FAME-a flexible appearance modeling environment

By Mikkel B. Stegmann, Bjarne Kjær Ersbøll, Rasmus Larsen. IEEE Transactions on Medical Imaging, 2003

347 citations recorded by the source corpus.

Combined modeling of pixel intensities and shape has proven to be a very robust and widely applicable approach to interpret images. As such the active appearance model (AAM) framework has been applied to a wide variety of problems within medical image analysis. This paper summarizes AAM applications within medicine and describes a public domain implementation, namely the flexible appearance modeling environment (FAME). We give guidelines for the use of this research platform, and show that the optimization techniques used renders it applicable to interactive medical applications. To increase performance and make models generalize better, we apply parallel analysis to obtain automatic and objective model truncation. Further, two different AAM training methods are compared along with a reference case study carried out on cross-sectional short-axis cardiac magnetic resonance images and face images. Source code and annotated data sets needed to reproduce the results are put in the public domain for further investigation.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/7646489b3feb2b79ed52d79327cc047818d91e16

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Mechanics

1. Roope Raisamo
2. Erno Mäkinen

In Computer Vision and Pattern Recognition

1. Roope Raisamo
2. Erno Mäkinen
3. Tal Hassner
4. Gil Levi

Citations in the corpus

• Evaluation of Gender Classification Methods with Automatically Detected and Aligned Faces (2008, methodology): “We used the IMM Face database [ 18 ] and the FERET database [2] for the experiments.”