Automated corneal endothelium image segmentation in the presence of cornea guttata via convolutional neural networks

2020 ◽  
Author(s):  
Juan S. Sierra ◽  
Jesús Pineda Castro ◽  
Eduardo Viteri ◽  
Daniela Rueda ◽  
Beatriz Tibaduiza ◽  
...  
Keyword(s):  
Neural Networks ◽  
Image Segmentation ◽  
Convolutional Neural Networks ◽  
Corneal Endothelium ◽  
Cornea Guttata

Hierarchical fusion convolutional neural networks for SAR image segmentation

2021 ◽  
Vol 147 ◽  
pp. 115-123
Author(s):  
Yinyin Jiang ◽  
Ming Li ◽  
Peng Zhang ◽  
Xiaofeng Tan ◽  
Wanying Song
Keyword(s):  
Neural Networks ◽  
Image Segmentation ◽  
Convolutional Neural Networks ◽  
Sar Image ◽  
Sar Image Segmentation

Dermoscopic image segmentation method based on convolutional neural networks

2021 ◽  
Vol 66 (2) ◽  
pp. 89
Author(s):  
Ugur Erkan ◽  
Dang Ngoc Hoang Thanh ◽  
Le Thi Thanh ◽  
V.B. Surya Prasath ◽  
Aditya Khamparia
Keyword(s):  
Neural Networks ◽  
Image Segmentation ◽  
Convolutional Neural Networks ◽  
Segmentation Method ◽  
Dermoscopic Image

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 Semantic and Structural Image Segmentation for Prosthetic Vision

2019 ◽  
Vol 7 ◽  
Author(s):  
Melani Sanchez Garcia ◽  
Rubén Martínez Cantín ◽  
José J. Guerrero
Keyword(s):  
Neural Networks ◽  
Image Segmentation ◽  
Convolutional Neural Networks ◽  
Vision System ◽  
Relevant Information ◽  
Indoor Environments ◽  
Schematic Representation ◽  
New Approach ◽  
Structural Image

We present a new approach to build a schematic representation of indoor environments for phosphene images. The proposed method combines a variety of convolutional neural networks for extracting and conveying relevant information about the scene such as structural informative edges of the environment and silhouettes of segmented objects. Experiments were conducted with normal sighted subjects with a Simulated Prosthetic Vision system.

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