Skip to main content
SpringerLink
Log in

A Color Image Segmentation Scheme for Extracting Foreground from Images with Unconstrained Lighting Conditions

  • Conference paper
  • First Online:
Book cover Intelligent Systems Technologies and Applications 2016 (ISTA 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 530))

Abstract

Segmentation plays a functional role in most of the image processing operations. In applications like object recognition systems, the efficiency of segmentation must be assured. Most of the existing segmentation techniques have failed to filter shadows and reflections from the image and the computation time required is marginally high to use in real time applications. This paper proposes a novel method for an unsupervised segmentation of foreground objects from a non-uniform image background. With this approach, false detections due to shadows, reflections from light sources and other noise components can be avoided at a fair level. The algorithm works on an adaptive thresholding, followed by a series of morphological operations in low resolution downsampled image and hence, the computational overhead can be minimized to a desired level. The segmentation mask thus obtained is then upsampled and applied to the full resolution image. So the proposed technique is best suited for batch segmentation of high-resolution images.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 259.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. R. C. Gonzalez, et al.: Digital Image Processing. 3rd edition, Prentice Hall, ISBN 9780131687288, 2008.

    Google Scholar 

  2. C. Wang and B. Yang.: An unsupervised object-level image segmentation method based on foreground and background priors, 2016 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI), Santa Fe, NM, 2016, pp. 141-144.

    Google Scholar 

  3. Xiaomu Song and Guoliang Fan.: A study of supervised, semi-supervised and unsupervised multiscale Bayesian image segmentation. Circuits and Systems, 2002. MWSCAS-2002. The 2002 45th Midwest Symposium on, 2002, pp. II-371-II-374 vol.2.

    Google Scholar 

  4. Otsu, N.: A threshold selection method from gray level histogram, IEEE Trans. Syst. Man Cybern., 1979, 9, (1), pp. 62–66

    Google Scholar 

  5. T. Sziranyi and J. Zerubia.: Markov random field image segmentation using cellular neural network.IEEE Transactions on Circuits and Systems I.Fundamental Theory and Applications, vol. 44, no. 1, pp. 86-89, Jan 1997

    Google Scholar 

  6. S. Zhu, X. Xia, Q. Zhang and K. Belloulata.: An Image Segmentation Algorithm in Image Processing Based on Threshold Segmentation.Signal-Image Technologies and Internet-Based System, 2007. SITIS ‘07. Third International IEEE Conference on, Shanghai, 2007, pp. 673-678.

    Google Scholar 

  7. R. Thendral, A. Suhasini and N. Senthil.: A comparative analysis of edge and color based segmentation for orange fruit recognition.Communications and Signal Processing (ICCSP), 2014 International Conference on, Melmaruvathur, 2014, pp. 463-466

    Google Scholar 

  8. Z. Ren, S. Gao, L. T. Chia and I. W. H. Tsang.: Region-Based Saliency Detection and Its Application in Object Recognition.IEEE Transactions on Circuits and Systems for Video Technology,May 2014 vol. 24, no. 5, pp. 769-779,

    Google Scholar 

  9. Md. Imrul Jubair, M. M. Rahman, S. Ashfaqueuddin and I. Masud Ziko.: An enhanced decision based adaptive median filtering technique to remove Salt and Pepper noise in digital images. Computer and Information Technology (ICCIT), 2011 14th International Conference on, Dhaka, 2011, pp. 428-433.

