scholarly journals Efficient Eventual Consistency in Collaborative Image Annotation Framework

2015 ◽  
pp. 9-17
Author(s):  
Hafed Zarzour ◽  
Mokhtar Sellami
Keyword(s):  
Image Annotation ◽  
Eventual Consistency

Principles of Eventual Consistency

2015 ◽  
Author(s):  
Madhavan Ravichandhran ◽  
G. Ramalingam ◽  
Kapil Vaswani
Keyword(s):  
Eventual Consistency

Image Annotation with Bayesian Universal Background Model

2013 ◽  
Vol 39 (10) ◽  
pp. 1674
Author(s):  
Dong YANG ◽  
Xiu-Ling ZHOU ◽  
Ping GUO
Keyword(s):  
Image Annotation ◽  
Background Model ◽  
Universal Background Model

scholarly journals Putting Order in Strong Eventual Consistency

2019 ◽  
pp. 36-56
Author(s):  
Kevin De Porre ◽  
Florian Myter ◽  
Christophe De Troyer ◽  
Christophe Scholliers ◽  
Wolfgang De Meuter ◽  
...  
Keyword(s):  
Eventual Consistency

scholarly journals Automatic Hip Detection in Anteroposterior Pelvic Radiographs—A Labelless Practical Framework

2021 ◽  
Vol 11 (6) ◽  
pp. 522
Author(s):  
Feng-Yu Liu ◽  
Chih-Chi Chen ◽  
Chi-Tung Cheng ◽  
Cheng-Ta Wu ◽  
Chih-Po Hsu ◽  
...  
Keyword(s):  
Medical Image ◽  
Image Annotation ◽  
Region Of Interest ◽  
Confidence Score ◽  
Heterogeneous Data ◽  
Single Shot ◽  
Hip Joints ◽  
Pelvic Radiographs ◽  
Wide Range ◽  
Average Confidence

Automated detection of the region of interest (ROI) is a critical step in the two-step classification system in several medical image applications. However, key information such as model parameter selection, image annotation rules, and ROI confidence score are essential but usually not reported. In this study, we proposed a practical framework of ROI detection by analyzing hip joints seen on 7399 anteroposterior pelvic radiographs (PXR) from three diverse sources. We presented a deep learning-based ROI detection framework utilizing a single-shot multi-box detector with a customized head structure based on the characteristics of the obtained datasets. Our method achieved average intersection over union (IoU) = 0.8115, average confidence = 0.9812, and average precision with threshold IoU = 0.5 (AP50) = 0.9901 in the independent testing set, suggesting that the detected hip regions appropriately covered the main features of the hip joints. The proposed approach featured flexible loose-fitting labeling, customized model design, and heterogeneous data testing. We demonstrated the feasibility of training a robust hip region detector for PXRs. This practical framework has a promising potential for a wide range of medical image applications.

scholarly journals Generation and Annotation of Simulation-Real Ship Images for Convolutional Neural Networks Training and Testing

2021 ◽  
Vol 11 (13) ◽  
pp. 5931
Author(s):  
Ji’an You ◽  
Zhaozheng Hu ◽  
Chao Peng ◽  
Zhiqiang Wang
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Image Annotation ◽  
Three Dimensional ◽  
Image Data ◽  
Detection Algorithm ◽  
Simulation Software ◽  
High Quality ◽  
Annotation Method ◽  
Detection Of Objects

Large amounts of high-quality image data are the basis and premise of the high accuracy detection of objects in the field of convolutional neural networks (CNN). It is challenging to collect various high-quality ship image data based on the marine environment. A novel method based on CNN is proposed to generate a large number of high-quality ship images to address this. We obtained ship images with different perspectives and different sizes by adjusting the ships’ postures and sizes in three-dimensional (3D) simulation software, then 3D ship data were transformed into 2D ship image according to the principle of pinhole imaging. We selected specific experimental scenes as background images, and the target ships of the 2D ship images were superimposed onto the background images to generate “Simulation–Real” ship images (named SRS images hereafter). Additionally, an image annotation method based on SRS images was designed. Finally, the target detection algorithm based on CNN was used to train and test the generated SRS images. The proposed method is suitable for generating a large number of high-quality ship image samples and annotation data of corresponding ship images quickly to significantly improve the accuracy of ship detection. The annotation method proposed is superior to the annotation methods that label images with the image annotation software of Label-me and Label-img in terms of labeling the SRS images.

Sign in / Sign up

Export Citation Format

Share Document