The Defect Identification of LED Chips Based on Bayesian Classifier

Autor: Jian Guo Wang, Shao Qing Ren, Yao Wu Pei, Liang Sun, Wei Shi
Rok vydání: 2013
Předmět:
Zdroj: Applied Mechanics and Materials. :1564-1568
ISSN: 1662-7482
DOI: 10.4028/www.scientific.net/amm.333-335.1564
Popis: The types, causes and image properties of the defective and damaged chips are been analyzes. On the basis of analyzing the image properties, Bayesian classifier and identification method are proposed. Based on the Bayesian decision theory, five kinds of image features such as the number of light spot, dark spot, edge spot, block and area are applied. Normal distribution is made use to building classifier and studying the correction strategy of model. In the process of identification, dynamical correction and model parameters completion make the classifier have some degree of self-learning feature and higher identification accuracy. Keywords: defect; feature; classifier; decision
Databáze: OpenAIRE