An Efficient Obstacle Detection Algorithm Using Colour and Texture
This paper presents a new classification algorithm using colour and texture for obstacle detection. Colour information is computationally cheap to learn and process. However in many cases, colour alone does not provide enough information for classification. Texture information can improve classification performance but usually comes at an expensive cost. Our algorithm uses both colour and texture features but texture is only needed when colour is unreliable. During the training stage, texture features are learned specifically to improve the performance of a colour classifier. The algorithm learns a set of simple texture features and only the most effective features are used in the classification stage. Therefore our algorithm has a very good classification rate while is still fast enough to run on a limited computer platform. The proposed algorithm was tested with a challenging outdoor image set. Test result shows the algorithm achieves a much better trade-off between classification performance and efficiency than a typical colour classifier.
[1] P. E. Richard O.Duda and D. G.Stork, Pattern Classification. Inter-
Science, 2001.
[2] I. Ulrich and I. R. NourbakhshLo, "Appearance-based obstacle detection
with monocular color vision," in AAAI/IAAI, 2000, pp. 866-871.
[3] M. Sridharan and P. Stone, "Color learning and illumination invariance
on mobile robots: A survey," Robotics and Autonomous Systems, vol. 57,
no. 6-7, pp. 629 - 644, 2009.
[4] L. Lorigo, R. Brooks, and W. Grimsou, "Visually-guided obstacle avoidance
in unstructured environments," in Intelligent Robots and Systems,
1997. IROS -97., Proceedings of the 1997 IEEE/RSJ International
Conference on, vol. 1, Grenoble, France, Sep. 1997, pp. 373-379.
[5] T. S. team, "Stanley : The robot that won the darpa grand challenge,"
Stanford University, Tech. Rep., 2005.
[6] S. Belongie, C. Carson, H. Greenspan, and J. Malik, "Color- and texturebased
image segmentation using em and its application to content-based
image retrieval," iccv, vol. 00, p. 675, 1998.
[7] Y.-C. Cheng and S.-Y. Chen, "Image classification using color, texture
and regions," Image and Vision Computing, vol. 21, no. 9, pp. 759-776,
Sep. 2003.
[8] D. Cremers, M. Rousson, and R. Deriche, "A review of statistical
approaches to level set segmentation: Integrating color, texture, motion
and shape," International Journal of Computer Vision, vol. 72, no. 2,
pp. 195-215, Apr. 2007.
[9] M. Shneier, T. Chang, T. Hong, W. Shackleford, R. Bostelman, and J. Albus,
"Learning traversability models for autonomous mobile vehicles,"
Autonomous Robots, vol. 24, no. 1, pp. 69-86, Jan. 2008.
[10] J. Michels, A. Saxena, and A. Y. Ng, "High speed obstacle avoidance
using monocular vision and reinforcement learning," in ICML -05:
Proceedings of the 22nd international conference on Machine learning.
New York, NY, USA: ACM Press, 2005, pp. 593-600.
[11] R. Haralick, "Statistical and structural approaches to texture," Proceedings
of the IEEE, vol. 67, no. 5, pp. 786-804, May 1979.
[12] T. P. Weldon, W. E. Higgins, and D. F. Dunn, "Efficient gabor filter
design for texture segmentation," Pattern Recognition, vol. 29, no. 12,
pp. 2005-2015, Dec. 1996.
[13] M. Unser, "Texture classification and segmentation using wavelet
frames," Image Processing, IEEE Transactions on, vol. 4, no. 11, pp.
1549-1560, Nov 1995.
[14] T. Ojala, M. Pietikinen, and D. Harwood, "A comparative study of
texture measures with classification based on featured distributions,"
Pattern Recognition, vol. 29, no. 1, pp. 51-59, Jan. 1996.
[15] S. Grigorescu, N. Petkov, and P. Kruizinga, "Comparison of texture
features based on gabor filters," Image Processing, IEEE Transactions
on, vol. 11, no. 10, pp. 1160-1167, Oct 2002.
[16] T. Randen and J. Husoy, "Filtering for texture classification: a comparative
study," Pattern Analysis and Machine Intelligence, IEEE Transactions
on, vol. 21, no. 4, pp. 291-310, Apr 1999.
[17] T. Leung and J. Malik, "Representing and recognizing the visual
appearance of materials using three-dimensional textons," Int. J. Comput.
Vision, vol. 43, no. 1, pp. 29-44, 2001.
[18] "http://www.gumstix.org."
[1] P. E. Richard O.Duda and D. G.Stork, Pattern Classification. Inter-
Science, 2001.
