Landmark Detection for Docking Tasks

2017 ◽  
pp. 3-13 ◽  
Author(s):  
Francisco Ferreira ◽  
Héber Sobreira ◽  
Germano Veiga ◽  
António Moreira
Keyword(s):  
Landmark Detection

Geolocation based Landmark Detection and Annotation

2013 ◽  
Vol 133 (1) ◽  
pp. 142-149
Author(s):  
Atsushi Shimada ◽  
Vincent Charvillat ◽  
Hajime Nagahara ◽  
Rin-ichiro Taniguchi
Keyword(s):  
Landmark Detection

Contactless palm landmark detection and localization on mobile devices

2016 ◽  
Vol 2016 (7) ◽  
pp. 1-6
Author(s):  
Yaqi Wang ◽  
Liangrui Peng ◽  
Shengjin Wang ◽  
Xiaoqing Ding
Keyword(s):  
Mobile Devices ◽  
Landmark Detection ◽  
Detection And Localization

scholarly journals Robust Facial Landmark Detection via Aggregation on Geometrically Manipulated Faces

2020 ◽  
Author(s):  
Seyed Mehdi Iranmanesh ◽  
Ali Dabouei ◽  
Sobhan Soleymani ◽  
Hadi Kazemi ◽  
Nasser M. Nasrabadi
Keyword(s):  
Landmark Detection ◽  
Facial Landmark Detection ◽  
Facial Landmark

scholarly journals Evaluating effects of focal length and viewing angle in a comparison of recent face landmark and alignment methods

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Xiang Li ◽  
Jianzheng Liu ◽  
Jessica Baron ◽  
Khoa Luu ◽  
Eric Patterson
Keyword(s):  
Focal Length ◽  
Ground Truth ◽  
Detection Methods ◽  
Viewing Angle ◽  
Deep Convolutional Neural Networks ◽  
Landmark Detection ◽  
High Performing ◽  
Detection Techniques ◽  
And Performance ◽  
Lens Focal Length

AbstractRecent attention to facial alignment and landmark detection methods, particularly with application of deep convolutional neural networks, have yielded notable improvements. Neither these neural-network nor more traditional methods, though, have been tested directly regarding performance differences due to camera-lens focal length nor camera viewing angle of subjects systematically across the viewing hemisphere. This work uses photo-realistic, synthesized facial images with varying parameters and corresponding ground-truth landmarks to enable comparison of alignment and landmark detection techniques relative to general performance, performance across focal length, and performance across viewing angle. Recently published high-performing methods along with traditional techniques are compared in regards to these aspects.

scholarly journals Deep learning for cephalometric landmark detection: systematic review and meta-analysis

2021 ◽  
Author(s):  
Falk Schwendicke ◽  
Akhilanand Chaurasia ◽  
Lubaina Arsiwala ◽  
Jae-Hong Lee ◽  
Karim Elhennawy ◽  
...  
Keyword(s):  
Systematic Review ◽  
Deep Learning ◽  
Meta Analysis ◽  
High Accuracy ◽  
Risk Of Bias ◽  
Automated Detection ◽  
Reference Test ◽  
Landmark Detection ◽  
Future Studies ◽  
Using Data

Abstract Objectives Deep learning (DL) has been increasingly employed for automated landmark detection, e.g., for cephalometric purposes. We performed a systematic review and meta-analysis to assess the accuracy and underlying evidence for DL for cephalometric landmark detection on 2-D and 3-D radiographs. Methods Diagnostic accuracy studies published in 2015-2020 in Medline/Embase/IEEE/arXiv and employing DL for cephalometric landmark detection were identified and extracted by two independent reviewers. Random-effects meta-analysis, subgroup, and meta-regression were performed, and study quality was assessed using QUADAS-2. The review was registered (PROSPERO no. 227498). Data From 321 identified records, 19 studies (published 2017–2020), all employing convolutional neural networks, mainly on 2-D lateral radiographs (n=15), using data from publicly available datasets (n=12) and testing the detection of a mean of 30 (SD: 25; range.: 7–93) landmarks, were included. The reference test was established by two experts (n=11), 1 expert (n=4), 3 experts (n=3), and a set of annotators (n=1). Risk of bias was high, and applicability concerns were detected for most studies, mainly regarding the data selection and reference test conduct. Landmark prediction error centered around a 2-mm error threshold (mean; 95% confidence interval: (–0.581; 95 CI: –1.264 to 0.102 mm)). The proportion of landmarks detected within this 2-mm threshold was 0.799 (0.770 to 0.824). Conclusions DL shows relatively high accuracy for detecting landmarks on cephalometric imagery. The overall body of evidence is consistent but suffers from high risk of bias. Demonstrating robustness and generalizability of DL for landmark detection is needed. Clinical significance Existing DL models show consistent and largely high accuracy for automated detection of cephalometric landmarks. The majority of studies so far focused on 2-D imagery; data on 3-D imagery are sparse, but promising. Future studies should focus on demonstrating generalizability, robustness, and clinical usefulness of DL for this objective.

A review on cephalometric landmark detection techniques

2021 ◽  
Vol 66 ◽  
pp. 102486
Author(s):  
Mamta Juneja ◽  
Poojita Garg ◽  
Ravinder Kaur ◽  
Palak Manocha ◽  
Prateek ◽  
...  
Keyword(s):  
Landmark Detection ◽  
Detection Techniques

scholarly journals 2D Wasserstein Loss for Robust Facial Landmark Detection

2021 ◽  
pp. 107945
Author(s):  
Yongzhe YAN ◽  
Stefan DUFFNER ◽  
Priyanka PHUTANE ◽  
Anthony BERTHELIER ◽  
Christophe BLANC ◽  
...  
Keyword(s):  
Landmark Detection ◽  
Facial Landmark Detection ◽  
Facial Landmark
Sign in / Sign up

Export Citation Format

Share Document