scholarly journals DEL: Deep Embedding Learning for Efficient Image Segmentation

2018 ◽  
Author(s):  
Yun Liu ◽  
Peng-Tao Jiang ◽  
Vahan Petrosyan ◽  
Shi-Jie Li ◽  
Jiawang Bian ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Similarity Measure ◽  
Convolutional Network ◽  
Trade Off ◽  
Fully Convolutional Network ◽  
Deep Embedding ◽  
Segmentation Algorithms ◽  
Efficiency And Effectiveness ◽  
Novel Method ◽  
Good Trade

Image segmentation has been explored for many years and still remains a crucial vision problem. Some efficient or accurate segmentation algorithms have been widely used in many vision applications. However, it is difficult to design a both efficient and accurate image segmenter. In this paper, we propose a novel method called DEL (deep embedding learning) which can efficiently transform superpixels into image segmentation. Starting with the SLIC superpixels, we train a fully convolutional network to learn the feature embedding space for each superpixel. The learned feature embedding corresponds to a similarity measure that measures the similarity between two adjacent superpixels. With the deep similarities, we can directly merge the superpixels into large segments. The evaluation results on BSDS500 and PASCAL Context demonstrate that our approach achieves a good trade-off between efficiency and effectiveness. Specifically, our DEL algorithm can achieve comparable segments when compared with MCG but is much faster than it, i.e. 11.4fps vs. 0.07fps.

High-accuracy image segmentation for lactating sows using a fully convolutional network

2018 ◽  
Vol 176 ◽  
pp. 36-47 ◽  
Author(s):  
Aqing Yang ◽  
Huasheng Huang ◽  
Chan Zheng ◽  
Xunmu Zhu ◽  
Xiaofan Yang ◽  
...  
Keyword(s):  
Image Segmentation ◽  
High Accuracy ◽  
Convolutional Network ◽  
Fully Convolutional Network ◽  
Lactating Sows

scholarly journals TISNet-Enhanced Fully Convolutional Network with Encoder-Decoder Structure for Tongue Image Segmentation in Traditional Chinese Medicine

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Xiaodong Huang ◽  
Hui Zhang ◽  
Li Zhuo ◽  
Xiaoguang Li ◽  
Jing Zhang
Keyword(s):  
Image Segmentation ◽  
Receptive Field ◽  
Positional Information ◽  
Dice Similarity Coefficient ◽  
Segmentation Method ◽  
Feature Maps ◽  
Convolutional Network ◽  
Fully Convolutional Network ◽  
Segmentation Methods ◽  
Feature Pyramid

Extracting the tongue body accurately from a digital tongue image is a challenge for automated tongue diagnoses, as the blurred edge of the tongue body, interference of pathological details, and the huge difference in the size and shape of the tongue. In this study, an automated tongue image segmentation method using enhanced fully convolutional network with encoder-decoder structure was presented. In the frame of the proposed network, the deep residual network was adopted as an encoder to obtain dense feature maps, and a Receptive Field Block was assembled behind the encoder. Receptive Field Block can capture adequate global contextual prior because of its structure of the multibranch convolution layers with varying kernels. Moreover, the Feature Pyramid Network was used as a decoder to fuse multiscale feature maps for gathering sufficient positional information to recover the clear contour of the tongue body. The quantitative evaluation of the segmentation results of 300 tongue images from the SIPL-tongue dataset showed that the average Hausdorff Distance, average Symmetric Mean Absolute Surface Distance, average Dice Similarity Coefficient, average precision, average sensitivity, and average specificity were 11.2963, 3.4737, 97.26%, 95.66%, 98.97%, and 98.68%, respectively. The proposed method achieved the best performance compared with the other four deep-learning-based segmentation methods (including SegNet, FCN, PSPNet, and DeepLab v3+). There were also similar results on the HIT-tongue dataset. The experimental results demonstrated that the proposed method can achieve accurate tongue image segmentation and meet the practical requirements of automated tongue diagnoses.

scholarly journals Fully convolutional network for rice seedling and weed image segmentation at the seedling stage in paddy fields

PLoS ONE ◽  
2019 ◽  
Vol 14 (4) ◽  
pp. e0215676 ◽  
Author(s):  
Xu Ma ◽  
Xiangwu Deng ◽  
Long Qi ◽  
Yu Jiang ◽  
Hongwei Li ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Rice Seedling ◽  
Seedling Stage ◽  
Paddy Fields ◽  
Convolutional Network ◽  
Fully Convolutional Network

scholarly journals Segmenting the Semi-Conductive Shielding Layer of Cable Slice Images Using the Convolutional Neural Network

