Abstract
3-dimensional pattern recognition requires the definition of a similarity measure between 3-dimensional patterns. We discuss how to match 3-dimensional patterns, which are represented by a set of images taken from multiple directions and approximately represented by subspaces. The proposed method is to calculate the canonical angles, in particular the third smallest angle between two subspaces. We demonstrate the viability of the proposed method by performing a pilot study of face recognition. © Springer-Verlag 2004.
Cite
CITATION STYLE
Maeda, K. I., Yamaguchi, O., & Fukui, K. (2004). Towards 3-dimensional pattern recognition. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3138, 1061–1068. https://doi.org/10.1007/978-3-540-27868-9_117
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