Classification of Specular Object Based on Statistical Learning Theory

Classification of Specular Object Based on Statistical Learning Theory

This paper has presented an efficient solder joint inspection technique through the use of wavelet transform and Support Vector Machines. The proposed scheme consists of two stages: a feature extraction stage for extracting features with wavelet transform, and a classification stage for classifying...

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1. Verfasser: Soo Yun, Tae
Format: Buchkapitel
Sprache:eng
Schlagworte:
Applied sciences
Artificial intelligence
Computer science
control theory
systems
Exact sciences and technology
Good Generalization Performance
Haar Wavelet
Pattern recognition. Digital image processing. Computational geometry
Solder Joint
Statistical Learn Theory
Support Vector Machine
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description This paper has presented an efficient solder joint inspection technique through the use of wavelet transform and Support Vector Machines. The proposed scheme consists of two stages: a feature extraction stage for extracting features with wavelet transform, and a classification stage for classifying solder joints with a support vector machines. Experimental results show that the proposed method produces a high classification rate in the nonlinearly separable problem of classifying solder joints.
doi_str_mv 10.1007/3-540-45723-2_67
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1611-3349
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source Springer Books
subjects Applied sciences
Artificial intelligence
Computer science
control theory
systems
Exact sciences and technology
Good Generalization Performance
Haar Wavelet
Pattern recognition. Digital image processing. Computational geometry
Solder Joint
Statistical Learn Theory
Support Vector Machine
title Classification of Specular Object Based on Statistical Learning Theory
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