Abstract
Because many model-based object representation appro- aches such as active appearance models (AAMs) use a fixed linear appearance model, they often fail to fit to a novel image that is captured in a different imaging condition from that of training images. To alleviate this problem, we propose to use adaptive linear appearance model that is updated by the incremental principal component analysis (PCA). Because the incremental update algorithm uses a new appearance data that is obtained in an on-line manner, a reliable method to measure the quality of the new data is required not to break the integrity of the appearance model. For this purpose, we modified the adaptive observation model (AOM), which has been used to model the varying appearance of the target object using statistical model such as Gaussian mixtures. Experiment results showed that the incremental AAM that uses adaptive linear appearance model greatly improved the robustness to the varying illumination condition when compared to the traditional AAM.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Cootes, T., Edwards, G., Taylor, C.: Active appearance models. IEEE Trans. on Pattern Recognition and Machine Intelligence 23, 681–685 (2001)
Matthews, I., Baker, S.: Active appearance models revisited. International Journal of Computer Vision 60, 135–164 (2004)
Blanz, V., Vetter, T.: A morphable model for the synthesis of 3d faces. In: SIGGRAPH. Computer Graphics, Annual Conference Series, pp. 187–194 (1999)
McKenna, S., Raja, Y., Gong, S.: Object tracking using adaptive color mixture models. In: Asian Conference on Computer Vision, pp. 615–622 (1997)
Jepson, A., Fleet, D., El-Maraghi, T.: Robust online appearance models for visual tracking. IEEE Trans. on Pattern Analysis and Machine Intelligence 25 (2003)
Zhou, S., Chellappa, R., Moghaddam, B.: Visual tracking and recognition using appearance-adaptive model in particle filter. IEEE Trans. on Image Processing 13, 1491–1506 (2004)
Artac, M., Jogan, M., Leonardis, A.: Incremental pca for on-line visual learning and recognition. In: International Conference on Pattern Recognition, pp. 781–784 (2002)
Hall, P., Marshall, D., Martin, R.: Incremental eigenanalysis for classification. In: British Machine Vision Conference (1998)
Li, Y.: On incremental and robust subspace learning. Pattern recognition 37, 1509–1518 (2004)
Baker, S., Matthews, I.: Lucas-kanade 20 years on: A unifying framework. International Journal of Computer Vision 56, 221–255 (2004)
Gross, R., Matthews, I., Baker, S.: Lucas-kanade 20 years on: A unifying framework: Part 3. Cmu-ri-tr-03-05, CMU (2003)
Kanade, T., Lucas, B.: An iterative image registration technique with an application to stereo vision. In: International Joint Conference on Artificial Intelligence, pp. 674–679 (1981)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lee, S., Sung, J., Kim, D. (2007). Incremental Update of Linear Appearance Models and Its Application to AAM: Incremental AAM. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2007. Lecture Notes in Computer Science, vol 4633. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74260-9_48
Download citation
DOI: https://doi.org/10.1007/978-3-540-74260-9_48
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74258-6
Online ISBN: 978-3-540-74260-9
eBook Packages: Computer ScienceComputer Science (R0)