Abstract
This paper proposes Normal Form Transformation (NFT) as a preprocessing of Support Vector Machines (SVMs). Object recognition from images can be regarded as a fundamental technique in discovery science. Aspect-based recognition with SVMs is effective under constrained situations. However, object recognition from rotated, shifted, magnified or reduced images is a difficult task for simple SVMs. In order to circumvent this problem, we propose NFT, which rotates an image based on low-luminance directed vector and shifts, magnifies or reduces the image based on the object’s maximum horizontal distance and maximum vertical distance. We have applied SVMs with NFT to a database of 7200 images concerning 100 different objects. The recognition rates were over 97% in these experiments except for cases of extreme reduction. These results clearly demonstrate the effectiveness of the proposed approach in aspect-based recognition.
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© 1999 Springer-Verlag Berlin Heidelberg
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Sugaya, S., Suzuki, E. (1999). Normal Form Transformation for Object Recognition Based on Support Vector Machines. In: Arikawa, S., Furukawa, K. (eds) Discovery Science. DS 1999. Lecture Notes in Computer Science(), vol 1721. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46846-3_28
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DOI: https://doi.org/10.1007/3-540-46846-3_28
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Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-66713-1
Online ISBN: 978-3-540-46846-2
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