The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Support Vector Machines for 3D Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
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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.