Contiguous Uniform Deviation for Multiple Linear Regression in Pattern Recognition

International Conference on Computing and Applied Informatics 2016

物理学;计算机科学

Andriana, A.S.^1,2 ; Prihatmanto, D.^1 ; Hidaya, E.M.I.^1 ; Supriana, I.^1 ; Machbub, C.^1

Institute of Technology Bandung, School of Electrical Engineering and Informatics, Indonesia^1

Indonesian Institute of Sciences, Research Centre for Informatics, Indonesia^2

关键词: Elements detection;    Face recognition methods;    Image pixel value;    Image recognition system;    Mathematical functions;    Multiple linear regressions;    Pattern structure;    Syntactic grammars;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/801/1/012046/pdf
DOI  :  10.1088/1742-6596/801/1/012046

学科分类：计算机科学（综合）

来源: IOP

【 摘 要 】

Understanding images by recognizing its objects is still a challenging task. Face elements detection has been developed by researchers but not yet shows enough information (low resolution in information) needed for recognizing objects. Available face recognition methods still have error in classification and need a huge amount of examples which may still be incomplete. Another approach which is still rare in understanding images uses pattern structures or syntactic grammars describing shape detail features. Image pixel values are also processed as signal patterns which are approximated by mathematical function curve fitting. This paper attempts to add contiguous uniform deviation method to curve fitting algorithm to increase applicability in image recognition system related to object movement. The combination of multiple linear regression and contiguous uniform deviation method are applied to the function of image pixel values, and show results in higher resolution (more information) of visual object detail description in object movement.