanxing Zhang ◽  
Zhenhuan Ma ◽  
Gang Zhang ◽  
Tao Lei ◽  
Rui Zhang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Image Segmentation ◽  
Convolutional Neural Networks ◽  
Semantic Segmentation ◽  
Autonomous Driving ◽  
Input Image ◽  
Feature Representation ◽  
Segmentation Algorithm ◽  
Deep Convolutional Neural Networks ◽  
Semantic Image Segmentation

Semantic image segmentation, as one of the most popular tasks in computer vision, has been widely used in autonomous driving, robotics and other fields. Currently, deep convolutional neural networks (DCNNs) are driving major advances in semantic segmentation due to their powerful feature representation. However, DCNNs extract high-level feature representations by strided convolution, which makes it impossible to segment foreground objects precisely, especially when locating object boundaries. This paper presents a novel semantic segmentation algorithm with DeepLab v3+ and super-pixel segmentation algorithm-quick shift. DeepLab v3+ is employed to generate a class-indexed score map for the input image. Quick shift is applied to segment the input image into superpixels. Outputs of them are then fed into a class voting module to refine the semantic segmentation results. Extensive experiments on proposed semantic image segmentation are performed over PASCAL VOC 2012 dataset, and results that the proposed method can provide a more efficient solution.

scholarly journals Deep convolutional neural networks for multi-modality isointense infant brain image segmentation

NeuroImage ◽  
2015 ◽  
Vol 108 ◽  
pp. 214-224 ◽  
Author(s):  
Wenlu Zhang ◽  
Rongjian Li ◽  
Houtao Deng ◽  
Li Wang ◽  
Weili Lin ◽  
...  
Keyword(s):  
Neural Networks ◽  
Image Segmentation ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
Brain Image ◽  
Infant Brain ◽  
Brain Image Segmentation

Accurate and automatic tooth image segmentation model with deep convolutional neural networks and level set method

2021 ◽  
Vol 419 ◽  
pp. 108-125
Author(s):  
Yunyun Yang ◽  
Ruicheng Xie ◽  
Wenjing Jia ◽  
Zhaoyang Chen ◽  
Yunna Yang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Image Segmentation ◽  
Level Set Method ◽  
Convolutional Neural Networks ◽  
Level Set ◽  
Deep Convolutional Neural Networks

scholarly journals Deep Convolutional Neural Networks for Hyperspectral Image Classification

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Wei Hu ◽  
Yangyu Huang ◽  
Li Wei ◽  
Fan Zhang ◽  
Hengchao Li
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Hyperspectral Image ◽  
Image Data ◽  
Classification Performance ◽  
Spectral Signature ◽  
Support Vector ◽  
Data Sets ◽  
Deep Convolutional Neural Networks ◽  
Visual Tasks

Recently, convolutional neural networks have demonstrated excellent performance on various visual tasks, including the classification of common two-dimensional images. In this paper, deep convolutional neural networks are employed to classify hyperspectral images directly in spectral domain. More specifically, the architecture of the proposed classifier contains five layers with weights which are the input layer, the convolutional layer, the max pooling layer, the full connection layer, and the output layer. These five layers are implemented on each spectral signature to discriminate against others. Experimental results based on several hyperspectral image data sets demonstrate that the proposed method can achieve better classification performance than some traditional methods, such as support vector machines and the conventional deep learning-based methods.

scholarly journals Hyperspectral image classification using deep convolutional neural networks

2017 ◽  
Vol 3 (1) ◽  
Author(s):  
Zilong Zhong ◽  
Jonathan Li
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Hyperspectral Image ◽  
Hyperspectral Imagery ◽  
Learning Models ◽  
Hyperspectral Image Classification ◽  
Deep Convolutional Neural Networks ◽  
Spectral Bands

The prevailing framework consisted of complex feature extractors following by conventional classifiers. Nevertheless, the high spatial and high spectral dimensionality of each pixel in the hyperspectral imagery hinders the development of hyperspectral image classification. Fortunately, since 2012, deep learning models, which can extract the hierarchical features of large amounts of daily three-channel optical images, have emerged as a better alternative to their shallow learning counterparts. Within all deep learning models, convolutional neural networks (CNNs) exhibit convincing and stunning ability to process a huge mass of data. In this paper, the CNNs have been adopted as an end-to-end pixelwise scheme to classify the pixels of hyperspectral imagery, in which each pixel contains hundreds of continuous spectral bands. According to the preliminarily qualitative and quantitative results, the existing CNN models achieve promising classification accuracy and process effectively and robustly on the University of Pavia dataset.

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