Journal of Engineering and Applied Sciences

Year: 2019
Volume: 14
Issue: 18
Page No. 6642 - 6649

Road Lane Detection using Hough Transform Method (Daylight Condition)

Authors : H. Tia Amelia, Agus Virgono and Randy Erfa Saputra

References

Aly, M., 2008. Real time detection of lane markers in urban streets. Proceedings of the 2008 IEEE International Symposium on Intelligent Vehicles, June 4-6, 2008, IEEE, Eindhoven, Netherlands, ISBN:978-1-4244-2568-6, pp: 7-12.

Bilgin, E. and S. Robila, 2016. Road sign recognition system on Raspberry Pi. Proceedings of the 2016 IEEE International Conference on Long Island Systems, Applications and Technology (LISAT), April 29, 2016, IEEE, Farmingdale, New York, USA., ISBN:978-1-4673-8490-2, pp: 1-5.

Borkar, A., M. Hayes, M.T. Smith and S. Pankanti, 2009. A layered approach to robust lane detection at night. Proceedings of the IEEE 2009 International Workshop on Computational Intelligence in Vehicles and Vehicular Systems, March 30-April 2, 2009, IEEE, Nashville, Tennessee, USA., ISBN:978-1-4244-2770-3, pp: 51-57.

Deokar, P.S. and A.R. Kaushik, 2014. Review on distributed canny edge detectorusing FPGA. Intl. J. Adv. Res. Electr. Electron. Instrum. Eng., 3: 11849-11856.
CrossRef  |  Direct Link  |  

Goel, A., 2014. Lane detection techniques-a review. Intl. J. Comput. Sci. Mob. Comput., 3: 596-602.
Direct Link  |  

Kanan, C. and G.W. Cottrell, 2012. Color-to-grayscale: Does the method matter in image?. PloS. One, 7: 1-7.
Direct Link  |  

Kaur, G. and D. Kumar, 2015. Lane detection techniques: A review. Intl. J. Comput. Appl., 112: 4-8.
Direct Link  |  

Kim, Z., 2008. Robust lane detection and tracking in challenging scenarios. IEEE Trans, Intell. Transp. Syst., 9: 16-26.
CrossRef  |  

Low, C.Y., H. Zamzuri and S.A. Mazlan, 2014. Simple robust road lane detection algorithm. Proceedings of the 2014 5th International Conference on Intelligent and Advanced Systems (ICIAS), June 3-5, 2014, IEEE, Kuala Lumpur, Malaysia, ISBN:978-1-4799-4653-2, pp: 1-4.

Macedo, S., G. Melo and J. Kelner, 2015. A comparative study of grayscale conversion techniques applied to SIFT descriptors. SBC. J. Inter. Syst., 6: 30-36.
Direct Link  |  

Mandlik, P.T. and A.B. Deshmukh, 2016. Raspberry-Pi based real time lane departure warning system using image processing. Intl. J. Eng. Res. Technol., 5: 762-766.
CrossRef  |  Direct Link  |  

Mariut, F., C. Fosalau and D. Petrisor, 2012. Lane mark detection using hough transform. Proceedings of the 2012 International Conference and Exposition on Electrical and Power Engineering, October 25-27, 2012, IEEE, Iasi, Romania, ISBN:978-1-4673-1173-1, pp: 871-875.

Mathibela, B., P. Newman and I. Posner, 2015. Reading the road: Road marking classification and interpretation. IEEE. Trans. Intell. Transp. Syst., 16: 2072-2081.
CrossRef  |  Direct Link  |  

Narvekar, N.D. and L.J. Karam, 2011. A no-reference image blur metric based on the cumulative probability of blur detection (CPBD). IEEE. Trans. Image Proc., 20: 2678-2683.
CrossRef  |  PubMed  |  Direct Link  |  

Panwar, S. and S. Raut, 2015. Survey on lane detection using Hough transform technique. Intl. J. Adv. Res. Electr. Electron. Instrum. Eng., 4: 401-405.
CrossRef  |  Direct Link  |  

Song, J. and M.R. Lyu, 2005. Hough transform based line recognition method utilizing both parameter space and image space. Pattern Recognit., 38: 539-552.
CrossRef  |  

Srivastava, S., R. Singal and M. Lumba, 2014. Efficient lane detection algorithm using different filtering techniques. Intl. J. Comput. Appl., 88: 6-11.
CrossRef  |  Direct Link  |  

Wang, Y., E.K. Teoh and D.G. Shen, 2004. Lane detection and tracking using B-Snake. Image Vision Comput., 22: 269-280.
CrossRef  |  

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