基于颜色纹理信息的盲道识别算法

计算机应用 ›› 2014, Vol. 34 ›› Issue (12): 3585-3588.

基于颜色纹理信息的盲道识别算法

彭玉青,薛杰,郭永芳

河北工业大学计算机科学与软件学院,天津 300401

收稿日期:2014-07-04 修回日期:2014-08-19 出版日期:2014-12-01 发布日期:2014-12-31
通讯作者: 薛杰
作者简介:彭玉青(1969-）,女,湖南永顺人,教授,主要研究方向:智能信息处理、图像处理；薛杰(1989-）,女,天津武清人,硕士研究生,主要研究方向:图像处理、模式识别；郭永芳(1980-）,女,河北正定人,讲师,博士,主要研究方向:计算机视觉、模式识别。
基金资助:
天津市自然科学基金资助项目;河北省科技计划项目

Blind road recognition algorithm based on color and texture information

PENG Yuqing,XUE Jie,GUO Yongfang

College of Computer Science and Software, Hebei University of Technology, Tianjin 300401,China

Received:2014-07-04 Revised:2014-08-19 Online:2014-12-01 Published:2014-12-31
Contact: XUE Jie

摘要/Abstract

摘要：

针对现有盲道识别率低,处理方式单一,且容易受光照、阴影的影响等问题,提出一种改进的盲道识别算法。该方法针对盲道颜色、纹理特性,分别利用颜色直方图特征的阈值分割结合改进的区域生长分割,灰度共生矩阵特征的模糊C均值聚类分割,结合Canny边缘检测和Hough变换算法,使得盲道区域与周围人行区域分开,确定出盲道的偏移方向。实验结果表明,该算法能够更加精准地分割多种类型盲道,检测出盲道区域的边界与行进方向,而且解决了部分光照和阴影问题,能够自适应选择速度最快而且高效的分割方法,可以应用在电子导盲等多种设备中。

Abstract:

Concerning the problem that existing blind road recognition method has low recognition rate, simplistic handling, and is easily influenced by light, or shadow, an improved blind road recognition method was proposed. According to the color and texture features of blind road, the algorithm used two segmentation methods respectively including color histogram feature threshold segmentation combined with improved region growing segmentation and fuzzy C-means clustering segmentation for gray level co-occurrence matrix feature. And combined with Canny edge detection and Hough transform algorithm, the proposed algorithm made the blind area separated from the pedestrian area and determines the migration direction for the blind. The experimental results show that the proposed algorithm can segment several kinds of blind road more accurately, detect the boundary and direction of blind road and solve the light and shadow problem partly. It can choose the fastest and the most effective segmentation method adoptively, and can be used in a variety of devices, such as electronic guide ones.

中图分类号:

TP391.4

彭玉青薛杰郭永芳. 基于颜色纹理信息的盲道识别算法[J]. 计算机应用, 2014, 34(12): 3585-3588.

PENG Yuqing XUE Jie GUO Yongfang. Blind road recognition algorithm based on color and texture information[J]. Journal of Computer Applications, 2014, 34(12): 3585-3588.

参考文献

［1］GOLDING A R, LESH N. Indoor navigation using a diverse set of cheap, wearable sensor［C］// Proceedings of the 3rd International Symposium on Wearable Computer. Piscataway: IEEE, 1999: 29-36.
［2］LOOMIS J M, GOLLEDGE R G, KLATZKY R L, et al. Personal guidance system for the visually impaired［C］// Proceedings of the 1st Annual ACM/SIGGAPH Conference On Assistive Technology. New York: ACM, 1994: 85-91.
［3］ZHOU Y,ZHAO Q. Blind region detection based on color information and follow algorithm［J］. Microcomputer Applications, 2010, 26(8): 47-50.(周毅, 赵群飞. 基于颜色信息的盲道区域检测与跟随算法［J］. 微型电脑应用, 2010, 26(8): 47-50.)
［4］YAN Z, WANG J,DOU R, et al. Sidewalk for the blind adaptive segmentation based on color clustering and line detection［J］. Journal of Tianjin Polytechnic University, 2010, 29(1): 80-84.(闫志杰, 汪剑鸣, 窦汝振, 等. 基于颜色聚类和直线检测的自适应盲道区域分割算法［J］. 天津工业大学学报, 2010, 29(1): 80-84.）
［5］KE J, ZHAO Q, SHI P. Blind way recognition algorithm based on image processing［J］. Computer Engineering, 2009, 35(1): 189-191.(柯剑光, 赵群飞, 施鹏飞. 基于图像处理的盲道识别算法［J］. 计算机工程, 2009, 35(1): 189-191.)
［6］KE J. The recognition system for the blind way based on image processing［D］. Shanghai: Shanghai Jiao Tong University, 2008.(柯剑光. 基于图像处理的盲道识别系统［D］. 上海: 上海交通大学, 2008.)
［7］CHEN W, LIU J. Application of color histogram segmentation method in fire detection［J］. Computer Technology and Development, 2011, 21(7):199-201.(陈威, 刘菁华. 颜色直方图特征分割方法在火灾探测中的应用［J］. 计算机技术与发展, 2011, 21(7): 199-201.)
［8］DENG Y, MANJUNATH B S. Unsupervised segmentation of color-texture regions in images and video［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001, 23(8): 800-810.
［9］LIU J, MA Y, CHEN K. An improved color image segmentation method based on region growing［J］. Computer Applications and Software, 2013, 29(11):288-291.(刘俊, 马燕, 陈坤. 一种改进的基于区域生长的彩色图像分割方法［J］. 计算机应用与软件, 2013, 29(11): 288-291.）
［10］ZHOU C. Canny operator edge detection of research and improvement［D］. Chongqing: Chongqing Normal University, 2012.(周超. 边缘检测 Canny 算子的研究与改进［D］. 重庆: 重庆师范大学, 2012.）
［11］LIANG C. Research on Hough transform for microscopic image segmentation［D］. Chongqing: Chongqing University of Technology, 2012.(梁琛颖. 基于 Hough 变换的显微图像分割方法研究［D］. 重庆:重庆理工大学, 2012.）
［12］WU D, YE N, SU X. Wood defect recognition based on GLCM and clustering algorithm［J］. Computer and Digital Engineering, 2010, 38(11):38-41.(吴东洋, 业宁, 苏小青. 基于灰度共生矩阵和聚类方法的木材缺陷识别［J］. 计算机与数字工程, 2010, 38(11): 38-41.）
［13］RAMPUN A, STRANGE H, ZWIGGELAAR R. Texture segmentation using different orientations of GLCM features［C］// Proceedings of the 6th International Conference on Computer Vision/Computer Graphics Collaboration Techniques and Applications. New York: ACM, 2013: 17.
［14］GONG Q, YAO Y. Image segmentation based on watershed and improved fuzzy c-means clustering［J］. Application Research of Computers, 2012, 28(12):4773-4775.(龚劬, 姚玉敏. 基于分水岭和改进的模糊聚类图像分割［J］. 计算机应用研究, 2012, 28(12): 4773-4775.）

[1]	罗圣钦陈金怡李洪均. 基于注意力机制的多尺度残差UNet实现乳腺癌灶分割[J]. 计算机应用, 0, (): 0-0.
[2]	杨鼎康黄帅王顺利翟鹏李一丹张立华. 基于对抗生成网络和网络集成的面部表情识别方法EE-GAN[J]. 计算机应用, 0, (): 0-0.
[3]	秦庭威赵鹏程秦品乐曾建朝柴锐黄永琦. 基于残差注意力机制的点云配准算法[J]. 计算机应用, 0, (): 0-0.
[4]	鲁永帅唐英杰马鑫然. 基于深度特征融合的无纺布低对比度浆丝缺陷检测方法[J]. 计算机应用, 0, (): 0-0.
[5]	裴仪瑶, 郭会明, 张丹普, 陈文博. 基于定位不确定性的鲁棒3D目标检测方法[J]. 计算机应用, 2021, 41(10): 2979-2984.
[6]	许学斌, 张佳达, 刘伟, 路龙宾, 赵雨晴. 融合空间和通道特征的高精度乳腺癌分类方法[J]. 计算机应用, 2021, 41(10): 3025-3032.
[7]	汪虹余, 张彧, 杨恒, 穆楠. 基于蚁群优化算法的弱光图像显著性目标检测[J]. 计算机应用, 2021, 41(10): 2970-2978.
[8]	魏淳武, 赵涓涓, 唐笑先, 强彦. 基于多时期蒸馏网络的随访数据知识提取方法[J]. 计算机应用, 2021, 41(10): 2871-2878.
[9]	韩建栋, 李晓宇. 基于多尺度特征融合的行人重识别方法[J]. 计算机应用, 2021, 41(10): 2991-2996.
[10]	张凯悦, 张鸿. 基于注意力机制网络的航运监控图像识别模型[J]. 计算机应用, 2021, 41(10): 3010-3016.
[11]	李自强, 王正勇, 陈洪刚, 李林怡, 何小海. 基于外观和动作特征双预测模型的视频异常行为检测[J]. 计算机应用, 2021, 41(10): 2997-3003.
[12]	高世伟, 张长柱, 王祝萍. 基于可分离金字塔的轻量级实时语义分割算法[J]. 计算机应用, 2021, 41(10): 2937-2944.
[13]	卓禹心, 韩素雅, 张榆锋, 李支尧, 董毅峰. 基于超声谐波包络Nakagami参数图像的微波消融区域自动分割方法[J]. 计算机应用, 2021, 41(10): 3089-3096.
[14]	李敬虎邢前国郑向阳李琳王丽丽. 基于深度学习的无人机影像夜光藻赤潮提取方法[J]. 计算机应用, 0, (): 0-0.
[15]	任炜白鹤翔. 基于全局与局部的多标签图像分类方法[J]. 计算机应用, 0, (): 0-0.