Improving feature location accuracy via paragraph vector tuning

2019 ◽  
Vol 116 ◽  
pp. 106177
Author(s):  
Allysson Costa e Silva ◽  
Marcelo de Almeida Maia
Keyword(s):  
Feature Location ◽  
Location Accuracy ◽  
Paragraph Vector

Study on Optimization of the Number of DF Stations

2014 ◽  
Vol 709 ◽  
pp. 485-490
Author(s):  
Xiang Wu ◽  
Jun Jun Zong ◽  
Xun Xue Cui ◽  
Chuan Xu Liu
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Likelihood Estimation ◽  
Direction Finding ◽  
Location Error ◽  
Location Accuracy ◽  
Error Models ◽  
Reasonable Number ◽  
Simulation Results

Reasonable number of direction finding station is examined in multi-station bearing-crossing location. Though it is believed that increasing the number of station is helpful to improve the location accuracy, In the paper, the maximum likelihood estimation (MLE) as an example. The algorithms and the location error models are given. The simulation results show that the location accuracy will be improved quickly with the increase of the number of the measuring participants, but the improvement will be sharply slowed down if too many station involved, which also boost the complexity of location.

scholarly journals Correction to: An empirical assessment of baseline feature location techniques

2021 ◽  
Vol 26 (3) ◽  
Author(s):  
Abdul Razzaq ◽  
Andrew Le Gear ◽  
Chris Exton ◽  
Jim Buckley
Keyword(s):  
Feature Location ◽  
Empirical Assessment ◽  
Link Type

A Correction to this paper has been published: 10.1007/s10664-020-09924-6

Feature Location Using Probabilistic Ranking of Methods Based on Execution Scenarios and Information Retrieval

2007 ◽  
Vol 33 (6) ◽  
pp. 420-432 ◽  
Author(s):  
Denys Poshyvanyk ◽  
Yann-Gael Gueheneuc ◽  
Andrian Marcus ◽  
Giuliano Antoniol ◽  
Vaclav Rajlich
Keyword(s):  
Information Retrieval ◽  
Feature Location

AN EFFICIENT APPROACH FOR FACE DETECTION AND FACIAL FEATURE LOCATION USING PRUNE-AND-SEARCH TECHNIQUE

2004 ◽  
Vol 18 (06) ◽  
pp. 987-1005 ◽  
Author(s):  
CHIN-CHEN CHANG ◽  
YUAN-HUI YU
Keyword(s):  
Face Detection ◽  
Facial Feature ◽  
Facial Features ◽  
Base Line ◽  
Human Face ◽  
Feature Location ◽  
Search Technique ◽  
Efficient Approach ◽  
Human Face Detection ◽  
Geometric Knowledge

This paper proposes an efficient approach for human face detection and exact facial features location in a head-and-shoulder image. This method searches for the eye pair candidate as a base line by using the characteristic of the high intensity contrast between the iris and the sclera. To discover other facial features, the algorithm uses geometric knowledge of the human face based on the obtained eye pair candidate. The human face is finally verified with these unclosed facial features. Due to the merits of applying the Prune-and-Search and simple filtering techniques, we have shown that the proposed method indeed achieves very promising performance of face detection and facial feature location.

Sign in / Sign up

Export Citation Format

Share Document