Abstract
Active Appearance Model is a well-known model that can represent a non-rigid object effectively. However, since it uses the fixed appearance model, the fitting results are often unsatisfactory when the imaging condition of the target image is different from that of training images. To alleviate this problem, incremental AAM was proposed which updates its appearance bases in an on-line manner. However, it can not deal with the sudden changes of illumination. To overcome this, we propose a novel scheme to update the appearance bases. When a new person appears in the input image, we synthesize illuminated images of that person and update the appearance bases of AAM using it. Since we update the appearance bases using synthesized illuminated images in advance, the AAM can fit their model to a target image well when the illumination changes drastically. The experimental results show that our proposed algorithm improves the fitting performance over both the incremental AAM and the original AAM.
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References
Cootes, T., Edwards, G., Taylor, C.: Active appearance models. In: Burkhardt, H., Neumann Ed.s, B. (eds.) Proc. of European Conference on Computer Vision, pp. 484–498 (1998)
Lee, S., Sung, J., Kim, D.: Incremental update of linear appearance models and its application to aam: incremental aam. In: Proc. of ICIAR 2007 (to be published, 2007)
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)
Hall, P., Marshall, D., Martin, R.: Incremental eigenanalysis for classification. In: British Machine Vision Conference (1998)
Tenenbaum, J., Freeman, W.: Separating style and content with bilinear models, neural computation. Neural computation 12, 1247–1283 (2000)
Sim, T., Kanade, T.: Combining models and exemplars for face recognition: An illuminating example. In: Workshop on Models versus Exemplars in Computer Vision (2001)
Belhumeur, P., Kriegman, D.: What is the set of images of an object under all possible illumination conditions. International Journal of Computer Vision 28, 245–260 (1998)
Shashua, A., Riklin-Raviv, T.: The quotient image: class-based re-rendering and recognition withvarying illuminations. IEEE Transactions on Pattern Analysis and Machine Intelligence 23, 129–139 (2001)
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© 2007 Springer-Verlag Berlin Heidelberg
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Lee, HS., Sung, J., Kim, D. (2007). Incremental AAM Using Synthesized Illumination Images. In: Ip, H.HS., Au, O.C., Leung, H., Sun, MT., Ma, WY., Hu, SM. (eds) Advances in Multimedia Information Processing – PCM 2007. PCM 2007. Lecture Notes in Computer Science, vol 4810. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77255-2_83
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DOI: https://doi.org/10.1007/978-3-540-77255-2_83
Publisher Name: Springer, Berlin, Heidelberg
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