http://iet.metastore.ingenta.com
1887

Region matching based on colour invariants in rgb orthogonal space

Region matching based on colour invariants in rgb orthogonal space

For access to this article, please select a purchase option:

Buy article PDF
£12.50
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Computer Vision — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Illumination influences the performance of region feature matching based on a grey image. A novel region-matching algorithm based on the colour invariants and colour-invariant moments in rgb orthogonal colour space is proposed. First, a colour image is converted in RGB colour space to rgb orthogonal colour space that has colour invariance. Second, the colour invariants H and C λ are calculated. Then, the maximally stable extremal region is extracted from the colour invariants and the colour-invariant moments are computed. Finally, the nearest neighbour method is used to find corresponding regions. The proposed method can take advantage of both the colour and geometric properties of the images to solve the problem of illumination influences. Experimental results from the Amsterdam Library of Object Images database and images captured on the Beijing Forestry University campus show that performance of the proposed algorithm is better than that of prior art methods.

References

    1. 1)
      • 1. Brown, M., Lowe, D.G.: ‘Automatic panoramic image stitching using invariant features’, Int. J. Comput. Vis., 2007, 74, (1), pp. 5973.
    2. 2)
      • 2. Hartley, R., Zisserman, A.: ‘Multiple view geometry in computer vision’ (Cambridge University Press, New York, 2000, 2003, 2nd edn.).
    3. 3)
      • 3. Liu, L., Jin, H.M., Li, J.X.: ‘Image retrieval based on the local multi-features statistical method’. Proc. Int. Conf. Communication Systems and Network Technologies, Changchun, China, May 2012, pp. 246249.
    4. 4)
      • 4. Pardeshi, S.A., Talbar, S.N.: ‘Face description with local invariant feature: application to face recognition’, Int. J. Comput. Appl., 2010, 1, (24), pp. 6271.
    5. 5)
      • 5. Mikolajczyk, K., Tuytelaars, T., Schmid, C., et al: ‘A comparison of affine region detectors’, Int. J. Comput. Vis., 2005, 65, (1–2), pp. 4372.
    6. 6)
      • 6. Blanca, N.P., Fuertes, J.M., Lucena, M.J.: ‘Matching deformable regions using local histograms of differential invariants’, Lect. Notes Comput. Sci., 2005, 3522, pp. 251258.
    7. 7)
      • 7. Tuytelaars, T., Gool, L.V.: ‘Wide baseline stereo matching based on local, affinely invariant regions’. Proc. 11th British Conf. Machine Vision, Bristol, UK, September 2000, pp. 412425.
    8. 8)
      • 8. Geusebroek, J.M., van den Boomgaard, R., Smeulders, A.W.M., et al: ‘Color constancy from physical principles’, Pattern Recognit. Lett., 2003, 24, (2003), pp. 16531662.
    9. 9)
      • 9. Bosch, A., Zisserman, A., Muoz, X.: ‘Scene classification using a hybrid generative/discriminative approach’, IEEE Trans. Pattern Anal. Mach. Intell., 2008, 30, (4), pp. 712728.
    10. 10)
      • 10. Fowers, S.G., Lee, D.J.: ‘An effective color addition to feature detection and description for book spine image matching’, Int. Sch. Res. Not. Mach. Vis., 2012, 2012, (2012), pp. 115.
    11. 11)
      • 11. Forssen, P.E.: ‘Maximally stable color regions for recognition and matching’. Proc. Int. Conf. Computer Vision and Pattern Recognition, Minneapolis, MN, USA, June 2007, pp. 18.
    12. 12)
      • 12. Corso, J.J., Hager, G.D.: ‘Coherent regions for concise and stable image description’. Proc. IEEE Computer Society Conf. Computer Vision and Pattern Recognition, San Diego, US, June 2005, pp. 184190.
    13. 13)
      • 13. Unnikrishnan, R., Hebert, M.: ‘Extracting scale and illuminant invariant regions through color’. Proc. 17th British Conf. Machine Vision, Edinburgh, UK, September 2006, pp. 499509.
    14. 14)
      • 14. Sotiras, A., Davatzikos, C., Paragios, N.: ‘Deformable medical image registration: a survey’, IEEE Trans. Med. Imaging, 2013, 32, (7), pp. 11531190.
    15. 15)
      • 15. Bay, H., Ess, A., Tuytelaars, T., et al: ‘Speeded-up robust feature’, Comput. Vis. Image Underst., 2008, 110, (3), pp. 346359.
    16. 16)
      • 16. Hu, M.K.: ‘Visual pattern recognition by moment invariants’, IRE Trans. Inf. Theory, 1962, 8, (2), pp. 179187.
    17. 17)
      • 17. Geusebroek, J.M., Smeulders, A.W.M., van den Boomgaard, R.: Measurement of color invariants’. Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2000, vol. 1, no. 2000, pp. 5057.
    18. 18)
      • 18. Geusebroek, J.M., van den Boomgaard, R., Smeulders, A.W.M., et al: ‘Color invariance’, IEEE Trans. Pattern Anal. Mach. Intell., 2001, 23, (12), pp. 13381350.
    19. 19)
      • 19. Karakasis, E.G., Papakostas, G.A., Koulouriotis, D.E., et al: ‘A unified methodology for computing accurate quaternion color moments and moment invariants’, IEEE Trans. Image Process., 2013, 23, (2), pp. 596611.
    20. 20)
      • 20. ‘The Amsterdam Library of Objects Images Database (ALOI)’, available at http://www.aloi.science.uva.nl/, accessed 12 November 2014.
    21. 21)
      • 21. Jafri, R., Ali, S.A., Arabnia, H.R., et al: ‘Computer vision-based object recognition for the visually impaired in an indoors environment: a survey’, Vis. Comput., 2013, 30, (11), pp. 11971222.
    22. 22)
      • 22. Forssen, P.E., Lowe, D.G.: ‘Shape descriptors for maximally stable extremal regions’. Proc. Int. Conf. Computer Vision, Rio de Janeiro, Brazil, October 2007, pp. 18.
    23. 23)
      • 23. Zhou, X.Z., Shi, Y.C., Wang, T.: ‘Icon recognition based on weighted Hu invariant moments in the HSV color space’, J. Nanjing Univ. Sci. Technol., 2005, 29, (3), pp. 363367.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-cvi.2015.0020
Loading

Related content

content/journals/10.1049/iet-cvi.2015.0020
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address