Semantic image segmentation using fully convolutional neural networks with multi-scale images and multi-scale dilated convolutions

2018 ◽  
Vol 77 (14) ◽  
pp. 18689-18707 ◽  
Author(s):  
Duc My Vo ◽  
Sang-Woong Lee
Keyword(s):  
Neural Networks ◽  
Image Segmentation ◽  
Convolutional Neural Networks ◽  
Semantic Image Segmentation ◽  
Multi Scale ◽  
Fully Convolutional Neural Networks

scholarly journals Multi-scale fully convolutional neural networks for histopathology image segmentation: From nuclear aberrations to the global tissue architecture

2021 ◽  
Vol 70 ◽  
pp. 101996
Author(s):  
Rüdiger Schmitz ◽  
Frederic Madesta ◽  
Maximilian Nielsen ◽  
Jenny Krause ◽  
Stefan Steurer ◽  
...  
Keyword(s):  
Neural Networks ◽  
Image Segmentation ◽  
Convolutional Neural Networks ◽  
Tissue Architecture ◽  
Multi Scale ◽  
Fully Convolutional Neural Networks

Cell detection in pathology and microscopy images with multi-scale fully convolutional neural networks

2018 ◽  
Vol 21 (6) ◽  
pp. 1721-1743 ◽  
Author(s):  
Xipeng Pan ◽  
Dengxian Yang ◽  
Lingqiao Li ◽  
Zhenbing Liu ◽  
Huihua Yang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Cell Detection ◽  
Multi Scale ◽  
Fully Convolutional Neural Networks ◽  
Microscopy Images

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 Use of convolutional neural networks for semantic image segmentation across different computing systems

2020 ◽  
Author(s):  
Andmorgan Fisher ◽  
Timothy Middleton ◽  
Jonathan Cotugno ◽  
Elena Sava ◽  
Laura Clemente-Harding ◽  
...  
Keyword(s):  
Neural Networks ◽  
Image Segmentation ◽  
Convolutional Neural Networks ◽  
Computing Systems ◽  
Semantic Image Segmentation

scholarly journals Segmentation of Lung Ct Images using Cascaded Fully Convolutional Neural Networks

2019 ◽  
Vol 8 (2) ◽  
pp. 5472-5474
Keyword(s):  
Neural Networks ◽  
Image Segmentation ◽  
Convolutional Neural Networks ◽  
Automatic Segmentation ◽  
Ct Images ◽  
Fully Convolutional Neural Networks ◽  
Proposal A ◽  
Fully Automatic ◽  
Lung Ct

Interpretation of CT Lung images by the radiologist can be enhanced to a greater extent by automatic segmentation of nodules. The efficiency of this interpretation depends on the completeness and non-ambiguousness of the CT Lung images. Here, a fully automatic cascaded basis was proposed for CT Lung image segmentation. In this proposal a customized FCN was used feature extractions exploration from many visual scales and differentiate anatomy with a thick forecast map. Widespread experimental outcomes demonstrate that this technique can address the incompleteness in boundary and this technique can achieve best accuracy in segmentation of Lung CT Images when compared to other techniques which address the same area

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