一种基于颜色纹理与SVM的盲道分割算法
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摘要
为了帮助盲人更好地利用盲道,需要将盲道从复杂的前方环境图像中提取出来,提出一种基于颜色纹理和SVM的盲道分割算法,首先利用SVM对样本进行特征训练,再利用训练后的SVM数据模型对输入的图像进行判别,从而将盲道部分提取出来。通过对比选取了HSV颜色空间的颜色特征和3个频率、2个方向角的Gabor滤波器组样本纹理特征,再将其输入到SVM分类器中训练。结果表明,相较于现有算法,该盲道分割算法具有更加稳定、普遍性高、系统处理时间短等优点。
In order to make better use of sidewalk for the blind,blind roads need to be divided from complex surrounding in pictures.In this paper,a blind road segmentation algorithm based on color texture and SVM is proposed,which firstly uses SVM to train the samples with different features and then distinguishes the input picture with the data trained by SVM to get the blind road.In comparison,we get the the color features of samples in HSV color space and the texture features of samples through the Gabor filter which has three frequencies and two direction angles and then put those features into SVM classifier to be trained.Experiment results shows that this algorithm is more stable,of higher universality and less process time compared with existing algorithms.
In order to make better use of sidewalk for the blind,blind roads need to be divided from complex surrounding in pictures.In this paper,a blind road segmentation algorithm based on color texture and SVM is proposed,which firstly uses SVM to train the samples with different features and then distinguishes the input picture with the data trained by SVM to get the blind road.In comparison,we get the the color features of samples in HSV color space and the texture features of samples through the Gabor filter which has three frequencies and two direction angles and then put those features into SVM classifier to be trained.Experiment results shows that this algorithm is more stable,of higher universality and less process time compared with existing algorithms.
引文
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[3]孙滔.基于颜色空间的图像特征提取的研究[D].长春:吉林大学,2006.
[4] TUCERYAN M,JAIN A K.Texture analysis[M].Singapore:World Scientific Publishing,1998.
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[6] VAPNIK V N.The nature of statistic learning theory[M].New York:Springer,1995.
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[8]杨红,罗飞.基于混沌优化的LS-SVM非线性预测控制方法[J].计算机工程与应用,2010,46(5):229-232.
[9]奉国和.SVM分类核函数及参数选择比较[J].计算机工程与应用,2011,47(3):123-124.
[10]陶卿,王常波,方廷健.一种求解闭凸集上二次规划问题的神经网络模型[J].模式识别与人工智能,1998(1):7-11.
[11]梁燕.SVM分类器的扩展及其应用研究[D].长沙:湖南大学,2008.
[12] Chen T W,CHEN Y L,CHIEN S Y.Fast image segmentation based on K-Means clustering with histograms in HSV color space[C].IEEE Workshop on Multimedia Signal Processing,2008:322-325.
[13] FARROKHNIA F,JAIN A K.Unsupervised texture segmentation using Gabor filters[J].Pattern Recognition,1991,24(12):1167-1186.
[14] DUNN D,HIGGINS W E.Optimal Gabor filter for texture segmentation[J].IEEE Transactions on Image Processing,1995,4(7):947-964.
[15] TEUNER A,PICHLER O,HOSTICKA B J.Unsupervised texture segmentation of images using tuned matched Gabor filters[J].IEEE Transactions on Image Processing,1995,4(6):863-870.
[16]陈小光,封举富.Gabor滤波器的快速实现[J].自动化学报,2007,33(5):456-461.
[17] WELDON T P,HIGGINS W E.Multiscale Rician approach to Gabor filter design for texture segmentation[J].IEEE Transactions on Image Processing,1994,2:620-624.
[18]曾姝彦,张广军,李秀智.基于Gabor滤波器的图像目标识别方法[J].北京航空航天大学学报,2006,32(8):954-957.
[19] TAN T N.Texture edge detection by modelling visual cortical channels[J].Pattern Recognition,1995,28(9):1283-1298.
[20] LI M,STAUNTON R C.Optimum Gabor filter design and local binary patterns for texture segmentation[J].Pattern Recognition Letters,2008,29(5):664-672.
[2] KE J,ZHAO Q,SHI P.Blind way recognition algorithm based on image processing[J].Computer Engineering,2009,35(1):189-191.
[3]孙滔.基于颜色空间的图像特征提取的研究[D].长春:吉林大学,2006.
[4] TUCERYAN M,JAIN A K.Texture analysis[M].Singapore:World Scientific Publishing,1998.
[5] FARROKHNIA F,Jain A K.Unsupervised texture segmentation using Gabor filters[J].Pattern Recognition,1991,24(12):1167-1186.
[6] VAPNIK V N.The nature of statistic learning theory[M].New York:Springer,1995.
[7]张岩.基于SVM算法的文本分类器实现[D].成都:电子科技大学,2011.
[8]杨红,罗飞.基于混沌优化的LS-SVM非线性预测控制方法[J].计算机工程与应用,2010,46(5):229-232.
[9]奉国和.SVM分类核函数及参数选择比较[J].计算机工程与应用,2011,47(3):123-124.
[10]陶卿,王常波,方廷健.一种求解闭凸集上二次规划问题的神经网络模型[J].模式识别与人工智能,1998(1):7-11.
[11]梁燕.SVM分类器的扩展及其应用研究[D].长沙:湖南大学,2008.
[12] Chen T W,CHEN Y L,CHIEN S Y.Fast image segmentation based on K-Means clustering with histograms in HSV color space[C].IEEE Workshop on Multimedia Signal Processing,2008:322-325.
[13] FARROKHNIA F,JAIN A K.Unsupervised texture segmentation using Gabor filters[J].Pattern Recognition,1991,24(12):1167-1186.
[14] DUNN D,HIGGINS W E.Optimal Gabor filter for texture segmentation[J].IEEE Transactions on Image Processing,1995,4(7):947-964.
[15] TEUNER A,PICHLER O,HOSTICKA B J.Unsupervised texture segmentation of images using tuned matched Gabor filters[J].IEEE Transactions on Image Processing,1995,4(6):863-870.
[16]陈小光,封举富.Gabor滤波器的快速实现[J].自动化学报,2007,33(5):456-461.
[17] WELDON T P,HIGGINS W E.Multiscale Rician approach to Gabor filter design for texture segmentation[J].IEEE Transactions on Image Processing,1994,2:620-624.
[18]曾姝彦,张广军,李秀智.基于Gabor滤波器的图像目标识别方法[J].北京航空航天大学学报,2006,32(8):954-957.
[19] TAN T N.Texture edge detection by modelling visual cortical channels[J].Pattern Recognition,1995,28(9):1283-1298.
[20] LI M,STAUNTON R C.Optimum Gabor filter design and local binary patterns for texture segmentation[J].Pattern Recognition Letters,2008,29(5):664-672.