Supporting Feature Estimation with Risk and Effort Annotations

2016 ◽  
Author(s):  
Simon Grapenthin ◽  
Matthias Book ◽  
Thomas Richter ◽  
Volker Gruhn
Keyword(s):  
Feature Estimation

Algorithm for Cepstral Analysis and Homomorphic Filtering for Glottal Feature Estimation in Speech Signals

2015 ◽  
Vol 10 (4) ◽  
pp. 561
Author(s):  
Leonardo A. Gongora ◽  
Diego A. Rojas ◽  
Olga Lucia Ramos
Keyword(s):  
Speech Signals ◽  
Cepstral Analysis ◽  
Homomorphic Filtering ◽  
Feature Estimation

Clean speech feature estimation based on soft spectral masking

2006 ◽  
Author(s):  
Young Joon Kim ◽  
Woohyung Lim ◽  
Nam Soo Kim
Keyword(s):  
Speech Feature ◽  
Feature Estimation ◽  
Spectral Masking

OTCA Approach Towards Blurred Image Feature Estimation and Enrichment

2017 ◽  
pp. 329-338
Author(s):  
Sandeep Kumar Sharma ◽  
C. S. Lamba ◽  
V. S. Rathore
Keyword(s):  
Image Feature ◽  
Blurred Image ◽  
Feature Estimation

Research on Target Tracking Algorithm Based on Similarity Feature Estimation

2020 ◽  
Vol 57 (24) ◽  
pp. 241005
Author(s):  
张博 Zhang Bo ◽  
刘刚 Liu Gang
Keyword(s):  
Target Tracking ◽  
Tracking Algorithm ◽  
Feature Estimation

GIS information feature estimation algorithm based on big data

2019 ◽  
Vol 15 (2) ◽  
pp. 198
Author(s):  
Chunyang Lu ◽  
Feng Wen
Keyword(s):  
Big Data ◽  
Estimation Algorithm ◽  
Feature Estimation

scholarly journals Extended Target Tracking and Feature Estimation for Optical Sensors Based on the Gaussian Process

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1704
Author(s):  
Haoyang Yu ◽  
Wei An ◽  
Ran Zhu
Keyword(s):  
Gaussian Process ◽  
Optical Sensors ◽  
Radial Function ◽  
Target Surface ◽  
Amplitude Distribution ◽  
Surface Shape ◽  
Feature Parameters ◽  
Extended Target ◽  
Feature Estimation ◽  
Tracking Surface

A problem of tracking surface shape-shifting extended target by using gray scale pixels on optical image is considered. The measurement with amplitude information (AI) is available to the proposed method. The target is regarded as a convex hemispheric object, and the amplitude distribution of the extended target is represented by a solid radial function. The Gaussian process (GP) is applied and the covariance function of GP is modified to fit the convex hemispheric shape. The points to be estimated on the target surface are selected reasonably in the hemispheric space at the azimuth and pitch directions. Analytical representation of the estimated target extent is provided and the recursive process is implemented by the extended Kalman filter (EKF). In order to demonstrate the algorithm’s ability of tracking complex shaped targets, a trailing target characterized by two feature parameters is simulated and the two feature parameters are extracted with the estimated points. The simulations verify the validity of the proposed method with compared to classical algorithms.

Glaucoma Diagnosis from Eye Fundus Images Based on Deep Morphometric Feature Estimation

2018 ◽  
pp. 319-327 ◽  
Author(s):  
Oscar Perdomo ◽  
Vincent Andrearczyk ◽  
Fabrice Meriaudeau ◽  
Henning Müller ◽  
Fabio A. González
Keyword(s):  
Fundus Images ◽  
Glaucoma Diagnosis ◽  
Morphometric Feature ◽  
Feature Estimation

Feature estimation performance using a two-dimensional parametric model of radar scattering

1997 ◽  
Author(s):  
Andria van der Merwe ◽  
Mike J. Gerry ◽  
Lee C. Potter ◽  
Inder J. Gupta
Keyword(s):  
Parametric Model ◽  
Two Dimensional ◽  
Estimation Performance ◽  
Radar Scattering ◽  
Feature Estimation
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