[2] I. Ulrich and I. R. NourbakhshLo, "Appearance-based obstacle detection
with monocular color vision," in AAAI/IAAI, 2000, pp. 866-871.
[3] M. Sridharan and P. Stone, "Color learning and illumination invariance
on mobile robots: A survey," Robotics and Autonomous Systems, vol. 57,
no. 6-7, pp. 629 - 644, 2009.
[4] L. Lorigo, R. Brooks, and W. Grimsou, "Visually-guided obstacle avoidance
in unstructured environments," in Intelligent Robots and Systems,
1997. IROS -97., Proceedings of the 1997 IEEE/RSJ International
Conference on, vol. 1, Grenoble, France, Sep. 1997, pp. 373-379.
[5] T. S. team, "Stanley : The robot that won the darpa grand challenge,"
Stanford University, Tech. Rep., 2005.
[6] S. Belongie, C. Carson, H. Greenspan, and J. Malik, "Color- and texturebased
image segmentation using em and its application to content-based
image retrieval," iccv, vol. 00, p. 675, 1998.
[7] Y.-C. Cheng and S.-Y. Chen, "Image classification using color, texture
and regions," Image and Vision Computing, vol. 21, no. 9, pp. 759-776,
Sep. 2003.
[8] D. Cremers, M. Rousson, and R. Deriche, "A review of statistical
approaches to level set segmentation: Integrating color, texture, motion
and shape," International Journal of Computer Vision, vol. 72, no. 2,
pp. 195-215, Apr. 2007.
[9] M. Shneier, T. Chang, T. Hong, W. Shackleford, R. Bostelman, and J. Albus,
"Learning traversability models for autonomous mobile vehicles,"
Autonomous Robots, vol. 24, no. 1, pp. 69-86, Jan. 2008.
[10] J. Michels, A. Saxena, and A. Y. Ng, "High speed obstacle avoidance
using monocular vision and reinforcement learning," in ICML -05:
Proceedings of the 22nd international conference on Machine learning.
New York, NY, USA: ACM Press, 2005, pp. 593-600.
[11] R. Haralick, "Statistical and structural approaches to texture," Proceedings
of the IEEE, vol. 67, no. 5, pp. 786-804, May 1979.
[12] T. P. Weldon, W. E. Higgins, and D. F. Dunn, "Efficient gabor filter
design for texture segmentation," Pattern Recognition, vol. 29, no. 12,
pp. 2005-2015, Dec. 1996.
[13] M. Unser, "Texture classification and segmentation using wavelet
frames," Image Processing, IEEE Transactions on, vol. 4, no. 11, pp.
1549-1560, Nov 1995.
[14] T. Ojala, M. Pietikinen, and D. Harwood, "A comparative study of
texture measures with classification based on featured distributions,"
Pattern Recognition, vol. 29, no. 1, pp. 51-59, Jan. 1996.
[15] S. Grigorescu, N. Petkov, and P. Kruizinga, "Comparison of texture
features based on gabor filters," Image Processing, IEEE Transactions
on, vol. 11, no. 10, pp. 1160-1167, Oct 2002.
[16] T. Randen and J. Husoy, "Filtering for texture classification: a comparative
study," Pattern Analysis and Machine Intelligence, IEEE Transactions
on, vol. 21, no. 4, pp. 291-310, Apr 1999.
[17] T. Leung and J. Malik, "Representing and recognizing the visual
appearance of materials using three-dimensional textons," Int. J. Comput.
Vision, vol. 43, no. 1, pp. 29-44, 2001.
[18] "http://www.gumstix.org."
@article{"International Journal of Information, Control and Computer Sciences:51076", author = "Chau Nguyen Viet and Ian Marshall", title = "An Efficient Obstacle Detection Algorithm Using Colour and Texture", abstract = "This paper presents a new classification algorithm using colour and texture for obstacle detection. Colour information is computationally cheap to learn and process. However in many cases, colour alone does not provide enough information for classification. Texture information can improve classification performance but usually comes at an expensive cost. Our algorithm uses both colour and texture features but texture is only needed when colour is unreliable. During the training stage, texture features are learned specifically to improve the performance of a colour classifier. The algorithm learns a set of simple texture features and only the most effective features are used in the classification stage. Therefore our algorithm has a very good classification rate while is still fast enough to run on a limited computer platform. The proposed algorithm was tested with a challenging outdoor image set. Test result shows the algorithm achieves a much better trade-off between classification performance and efficiency than a typical colour classifier.
", keywords = "Colour, texture, classification, obstacle detection.", volume = "3", number = "12", pages = "2747-6", }
