Refereed Publications
- Toru Wakahara, “Adaptive Normalization of Handwritten
Characters Using GAT Correlation and Mixture Models,”
in Proceedings of the 17th International Conference on
Pattern Recognition, Vol.1, August 2004, pp. 393-396.
Abstract - This paper
proposes an adaptive or category-dependent normalization
technique for handwritten characters featuring global
affine transformation (GAT) correlation and mixture models.
Key ideas are twofold. First, we estimate a probability
density function (PDF) of black pixels for each category
using mixture models of Gaussian distribution functions
and the EM algorithm. Second, we determine optimal, global
affine transformation that maximizes a normalized cross-correlation
value between a GAT-superimposed input pattern and the
above-mentioned PDF by the successive iteration method.
Experiments using the handwritten numeral database IPTP
CDROM1B show that the entropy of optimally GAT-superimposed
test samples decreases substantially by more than 20%.
We discuss the enhanced normalization ability and the
computational complexity of the proposed method.
- Chihiro Iga and Toru Wakahara, “Character Image
Reconstruction from a Feature Space Using Shape Morphing
and Genetic Algorithms,” in Proceedings of the 9th
International Workshop on Frontiers in Handwriting Recognition,
October 2004, pp. 341-346.
Abstract - This paper
proposes a powerful method that realizes image reconstruction
from a feature space in optical character recognition.
Due to the invisibility of a high-dimensional feature
space, it is difficult to fully understand advantages
and disadvantages of the given feature space and search
for more robust features. The proposed method consists
of two parts. The first part is 2D shape morphing based
on a mesh model via bilinear transformation. The second
part is use of genetic algorithms for determining optimal
morphing parameters. Given an arbitrary feature vector
in a feature space the proposed method deforms each category’s
template to yield the maximal fitness value against the
given feature vector and the deformed template thus obtained
is considered as a reconstructed image from a feature
space. In experiments we use the public handwritten numeral
database IPTP CDROM1B and a gradient feature space. We
first demonstrate a high matching ability of the proposed
mesh model. Then, we show promising experimental results
of image reconstruction from a feature space and discuss
how to use this technique to improve recognition performance.
Other Publications
- Toru Wakahara and Toshiaki Sugimura, “Scheme for
Identifying Gray-Scale Image,” U.S. Patent No. 6,658,149
B1. Feb. 2004.
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