计算机科学 ›› 2016, Vol. 43 ›› Issue (9): 315-319.doi: 10.11896/j.issn.1002-137X.2016.09.063

• 图形图像与模式识别 • 上一篇    

基于改进的背景模型的图像识别算法研究

魏霖静,宁璐璐,代永强,侯振兴   

  1. 甘肃农业大学信息科学技术学院 兰州730070,江南大学生物工程学院 无锡214000,甘肃农业大学信息科学技术学院 兰州730070;兰州大学信息科学与工程学院 兰州730000,南京大学信息管理学院 南京210093
  • 出版日期:2018-12-01 发布日期:2018-12-01
  • 基金资助:
    本文受国家自然科学基金项目(034031122,61063028),兰州市科技局项目(2014-1-74)资助

Image Segmentation Method Based on Background Model and its Application in Face Recognition

WEI Lin-jing, NING Lu-lu, DAI Yong-qiang and HOU Zhen-xing   

  • Online:2018-12-01 Published:2018-12-01

摘要: 当图像亮度不均匀、对比度低时,提取图像前景较困难。为此,提出一种图像分割方法,结合正弦基函数和绝对值距离测度构建背景模型,依据优化理论和迭代法求解背景模型,通过比较背景模型中各像素点亮度与实际图像中各像素点亮度来判别各像素点是背景还是前景。为应对图像亮度不均匀的情况,在图像分割前对图像进行分块,在分块图像中依据背景模型或相邻分块背景相似度进行图像分割。实验结果表明,在普适性方面,相对于经典的模糊C均值法和OTSU法,该方法的分割误差小,尤其是对亮度不均匀和对比度低的图像;在掌纹图像分割应用方面,与迭代线跟踪法和模糊粗糙集法相比,该方法的错误率低、信噪比高、处理时间短。最后将提出的分割算法应用在人脸识别上,实验结果表明了该算法的先进性。

关键词: 背景模型,图像分割,基函数,正弦函数,优化理论,迭代法,距离测度,人脸识别

Abstract: The foreground image is hard to be extracted when image’s intensity is inhomogeneous or the contrast is low.To solve this problem,an image segmentation method was proposed.This method reconstructs background model by combining sine basis functions with absolute distance measurement,and solves the model according to optimization theory and iteration method.This method discriminates between background and foreground of every pixel by comparing the intensity of each pixel in the background model with that in real image.To deal with the situation of inhomogeneous intensity of image,the image is divided into blocks before image segmentation,and is segmented according to background model and background similarity among adjacent blocks in sub-block image.Experimental results show that,comparing with classical methods including fuzzy C-means and OTSU,this method has lowest segmentation error,especially for the image with inhomogeneous intensity and low contrast.In the applications of palmprint image segmentation,comparing with iterated line tracing method and rough-fuzzy set method,this method has the characteristics of low error rate,high signal to noise ratio and shorter processing time.

Key words: Background model,Image segmentation,Basis function,Sine function,Optimization theory,Iteration method,Distance measure,Face recognition

[1] Zhang H,Fritts J E,Goldman S A.Image segmentation evaluation:A survey of unsupervised methods[J].Computer Vision & Image Understanding,2008,110(2):260-280
[2] Boykov Y,Funka-Lea G.Graph Cuts and Efficient N-D ImageSegmentation[J].International Journal of Computer Vision,2006,0(2):109-131
[3] Pal N R,Pal S K.A review on image segmentation techniques[J].Pattern Recognition,1993,26(93):1277-1294
[4] Lin Zheng-chun,Wang Zhi-yan,Zhang Yan-qing.Optimal Evolution Algrithm for Image Thrcsholding[J].Journal of Compu-ter-Aided Design & Computer Graphics,2010,22(7):1201-1206(in Chinese) 林正春,王知衍,张艳青.最优进化图像阈值分割法[J].计算机辅助设计与图形学学报,2010,2(7):1201-1206
[5] Riquelme M T,Barreiro P,Ruiz-Altisent M,et al.Olive classification according to external damage using image analysis[J].Journal of Food Engineering,2008,87(3):371-379
[6] Rahimi S,Zargham M,Thakre A,et al.A Parallel Fuzzy C-Mean algorithm for Image Segmentation[C]∥ Processing Nafips’04.2004:234-237
[7] Otsu N.A threshold selection method from gray-level histo-grams[J].IEEE Transactions on System Man and Cybernetic,1979,9(1):62-66
[8] Li C,Kao C Y,Gore J C,et al.Minimization of region-scalable fitting energy for image segmentation[J].IEEE Transactions on Image Processing A Publication of the IEEE Signal Processing Society,2008,17(10):1940-1949
[9] Felzenszwalb P F,Huttenlocher D P.Efficient Graph-Based Ima-ge Segmentation[J].International Journal of Computer Vision,2004,59(2):167-181
[10] Liu T,Xie J B,Yan W,et al.An algorithm for finger-vein segmentation based on modified repeated line tracking[J].Imaging Science Journal,2013,61(6):491-502
[11] Pham V H,Lee B R.An image segmentation approach for fruit defect detection using k-means clustering and graph-based algorithm[J].Vietnam Journal of Computer Science,2014,2(1):25-33
[12] Brodi D.Text Line Segmentation With Water Flow Algorithm Based on Power Function[J].Journal of Electrical Engineering,2015,66(3):132-141
[13] Wang X H,Yi-Gang H E,Zeng Z Z.Optimized Design of the Type-four FIR Filter Based on Neural Networks with Sine Basis Functions[J].Journal of Circuits & Systems,2003,8(5):97-100
[14] Jia Xu,Sun Fu-ming,Cao Yu-dong,et al.Dorsal Hand Vein Re-cognition Algorithm Based on Effective Dimensional Feature[J].Computer Science,2016,3(1):315-318(in Chinese) 贾旭,孙福明,曹玉东,等.基于有效维度特征的手背静脉识别算法[J].计算机科学,2016,43(1):315-318
[15] He J H.A New Iteration Method For Solving Algebraic Equations[J].Applied Mathematics & Computation,2003,135(1):81-84
[16] Maji P,Roy S.Rough-Fuzzy Clustering and Unsupervised Feature Selection for Wavelet Based MR Image Segmentation[J].PloS One,2015,10(4):e123677
[17] http://www4.comp.polyu.edu.hk/~biometrics/MultispectralPalmprint/MSP.htm
[18] Hou Zhi-xu,Zhang Jian-xun.A method of Color Image Segmentation Used in Obstacle Recognition[J].Journal of Chongqing University of Technology(Natural Science),2016,0(3):94-111(in Chinese) 侯之旭,张建勋.一种彩色图像分割的障碍物识别方法[J].重庆理工大学学报(自然科学),2016,0(3):94-111

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