Weakly Supervised Segmentation via Instance-aware Propagation

2021 ◽  
Author(s):  
Xin Huang ◽  
Qianshu Zhu ◽  
Yongtuo Liu ◽  
Shengfeng He
Keyword(s):  
Supervised Segmentation ◽  
Weakly Supervised

Optimal Scale of Hierarchical Image Segmentation with Scribbles Guidance for Weakly Supervised Semantic Segmentation

2021 ◽  
pp. 2154026
Author(s):  
Zaid Al-Huda ◽  
Donghai Zhai ◽  
Yan Yang ◽  
Riyadh Nazar Ali Algburi
Keyword(s):  
Image Segmentation ◽  
Graphical Model ◽  
Semantic Segmentation ◽  
Saliency Map ◽  
Training Data ◽  
Deep Convolutional Neural Networks ◽  
High Quality ◽  
Optimal Scale ◽  
Supervised Segmentation ◽  
Weakly Supervised

Deep convolutional neural networks (DCNNs) trained on the pixel-level annotated images have achieved improvements in semantic segmentation. Due to the high cost of labeling training data, their applications may have great limitation. However, weakly supervised segmentation approaches can significantly reduce human labeling efforts. In this paper, we introduce a new framework to generate high-quality initial pixel-level annotations. By using a hierarchical image segmentation algorithm to predict the boundary map, we select the optimal scale of high-quality hierarchies. In the initialization step, scribble annotations and the saliency map are combined to construct a graphic model over the optimal scale segmentation. By solving the minimal cut problem, it can spread information from scribbles to unmarked regions. In the training process, the segmentation network is trained by using the initial pixel-level annotations. To iteratively optimize the segmentation, we use a graphical model to refine segmentation masks and retrain the segmentation network to get more precise pixel-level annotations. The experimental results on Pascal VOC 2012 dataset demonstrate that the proposed framework outperforms most of weakly supervised semantic segmentation methods and achieves the state-of-the-art performance, which is [Formula: see text] mIoU.

scholarly journals Weakly Supervised Segmentation by a Deep Geodesic Prior

2019 ◽  
pp. 238-246 ◽  
Author(s):  
Aliasghar Mortazi ◽  
Naji Khosravan ◽  
Drew A. Torigian ◽  
Sila Kurugol ◽  
Ulas Bagci
Keyword(s):  
Supervised Segmentation ◽  
Weakly Supervised

An image-level weakly supervised segmentation method for No-service rail surface defect with size prior

2022 ◽  
Vol 165 ◽  
pp. 108334
Author(s):  
Defu Zhang ◽  
Kechen Song ◽  
Jing Xu ◽  
Hongwen Dong ◽  
Yunhui Yan
Keyword(s):  
Surface Defect ◽  
Segmentation Method ◽  
Supervised Segmentation ◽  
Weakly Supervised ◽  
Rail Surface Defect

scholarly journals A Conditional Random Field Weakly Supervised Segmentation Approach for Segmenting keratocytes Cells in Corneal Optical Coherence Tomography Images

2015 ◽  
Vol 1 (1) ◽  
Author(s):  
Ameneh Boroomand ◽  
Alexander Wong ◽  
Kostadinka Bizheva
Keyword(s):  
Random Field ◽  
Conditional Random Field ◽  
Automatic Segmentation ◽  
Cell Segmentation ◽  
Human Cornea ◽  
Optical Coherence ◽  
Sparse Model ◽  
Supervised Segmentation ◽  
Segmentation Approach ◽  
Weakly Supervised

<p>Keratocytes are vital for maintaining the overall health of human<br />cornea as they preserve the corneal transparency and help in healing<br />corneal injuries. Manual segmentation of keratocytes is challenging,<br />time consuming and also needs an expert. Here, we propose<br />a novel semi-automatic segmentation framework, called Conditional<br />Random FieldWeakly Supervised Segmentation (CRF-WSS)<br />to perform the keratocytes cell segmentation. The proposed framework<br />exploits the concept of dictionary learning in a sparse model<br />along with the Conditional Random Field (CRF) modeling to segment<br />keratocytes cells in Ultra High Resolution Optical Coherence<br />Tomography (UHR-OCT) images of human cornea. The results<br />show higher accuracy for the proposed CRF-WSS framework compare<br />to the other tested Supervised Segmentation (SS) andWeakly<br />Supervised Segmentation (WSS) methods.</p>

scholarly journals Weakly Supervised Segmentation of COVID19 Infection with Scribble Annotation on CT Images

2022 ◽  
Vol 122 ◽  
pp. 108341
Author(s):  
Xiaoming Liu ◽  
Quan Yuan ◽  
Yaozong Gao ◽  
Kelei He ◽  
Shuo Wang ◽  
...  
Keyword(s):  
Ct Images ◽  
Supervised Segmentation ◽  
Weakly Supervised

scholarly journals Cross-Image Region Mining with Region Prototypical Network for Weakly Supervised Segmentation

2021 ◽  
pp. 1-1
Author(s):  
Weide Liu ◽  
Xiangfei Kong ◽  
Tzu-Yi Hung ◽  
Guosheng Lin
Keyword(s):  
Image Region ◽  
Supervised Segmentation ◽  
Weakly Supervised
Sign in / Sign up

Export Citation Format

Share Document