Chapter

Computer Vision — ECCV’98

Volume 1407 of the series Lecture Notes in Computer Science pp 484-498

Date:

Active appearance models

  • T. F. CootesAffiliated withWolfson Image Analysis Unit, Department of Medical Biophysics, University of Manchester
  • , G. J. EdwardsAffiliated withWolfson Image Analysis Unit, Department of Medical Biophysics, University of Manchester
  • , C. J. TaylorAffiliated withWolfson Image Analysis Unit, Department of Medical Biophysics, University of Manchester

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Abstract

We demonstrate a novel method of interpreting images using an Active Appearance Model (AAM). An AAM contains a statistical model of the shape and grey-level appearance of the object of interest which can generalise to almost any valid example. During a training phase we learn the relationship between model parameter displacements and the residual errors induced between a training image and a synthesised model example. To match to an image we measure the current residuals and use the model to predict changes to the current parameters, leading to a better fit. A good overall match is obtained in a few iterations, even from poor starting estimates. We describe the technique in detail and give results of quantitative performance tests. We anticipate that the AAM algorithm will be an important method for locating deformable objects in many applications.