Study of landmarks estimation stability produced by AAM

Victor Glebov; Oleg Lashmanov

doi:10.1117/12.2264162

26 June 2017 Study of landmarks estimation stability produced by AAM

Victor Glebov, Oleg Lashmanov

Proceedings Volume 10334, Automated Visual Inspection and Machine Vision II; 103340E (2017) https://doi.org/10.1117/12.2264162
Event: SPIE Optical Metrology, 2017, Munich, Germany

Abstract

Active Appearance Model (AAM) is an accurate and robust tool and is suitable when it’s needed to estimate shape of object when its’ approximate shape is known but varies within a certain range from instance to instance. An AAM allows complex models of shape (for example human face) and appearance to be matched to new images rapidly. An AAM contains a statistical model of the shape and gray level or color appearance of an object of interest. The associated search algorithm exploits the locally linear relationship between model parameter displacements and the residual errors between model instance and image. AAM is widely used but the research of its’ accuracy and stability still remains an important and not fully learned issue. In this paper, we study landmarks stability and error estimation produced by AAM in different lightning conditions and signal-to-noise ratio (SNR).

Citation Download Citation

Victor Glebov and Oleg Lashmanov "Study of landmarks estimation stability produced by AAM", Proc. SPIE 10334, Automated Visual Inspection and Machine Vision II, 103340E (26 June 2017); https://doi.org/10.1117/12.2264162

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available