    Google Scholar 

  10. Liang Chen, Lei Guo and Ning Yang Yaqin Du.: Multi-level image thresholding. based on histogram voting. 2nd International Congress on Image and Signal Processing, CISP ’09., Tianjin, 2009

    Google Scholar 

  11. Ashraf A. Aly1, Safaai Bin Deris2, Nazar Zaki3.: Research Review for Digital Image Segmentation techniques International Journal of Computer Science & Information Technology (IJCSIT) Vol 3, No 5, Oct 2011

    Google Scholar 

  12. Arti Taneja; Priya Ranjan; Amit Ujjlayan.: A performance study of image segmentation techniques Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions), 4th International Conference, 2015

    Google Scholar 

  13. Kass M,Witkin A,Terzopoulos D.: Snake:active contour models. Proc.Of 1st Intern Conf on Computer Vision, London,1987,321~331

    Google Scholar 

  14. G. Wan, X. Huang and M. Wang.: An Improved Active Contours Model Based on Morphology for Image Segmentation. Image and Signal Processing, 2009. CISP ‘09. 2nd International Congress on, Tianjin, 2009, pp. 1-5

    Google Scholar 

  15. B. Wu and Y. Yang.: Local-and global-statistics-based active contour model for image segmentation. Mathematical Problems in Engineering, vol. 2012

    Google Scholar 

  16. S. Kim, Y. Kim, D. Lee and S. Park.: Active contour segmentation using level set function with enhanced image from prior intensity. 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Milan, 2015, pp. 3069-3072.

    Google Scholar 

  17. T. Duc Bui, C. Ahn and J. Shin.: Fast localised active contour for inhomogeneous image segmentation. IET Image Processing, vol. 10, no. 6, pp. 483-494, 6 2016.

    Google Scholar 

  18. J. Moinar, A. I. Szucs, C. Molnar and P. Horvath.: Active contours for selective object segmentation. 2016 IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Placid, NY, 2016, pp. 1-9.

    Google Scholar 

  19. Thyagarajan, H. Bohlmann and H. Abut.: Image coding based on segmentation using region growing. Acoustics, Speech, and Signal Processing. IEEE International Conference on ICASSP ‘87., 1987, pp. 752-755

    Google Scholar 

  20. Jun Tang.: A color image segmentation algorithm based on region growing. Computer Engineering and Technology (ICCET), 2010 2nd International Conference on, Chengdu, 2010, pp. V6-634-V6-637

    Google Scholar 

  21. X. Yu and J. Yla-Jaaski.: A new algorithm for image segmentation based on region growing and edge detection. Circuits and Systems, 1991., IEEE International Symposium on, 1991, pp. 516-519 vol.1

    Google Scholar 

  22. Ahlem Melouah.: Comparison of Automatic Seed Generation Methods for Breast Tumor Detection Using Region Growing Technique. Computer Science and Its Applications, Volume 456 of the series IFIP Advances in Information and Communication Technology. pp 119-128

    Google Scholar 

  23. S. Mukherjee and S. T. Acton.: Region Based Segmentation in Presence of Intensity Inhomogeneity Using Legendre Polynomials. IEEE Signal Processing Letters, vol. 22, no. 3, March 2015, pp. 298-302

    Google Scholar 

  24. P. K. Jain and S. Susan.: An adaptive single seed based region growing algorithm for color image segmentation. 2013 Annual IEEE India Conference (INDICON), Mumbai, 2013, pp. 1-6.

    Google Scholar 

  25. D H Al Saeed, A. Bouridane, A. ElZaart, and R. Sammouda.: Two modified Otsu image segmentation methods based on Lognormal and Gamma distribution models. Information Technology and e-Services (ICITeS), 2012 International Conference on, Sousse, 2012, pp. 1-5.

    Google Scholar 

  26. Q. Chen, L. Zhao, J. Lu, G. Kuang, N. Wang and Y. Jiang.: Modified two-dimensional Otsu image segmentation algorithm and fast realization. IET Image Processing, vol. 6, no. 4, , June 2012, pp. 426-433

    Google Scholar 

  27. C. Zhou, L. Tian, H. Zhao and K. Zhao.: A method of Two-Dimensional Otsu image threshold segmentation based on improved Firefly Algorithm. Cyber Technology in Automation, Control, and Intelligent Systems (CYBER), 2015 IEEE International Conference on, Shenyang, 2015, pp. 1420-1424.

