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 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 CircleNet for Hip Landmark Detection

2020 ◽  
Vol 34 (07) ◽  
pp. 12370-12377
Author(s):  
Hai Wu ◽  
Hongtao Xie ◽  
Chuanbin Liu ◽  
Zheng-Jun Zha ◽  
Jun Sun ◽  
...  
Keyword(s):  
Object Detection ◽  
Critical Role ◽  
Developmental Dysplasia ◽  
Context Modeling ◽  
Modeling Framework ◽  
Landmark Detection ◽  
X Ray ◽  
Detection Techniques ◽  
Non Local ◽  
First Time

Landmark detection plays a critical role in diagnosis of Developmental Dysplasia of the Hip (DDH). Heatmap and anchor-based object detection techniques could obtain reasonable results. However, they have limitations in both robustness and precision given the complexities and inhomogeneity of hip X-ray images. In this paper, we propose a much simpler and more efficient framework called CircleNet to improve the accuracy of landmark detection by predicting landmark and corresponding radius. Using the CircleNet, we not only constrain the relationship between landmarks but also integrate landmark detection and object detection into an end-to-end framework. In order to capture the effective information of the long-range dependency of landmarks in the DDH image, here we propose a new context modeling framework, named the Local Non-Local (LNL) block. The LNL block has the benefits of both non-local block and lightweight computation. We construct a professional DDH dataset for the first time and evaluate our CircleNet on it. The dataset has the largest number of DDH X-ray images in the world to our knowledge. Our results show that the CircleNet can achieve the state-of-the-art results for landmark detection on the dataset with a large margin of 1.8 average pixels compared to current methods. The dataset and source code will be publicly available.

ION DETECTION TECHNIQUES APPLIED TO RYDBERG ATOMS IN EXTERNAL FIELDS

1983 ◽  
Vol 44 (C7) ◽  
pp. C7-193-C7-208 ◽  
Author(s):  
F. Penent ◽  
C. Chardonnet ◽  
D. Delande ◽  
F. Biraben ◽  
J. C. Gay
Keyword(s):  
Rydberg Atoms ◽  
External Fields ◽  
Ion Detection ◽  
Detection Techniques

Comparative quantification of phosphatidylcholine in sea urchins eggs by instrumental TLC with various detection techniques

Planta Medica ◽  
2010 ◽  
Vol 76 (12) ◽  
Author(s):  
S Ivanova ◽  
I Urakova ◽  
O Pozharitskaya ◽  
A Shikov ◽  
V Makarov
Keyword(s):  
Sea Urchins ◽  
Detection Techniques

Advances in profiling of natural products by triple detection techniques combined with super critical CO2 mobile phases and Sub-2 µm stationary phases

Planta Medica ◽  
2015 ◽  
Vol 81 (16) ◽  
Author(s):  
D Rodriguez Cabaleiro ◽  
P Hong ◽  
G Isaac ◽  
J Yuk ◽  
K Yu ◽  
...  
Keyword(s):  
Natural Products ◽  
Stationary Phases ◽  
Detection Techniques ◽  
Mobile Phases

Conflict Detection Techniques for Preserving Privacy in Social Media

2018 ◽  
pp. 78-85
Author(s):  
Fulpagare Priya K. ◽  
Nitin N. Patil
Keyword(s):  
Social Media ◽  
Privacy Management ◽  
Crucial Importance ◽  
Network Integration ◽  
Critical Problem ◽  
Detection Techniques ◽  
Privacy Preferences ◽  
Content Sharing ◽  
Privacy Settings ◽  
Relationship Network

Social Network is an emerging e-service for Content Sharing Sites (CSS). It is an emerging service which provides reliable communication. Some users over CSS affect user’s privacy on their personal contents, where some users keep on sending annoying comments and messages by taking advantage of the user’s inherent trust in their relationship network. Integration of multiple user’s privacy preferences is very difficult task, because privacy preferences may create conflict. The techniques to resolve conflicts are essentially required. Moreover, these methods need to consider how users would actually reach an agreement about a solution to the conflict in order to offer solutions acceptable by all of the concerned users. The first mechanism to resolve conflicts for multi-party privacy management in social media that is able to adapt to different situations by displaying the enterprises that users make to reach a result to the conflicts. Billions of items that are uploaded to social media are co-owned by multiple users. Only the user that uploads the item is allowed to set its privacy settings (i.e. who can access the item). This is a critical problem as users’ privacy preferences for co-owned items can conflict. Multi-party privacy management is therefore of crucial importance for users to appropriately reserve their privacy in social media.

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

A Review on Heart Disease Detection Techniques

2017 ◽  
Vol 7 (7) ◽  
pp. 395
Author(s):  
. Anika ◽  
Navpreet Kaur
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Review Paper ◽  
Vascular Diseases ◽  
Disease Detection ◽  
Automated Classification ◽  
Feature Extraction Method ◽  
Detection Techniques ◽  
Cardio Vascular ◽  
Public Domain Software

The paper exhibits a formal audit on early detection of heart disease which are the major cause of death. Computational science has potential to detect disease in prior stages automatically. With this review paper we describe machine learning for disease detection. Machine learning is a method of data analysis that automates analytical model building.Various techniques develop to predict cardiac disease based on cases through MRI was developed. Automated classification using machine learning. Feature extraction method using Cell Profiler and GLCM. Cell Profiler a public domain software, freely available is flourished by the Broad Institute's Imaging Platform and Glcm is a statistical method of examining texture .Various techniques to detect cardio vascular diseases.

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