The Image Recognition of Mobile Robot Based on CIE Lab Space

Full Text (PDF, 331KB), PP.29-35

Views: 0 Downloads: 0

Author(s)

Xuan Zou 1,* Bin Ge 1

1. College of information and Engineering of Dalian University, Dalian, China

* Corresponding author.

DOI: https://doi.org/10.5815/ijitcs.2014.02.04

Received: 7 Jun. 2013 / Revised: 5 Sep. 2013 / Accepted: 23 Oct. 2013 / Published: 8 Jan. 2014

Index Terms

Image Recognition, CIE Lab Space, Image Segmentation, Robot Vision System

Abstract

The image recognition of mobile robot is to extract the effective target information. The essence of image extraction is image segmentation. By extracting and distinguishing planar objects and three-dimensional objects, we propose two new algorithms. The color image is extracted by using CIE Lab Space. Then we propose a comparison method through the collection of two image samples. According to the principle of geometric distortion in the geometric space, we can easily distinguish the planar object in the environment. Therefore, Experimental results show that the combination of these two methods is accurate and fast in the color image recognition.

Cite This Paper

Xuan Zou, Bin Ge, "The Image Recognition of Mobile Robot Based on CIE Lab Space", International Journal of Information Technology and Computer Science(IJITCS), vol.6, no.2, pp.29-35, 2014. DOI:10.5815/ijitcs.2014.02.04

Reference

[1]Pu Hongyi, Ye Bin, Hou Tingbo. “Mobile robot visual control system”[J]. Robot Technology and Application. 2004,03

[2]Cai Yeqing, Long Yonghong. “Application of soccer robots based on the color characteristics of image segmentation methods”[J]. Computing Technology and Automation. 2008/01

[3]Bergman R, Nachlieli H. “Perceptual Segmentation: Combining Image Segmentation With Object Tagging”[J]. Transactions on Image Processing, 2011/6

[4]Zhang Yujin. “Image processing and analysis”[M]. Beijing: Tsinghua University Press, 2004.

[5]Chen Lixue, Chen Zhaojiong. “Lab space-based image retrieval algorithm”[J]. Computer Engineering, 2008/13

[6]Fan Jiulun, Zhao Feng. “Two-Dimensional Otsu's Curve Thresholding Segmentation Method for Gray-Level Images”[J]. ACTA ELECTRONICA SINICA, 2007, 35(4)

[7]Hu Min, Li Mei, Wang Ronggui. “Improved Otsu algorithm for image segmentation”[J]. Journal of Electronic Measurement and Instrument, 2010(05)

[8]Liu Changyu, Wang Hongjun, Zou Xiangjun. “Agricultural robot vision error research based the epipolar geometry transform”[J]. Computer information, 2010/17.