    Google Scholar 

  28. Serge Beucher and Christian Lantuéj.: Uses of watersheds in contour detection. Workshop on image processing, real-time edge and motion detection/estimation, Rennes, France (1979)

    Google Scholar 

  29. L Vincent and P Soille.: Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 13, no. 6, Jun 1991, pp. 583-598

    Google Scholar 

  30. Serge Beucher and Fernand Meyer.: The morphological approach to segmentation: the watershed transformation. Mathematical Morphology in Image Processing (Ed. E. R. Dougherty), pages 433–481 (1993).

    Google Scholar 

  31. Norberto Malpica, Juan E Ortufio, Andres Santos.: A multichannel watershed-based algorithm for supervised texture segmentation. Pattern Recognition Letters, 2003, 24 (9-10): 1545-1554

    Google Scholar 

  32. M. H. Rahman and M. R. Islam.: Segmentation of color image using adaptive thresholding and masking with watershed algorithm. Informatics, Electronics & Vision (ICIEV), 2013 International Conference on, Dhaka, 2013, pp. 1-6

    Google Scholar 

  33. A. Shiji and N. Hamada: Color image segmentation method using watershed algorithm and contour information. Image Processing, 1999. ICIP 99. Proceedings. 1999 International Conference on, Kobe, 1999, pp. 305-309 vol.4

    Google Scholar 

  34. G. M. Zhang, M. M. Zhou, J. Chu and J. Miao.: Labeling watershed algorithm based on morphological reconstruction in color space. Haptic Audio Visual Environments and Games (HAVE), 2011 IEEE International Workshop on, Hebei, 2011, pp. 51-55

    Google Scholar 

  35. Qinghua Ji and Ronggang Shi.: A novel method of image segmentation using watershed transformation. Computer Science and Network Technology (ICCSNT), 2011 International Conference on, Harbin, 2011, pp. 1590-1594

    Google Scholar 

  36. B. Han.: Watershed Segmentation Algorithm Based on Morphological Gradient Reconstruction. Information Science and Control Engineering (ICISCE), 2015 2nd International Conference on, Shanghai, 2015, pp. 533-536

    Google Scholar 

  37. Y. Chen and J. Chen.: A watershed segmentation algorithm based on ridge detection and rapid region merging. Signal Processing, Communications and Computing (ICSPCC), 2014 IEEE International Conference on, Guilin, 2014, pp. 420-424.

    Google Scholar 

  38. S. Chebbout and H. F. Merouani.: Comparative Study of Clustering Based Colour Image Segmentation Techniques. Signal Image Technology and Internet Based Systems (SITIS), 2012 Eighth International Conference on, Naples, 2012, pp. 839-844.

    Google Scholar 

  39. J. Xie and S. Jiang.: A Simple and Fast Algorithm for Global K-means Clustering. Education Technology and Computer Science (ETCS), 2010 Second International Workshop on, Wuhan, 2010, pp. 36-40

    Google Scholar 

  40. S. Vij, S. Sharma and C. Marwaha.: Performance evaluation of color image segmentation using K means clustering and watershed technique. Computing, Communications and Networking Technologies (ICCCNT), 2013. Fourth International Conference on, Tiruchengode, 2013, pp. 1-4

    Google Scholar 

  41. N. A. Mat Isa, S. A. Salamah and U. K. Ngah.: Adaptive fuzzy moving K-means clustering algorithm for image segmentation. in IEEE Transactions on Consumer Electronics, vol. 55, no. 4, November 2009, pp. 2145-2153

    Google Scholar 

  42. Hui Xiong, Junjie Wu.: Kmeans Clustering versus Validation Measures: A Data Distribution Perspective, 2006

    Google Scholar 

  43. Jimmy Nagau, Jean-Luc Henry. L.: An optimal global method for classification of color pixels. International Conference on Complex, Intelligent and Software Intensive Systems 2010