Polymers ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 2085
Author(s):  
Wen Zhu ◽  
Fei Dong ◽  
Beiping Hou ◽  
Wesley Kenniard Takudzwa Gwatidzo ◽  
Le Zhou ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Essential Role ◽  
Structural Parameters ◽  
Receptive Fields ◽  
Structural Parameter ◽  
Pixel Classification ◽  
Convolutional Network ◽  
Fully Convolutional Network ◽  
Novel Method

Being an important part of aerial insulated cable, the semiconductive shielding layer is made of a typical polymer material and can improve the cable transmission effects; the structural parameters will affect the cable quality directly. Then, the image processing of the semiconductive layer plays an essential role in the structural parameter measurements. However, the semiconductive layer images are often disturbed by the cutting marks, which affect the measurements seriously. In this paper, a novel method based on the convolutional neural network is proposed for image segmentation. In our proposed strategy, a deep fully convolutional network with a skip connection algorithm is defined as the main framework. The inception structure and residual connection are employed to fuse features extracted from the receptive fields with different sizes. Finally, an improved weighted loss function and refined algorithm are utilized for pixel classification. Experimental results show that our proposed algorithm achieves better performance than the current algorithms.

Deep Learning Models for Semantic Multi-Modal Medical Image Segmentation

2020 ◽  
pp. 12-30
Author(s):  
V. R. S. Mani
Keyword(s):  
Neural Networks ◽  
Image Segmentation ◽  
Deep Learning ◽  
Medical Image Segmentation ◽  
Learning Models ◽  
Convolutional Network ◽  
Fully Convolutional Network ◽  
Comprehensive Picture ◽  
Application Methods ◽  
Segmentation Task

In this chapter, the author paints a comprehensive picture of different deep learning models used in different multi-modal image segmentation tasks. This chapter is an introduction for those new to the field, an overview for those working in the field, and a reference for those searching for literature on a specific application. Methods are classified according to the different types of multi-modal images and the corresponding types of convolution neural networks used in the segmentation task. The chapter starts with an introduction to CNN topology and describes various models like Hyper Dense Net, Organ Attention Net, UNet, VNet, Dilated Fully Convolutional Network, Transfer Learning, etc.

scholarly journals Learning Multi-level Region Consistency with Dense Multi-label Networks for Semantic Segmentation

2017 ◽  
Author(s):  
Tong Shen ◽  
Guosheng Lin ◽  
Chunhua Shen ◽  
Ian Reid
Keyword(s):  
Image Segmentation ◽  
Semantic Segmentation ◽  
Image Understanding ◽  
Network Module ◽  
Convolutional Network ◽  
Semantic Image Segmentation ◽  
Fully Convolutional Network ◽  
Multi Level ◽  
Different Levels ◽  
Semantic Labelling

Semantic image segmentation is a fundamental task in image understanding. Per-pixel semantic labelling of an image benefits greatly from the ability to consider region consistency both locally and globally. However, many Fully Convolutional Network based methods do not impose such consistency, which may give rise to noisy and implausible predictions. We address this issue by proposing a dense multi-label network module that is able to encourage the region consistency at different levels. This simple but effective module can be easily integrated into any semantic segmentation systems. With comprehensive experiments, we show that the dense multi-label can successfully remove the implausible labels and clear the confusion so as to boost the performance of semantic segmentation systems.

scholarly journals A novel cell segmentation method for developing embryos using machine learning

2018 ◽  
Author(s):  
Rikifumi Ota ◽  
Takahiro Ide ◽  
Tatsuo Michiue
Keyword(s):  
Machine Learning ◽  
Cell Shape ◽  
State Of The Art ◽  
Cell Segmentation ◽  
Segmentation Method ◽  
Convolutional Network ◽  
Fully Convolutional Network ◽  
Segmented Image ◽  
Novel Method ◽  
Better Than

AbstractCell segmentation is crucial in the study of morphogenesis in developing embryos, but it is limited in its accuracy. In this study we provide a novel method for cell segmentation using machine-learning, termed Cell Segmenter using Machine Learning (CSML). CSML performed better than state-of-the-art methods, such as RACE and watershed, in the segmentation of ectodermal cells in the Xenopus embryo. CSML required only one whole embryo image for training a Fully Convolutional Network classifier, and it took 20 seconds per each image to return a segmented image. To validate its accuracy, we compared it to other methods in assessing several indicators of cell shape. We also examined the generality by measuring its performance in segmenting independent images. Our data demonstrates the superiority of CSML, and we expect this application to significantly improve efficiency in cell shape studies.

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