EigenHistograms: Using Low Dimensional Models of Color Distribution for Real Time Object Recognition
Distribution of object colors has been used in computer vision for recognition and indexing. Most of the recent approaches to this problem have been focused on defining optimal spaces for representing pixel values that are related to physical models and that present some invariance. We propose a new approach to identify individual object color distributions by using statistical learning techniques and to allow their compact representation in low dimensional spaces. This approach outperforms generic “optimal” spaces when color illumination is constant, allowing changes in object pose and illumination direction. This approach has been tested for real time industrial inspection of multicolored objects.
KeywordsObject Recognition Color Indexing Color Histogram Color Distribution Constant Color
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- 1.Funt B., Finlayson G.: Color Constant Color Indexing. IEEE Trans. PAMI, 17, 1995 522–528Google Scholar
- 4.Murase H., Nayar S.K.: Learning and recognition of 3D objects from appearance. Proc. IEEE Qualitative Vision Workshop, NY, (1993) 39–49Google Scholar
- 5.Gevers T., Smeulders A.: Color Based Object Recognition. In Image Analysis and Processing, Alberto del Bimbo (Ed), LNCS 1310 (1997).Google Scholar
- 6.Ullman S.: High-Level Vision, MIT Press (1996).Google Scholar
- 7.Grimson W.: Object Recognition by Computer, MIT Press (1990).Google Scholar
- 8.Moghaddam B. and Pentland A. (1996) Probabilistic Visual Learning for Object Recognition, in Nayar S. and Poggio T. (eds.) “Early Visual Learning”, Oxford University Press.Google Scholar