scholarly journals Deformable Template Tracking in 1ms

2014 ◽  
Author(s):  
David Joseph Tan ◽  
Stefan Holzer ◽  
Nassir Navab ◽  
Slobodan Ilic
Keyword(s):  
Deformable Template ◽  
Template Tracking

Machine vision-based sensing for helicopter flight control

Robotica ◽  
2000 ◽  
Vol 18 (3) ◽  
pp. 299-303 ◽  
Author(s):  
Carl-Henrik Oertel
Keyword(s):  
Machine Vision ◽  
Measurement System ◽  
Flight Control ◽  
A Priori ◽  
Image Data ◽  
Two Dimensional ◽  
Position Measurement ◽  
Additional Information ◽  
Helicopter Flight ◽  
Template Tracking

Machine vision-based sensing enables automatic hover stabilization of helicopters. The evaluation of image data, which is produced by a camera looking straight to the ground, results in a drift free autonomous on-board position measurement system. No additional information about the appearance of the scenery seen by the camera (e.g. landmarks) is needed. The technique being applied is a combination of the 4D-approach with two dimensional template tracking of a priori unknown features.

HUMAN FACE RECOGNITION FROM A SINGLE FRONT VIEW

1992 ◽  
Vol 06 (04) ◽  
pp. 571-593 ◽  
Author(s):  
CHING-WEN CHEN ◽  
CHUNG-LIN HUANG
Keyword(s):  
Face Recognition ◽  
Active Contour Model ◽  
Facial Feature ◽  
Recognition System ◽  
Facial Features ◽  
Human Face Recognition ◽  
Front View ◽  
Deformable Template ◽  
Face Recognition System ◽  
Feature Values

This paper presents a face recognition system which can identify the unknown identity effectively using the front-view facial features. In front-view facial feature extractions, we can capture the contours of eyes and mouth by the deformable template model because of their analytically describable shapes. However, the shapes of eyebrows, nostrils and face are difficult to model using a deformable template. We extract them by using the active contour model (snake). After the contours of all facial features have been captured, we calculate effective feature values from these extracted contours and construct databases for unknown identities classification. In the database generation phase, 12 models are photographed, and feature vectors are calculated for each portrait. In the identification phase if any one of these 12 persons has his picture taken again, the system can recognize his identity.

Application of Repeated GA to Deformable Template Matching in Cattle Images

2004 ◽  
pp. 134-145 ◽  
Author(s):  
Horacio M. González Velasco ◽  
Carlos J. García Orellana ◽  
Miguel Macías Macías ◽  
Ramón Gallardo Caballero ◽  
M. Isabel Acevedo Sotoca
Keyword(s):  
Template Matching ◽  
Deformable Template

Shape Recognition of the Embryo Cell Using Deformable Template for Micromanipulation

2004 ◽  
pp. 463-472 ◽  
Author(s):  
Min-Soo Jang ◽  
Seok-Joo Lee ◽  
Ho-dong Lee ◽  
Yong-Guk Kim ◽  
Byungkyu Kim ◽  
...  
Keyword(s):  
Shape Recognition ◽  
Embryo Cell ◽  
Deformable Template

Real-Time Edge Template Tracking via Homography Estimation

2018 ◽  
Author(s):  
Xuebin Qin ◽  
Shida He ◽  
Zichen Zhang ◽  
Masood Dehghan ◽  
Jun Jin ◽  
...  
Keyword(s):  
Real Time ◽  
Homography Estimation ◽  
Template Tracking

Identification of a DNA primase template tracking site redefines the geometry of primer synthesis

2008 ◽  
Vol 15 (2) ◽  
pp. 163-169 ◽  
Author(s):  
Jacob E Corn ◽  
Jeffrey G Pelton ◽  
James M Berger
Keyword(s):  
Dna Primase ◽  
Template Tracking

scholarly journals Face Detection: Mapping Human Performance

Perception ◽  
10.1068/p5007 ◽  
2003 ◽  
Vol 32 (8) ◽  
pp. 903-920 ◽  
Author(s):  
Michael B Lewis ◽  
Andrew J Edmonds
Keyword(s):  
Reaction Time ◽  
Face Detection ◽  
Human Performance ◽  
Limited Attention ◽  
Focused Attention ◽  
Deformable Template ◽  
Detection Systems ◽  
Face Experiment ◽  
Extensive Body ◽  
Inversion Effects

The recognition of faces has been the focus of an extensive body of research, whereas the preliminary and prerequisite task of detecting a face has received limited attention from psychologists. Four experiments are reported that address the question how we detect a face. Experiment 1 reveals that we use information from the scene to aid detection. In experiment 2 we investigated which features of a face speed the detection of faces. Experiment 3 revealed inversion effects and an interaction between the effects of blurring and reduction of contrast. In experiment 4 the sizes of effects of reversal of orientation, luminance, and hue were compared. Luminance was found to have the greatest effect on reaction time to detect faces. The results are interpreted as suggesting that face detection proceeds by a pre-attentive stage that identifies possible face regions, which is followed by a focused-attention stage that employs a deformable template. Comparisons are drawn with automatic face-detection systems.

Sign in / Sign up

Export Citation Format

Share Document