Comparing Active Shape Models with Active Appearance Models

1999 ◽  
Author(s):  
T.F. Cootes ◽  
G. Edwards ◽  
C. J. Taylor
Keyword(s):  
Active Appearance Models ◽  
Active Shape Models ◽  
Shape Models ◽  
Active Shape ◽  
Appearance Models ◽  
Active Appearance

scholarly journals Brain Structures Segmentation by using Statistical Models

2017 ◽  
Vol 1 (3) ◽  
pp. 59
Author(s):  
Abdelfettah Meziane ◽  
Saïd MAHMOUDI ◽  
Mohammed Amine CHIKH
Keyword(s):  
Automatic Segmentation ◽  
Brain Structures ◽  
Segmentation Method ◽  
Resonance Imaging ◽  
Active Appearance Models ◽  
Active Shape Models ◽  
Shape Models ◽  
Appearance Models ◽  
Magnetic Resonance Imaging Mri ◽  
Aided Diagnosis

Automatic segmentation of brain structures is a fundamental step for quantitative analysis of images in many brain’s pathologies such as Alzheimer’s, brain’s tumors or multiple sclerosis. The large variation of brain structures requires the development of efficient and specific methods, often by using Magnetic Resonance Imaging (MRI) modality. The goal of our work is to implement an automatic brain’s structures segmentation method that uses the active shape models (ASM) and active appearance models (AAM) techniques. Another goal of this work is to compare the performances of these segmentation approaches, and also to evaluate their use in a computer aided diagnosis tools and to compare their performances.

scholarly journals Principles and Methods for Face Recognition and Face Modelling

2010 ◽  
pp. 53-78 ◽  
Author(s):  
Tim Rawlinson ◽  
Abhir Bhalerao ◽  
Li Wang
Keyword(s):  
Face Recognition ◽  
Extraction Methods ◽  
Active Appearance Models ◽  
Active Shape Models ◽  
Shape Models ◽  
3D Geometry ◽  
The Face ◽  
Appearance Models ◽  
Recognition Systems ◽  
Image Pixels

This chapter focuses on the principles behind methods currently used for face recognition, which have a wide variety of uses from biometrics, surveillance and forensics. After a brief description of how faces can be detected in images, the authors describe 2D feature extraction methods that operate on all the image pixels in the face detected region: Eigenfaces and Fisherfaces first proposed in the early 1990s. Although Eigenfaces can be made to work reasonably well for faces captured in controlled conditions, such as frontal faces under the same illumination, recognition rates are poor. The authors discuss how greater accuracy can be achieved by extracting features from the boundaries of the faces by using Active Shape Models and, the skin textures, using Active Appearance Models, originally proposed by Cootes and Talyor. The remainder of the chapter on face recognition is dedicated such shape models, their implementation and use and their extension to 3D. The authors show that if multiple cameras are used the 3D geometry of the captured faces can be recovered without the use of range scanning or structured light. 3D face models make recognition systems better at dealing with pose and lighting variation.

scholarly journals Active appearance models with occlusion

2006 ◽  
Vol 24 (6) ◽  
pp. 593-604 ◽  
Author(s):  
Ralph Gross ◽  
Iain Matthews ◽  
Simon Baker
Keyword(s):  
Active Appearance Models ◽  
Appearance Models ◽  
Active Appearance

scholarly journals Robust Infants Face Tracking Using Active Appearance Models: A Mixed-State CONDENSATION Approach

2007 ◽  
pp. 13-23
Author(s):  
Luigi Bagnato ◽  
Matteo Sorci ◽  
Gianluca Antonini ◽  
Giuseppe Baruffa ◽  
Andrea Maier ◽  
...  
Keyword(s):  
Mixed State ◽  
Face Tracking ◽  
Active Appearance Models ◽  
Appearance Models ◽  
Active Appearance
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