Results 31 to 40 of about 58,990 (298)

Convolutional neural network-based automated segmentation and labeling of the lumbar spine X-ray

Journal of Craniovertebral Junction and Spine, 2021
Purpose: This study investigated the segmentation metrics of different segmentation networks trained on 730 manually annotated lateral lumbar spine X-rays to test the generalization ability and robustness which are the basis of clinical decision support ...
Sandor Konya, T R Sai Natarajan, Hassan Allouch, Kais Abu Nahleh, Omneya Yakout Dogheim, Heinrich Boehm   +5 more
doaj   +1 more source

SS-IPLE: Semantic Segmentation of Electric Power Corridor Scene and Individual Power Line Extraction From UAV-Based Lidar Point Cloud

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023
Key objects’ semantic segmentation and power line extraction from the electric power corridor point cloud are critical steps in power line inspection.
Xiuning Liu, Feng Shuang, Yong Li, Liqiang Zhang, Xingwen Huang, Jianchuang Qin   +5 more
doaj   +1 more source

Bladder Semantic Segmentation

Proceedings of the 30th International Conference on Computer Graphics and Machine Vision (GraphiCon 2020). Part 2, 2020
Obtaining information about the shape and volume of the bladder plays a significant role in determining the pathologies of this organ. To collect the relevant data, the first thing to do is to separate the bladder from the background on the ultrasound image.
Vadim Chernyshev, Alexander Gromov, Anton Konushin, Anna Mesheryakova   +3 more
openaire   +1 more source

Parallel Fully Convolutional Network for Semantic Segmentation

IEEE Access, 2021
Fully convolutional networks (FCNs) have been widely applied for dense classification tasks such as semantic segmentation. As a large number of works based on FCNs are proposed, various semantic segmentation models have been improved significantly ...
Jian Ji, Xiaocong Lu, Mai Luo, Minghui Yin, Qiguang Miao, Xiangzeng Liu   +5 more
doaj   +1 more source

Real-Time Semantic Understanding and Segmentation of Urban Scenes for Vehicle Visual Sensors by Optimized DCNN Algorithm

Applied Sciences, 2022
The modern urban environment is becoming more and more complex. In helping us identify surrounding objects, vehicle vision sensors rely more on the semantic segmentation ability of deep learning networks.
Yanyi Li, Jian Shi, Yuping Li
doaj   +1 more source

Class Semantic Enhancement Network for Semantic Segmentation

SSRN Electronic Journal, 2022
Hualiang Wang, Xiaoxuan He, Yongwen Long, Jianhong Bai, Tao Ouyang, Yuanjia Huang, Mengqiu Zhou, Haoji Hu   +7 more
openalex   +2 more sources

Semantic Amodal Segmentation [PDF]

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017
Common visual recognition tasks such as classification, object detection, and semantic segmentation are rapidly reaching maturity, and given the recent rate of progress, it is not unreasonable to conjecture that techniques for many of these problems will approach human levels of performance in the next few years.
Yan Zhu, Yuandong Tian, Dimitris Metaxas, Piotr Dollar   +3 more
openaire   +1 more source

Meta-Seg: A Generalized Meta-Learning Framework for Multi-Class Few-Shot Semantic Segmentation

IEEE Access, 2019
Semantic segmentation performs pixel-wise classification for given images, which can be widely used in autonomous driving, robotics, medical diagnostics and etc.
Zhiying Cao, Tengfei Zhang, Wenhui Diao, Yue Zhang, Xiaode Lyu, Kun Fu, Xian Sun   +6 more
doaj   +1 more source

Morpho-orthographic segmentation without semantics [PDF]

Psychonomic Bulletin & Review, 2015
Masked priming studies have repeatedly provided evidence for a form-based morpho-orthographic segmentation mechanism that blindly decomposes any word with the mere appearance of morphological complexity (e.g., corn + er). This account has been called into question by Baayen et al.
Elisabeth, Beyersmann, Johannes C, Ziegler, Anne, Castles, Max, Coltheart, Yvette, Kezilas, Jonathan, Grainger   +5 more
openaire   +2 more sources

An unsupervised semantic segmentation method that combines the ImSE-Net model with SLICm superpixel optimization

International Journal of Digital Earth
In the field of remote sensing, using a large amount of labeled image data to supervise the training of fully convolutional networks for the semantic segmentation of images is expensive.
Zenan Yang, Haipeng Niu, Xiaoxuan Wang, Liang Huang, Kui Yang   +4 more
doaj   +1 more source