    Google Scholar 

  44. Feng Ge, Song Wang, Tiecheng Liu.: New benchmark for image segmentation evaluation. Journal of Electronic Imaging 16(3), 033011 (Jul–Sep 2007)

    Google Scholar 

  45. Ran Jin,Chunhai Kou,Ruijuan Liu,Yefeng Li.: A Color Image Segmentation Method Based on Improved K-Means Clustering Algorithm. International Conference on Information Engineering and Applications (IEA) 2012, Lecture Notes in Electrical Engineering 217

    Google Scholar 

  46. C. Y. Lien, C. C. Huang, P. Y. Chen and Y. F. Lin, “An Efficient Denoising Architecture for Removal of Impulse Noise in Images,” in IEEE Transactions on Computers, vol. 62, no. 4, pp. 631-643, April 2013 doi: 10.1109/TC.2011.256.

    Google Scholar 

  47. R. Bernstein.: Adaptive nonlinear filters for simultaneous removal of different kinds of noise in images. IEEE Transactions on Circuits and Systems, vol. 34, no. 11, Nov 1987, pp. 1275-1291

    Google Scholar 

  48. Weibo Yu, Yanhui, Liming Zheng, Keping Liu.: Research of Improved Adaptive Median Filter Algorithm. Proceedings of the 2015 International Conference on Electrical and Information Technologies for Rail Transportation Volume 378 of the series Lecture Notes in Electrical Engineering. pp 27-34

    Google Scholar 

  49. K. Manglem Singh and P. K. Bora.: Adaptive vector median filter for removal impulses from color images. Circuits and Systems, 2003. ISCAS ‘03. Proceedings of the 2003 International Symposium on, 2003, pp. II-396-II-399 vol.2

    Google Scholar 

  50. J. Pont-Tuset and F. Marques.: Supervised Evaluation of Image Segmentation and Object Proposal Techniques. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 38, no. 7, July 1 2016, pp. 1465-1478

    Google Scholar 

  51. T. C. W. Landgrebe, P. Paclik and R. P. W. Duin.: Precision-recall operating characteristic (P-ROC) curves in imprecise environments.18th International Conference on Pattern Recognition (ICPR’06), Hong Kong, 2006, pp. 123-127.

    Google Scholar 

  52. J. Canny.: Computational Approach to Edge Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. PAMI-8, no. 6, Nov. 1986, pp. 679-698

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Indian Institute of Information Technology and Management, Kerala, India

    S. Niyas, P. Reshma & Sabu M. Thampi

Authors
  1. S. Niyas
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. P. Reshma
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sabu M. Thampi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to S. Niyas .

Editor information

Editors and Affiliations

  1. Dept. of CS, School of Science, University of Salamanca Dept. of CS, School of Science, Salamanca, Spain

    Juan Manuel Corchado Rodriguez

  2. Machine Intelligence Unit, Indian Statistical Institute Machine Intelligence Unit, Kolkata, India

    Sushmita Mitra

  3. and Management – Kerala (IIITM-K), Indian Inst. of Information Technology and Management – Kerala (IIITM-K), Trivandrum, Kerala, India

    Sabu M. Thampi

  4. King Fahd Univ of Petroleum and Minerals , Dhahran, Saudi Arabia

    El-Sayed El-Alfy

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing AG

About this paper

Cite this paper

Niyas, S., Reshma, P., Thampi, S.M. (2016). A Color Image Segmentation Scheme for Extracting Foreground from Images with Unconstrained Lighting Conditions. In: Corchado Rodriguez, J., Mitra, S., Thampi, S., El-Alfy, ES. (eds) Intelligent Systems Technologies and Applications 2016. ISTA 2016. Advances in Intelligent Systems and Computing, vol 530. Springer, Cham. https://doi.org/10.1007/978-3-319-47952-1_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-47952-1_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-47951-4

  • Online ISBN: 978-3-319-47952-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation