A Measure of Texture Directionality

Manil Maskey, Timothy Newman

Abstract

Determining the directionality (i.e., orientedness) of textures is considered here. The work has three major components. The first component is a new method that indicates if a texture is directional or not. The new method considers both local and global aspects of a texture’s directionality. Local pixel intensity differences provide most of the local aspect. A frequency domain analysis provides most of the global aspect. The second component is a comparison study (based on the complete set of Brodatz textures) of the method versus the known, competing methods for determining texture directionality. The third component is a user study of the method’s utility.

References

  1. Abbadeni, N. (2000). Autocovariance-based perceptual textural features corresponding to human visual perception. In Proc., Int'l Conf. on Pattern Recognition 7800, volume 3, pages 901-904.
  2. Abbadeni, N., Zhou, D., and Wang, S. (2000). Computational measures corresponding to perceptual textural features. In Proc., Int'l Conf. on Image Processing 7800, volume 3, pages 897-900.
  3. Beck, J. (1982). Textural Segmentation, in Organization and Representation in Perception. Hillsdale, NY: Erlbaum.
  4. Blake, R. and Holopigan, K. (1985). Orientation selectivity in cats and humans assessed by masking. Vision Research, 25(10):1459-1467.
  5. Cao, F., Guichard, F., and Hornung, H. (2009). Measuring texture sharpness of a digital camera.
  6. Chetverikov., D. (1984). Measuring the degree of texture regularity. in proc. international conf. on pattern recognition. In Proc., International Conf. on Pattern Recognition, pages 80-82.
  7. Chetverikov, D. and Hanbury, A. (2002). Finding defects in texture using regularity and local orientation. Pattern Recognition, 35(10):2165-2180.
  8. Freeman, W. and Adelson, E. (1991). The design and use of steerable filters. IEEE Trans. Pattern Anal. and Machine Intel., 13(9):891-906.
  9. Gorkani, M. and Picard, R. (1994). Texture orientation for sorting photos ”at a glance”. In Pattern Recognition, 1994. Vol. 1 - Conference A: Computer Vision amp; Image Processing., Proceedings of the 12th IAPR International Conference on, volume 1, pages 459-464 vol.1.
  10. Hagh-Shenas, H. and Interrante, V. (2005). A closer look at texture metrics. In Proc., 2nd Symp. on Applied Perception in Graphics and Vis. (APGV 7805), pages 176-176.
  11. Haralick, R. (1979). Statistical and structural approaches to texture. Proceedings of the IEEE, 67(5):786-804.
  12. Hawkins, J. K. (1970). Picture Processing and Psychopictorics. Academic Press, New York, NY, USA, as cited by W. K. Pratt, Digital Image Processing 2nd Ed., 1991, Wiley.
  13. Healey, C. and Enns, J. (1999). Large datasets at a glance: Combining textures and colors in scientific visualization. IEEE Trans. Vis. and Computer Graphics, 5(2):145-167.
  14. Hubel, D. and Wiesel, T. (1968). Receptive fields and functional architecture of monkey striate cortex. Physiology, 195:215-243.
  15. Jackson, S. L. (2009). Research Methods and Statistics : A Critical Thinking Approach. Wadsworth Cengage Learning, Belmont, CA.
  16. Kekre, H., Thepade, S. D., Jain, J., and Agrawal, N. (2010). Article:iris recognition using texture features extracted from haarlet pyramid. International Journal of Computer Applications, 11(12):1-5. Published By Foundation of Computer Science.
  17. Manjunath, B., Ohm, J.-R., Vasudevan, V., and Yamada, A. (2001). Color and texture descriptors. Circuits and Systems for Video Technology, IEEE Transactions on, 11(6):703-715.
  18. Mudigonda, N. R., Rangayyan, R. M., and Desautels, J. L. (2001). Detection of breast masses in mammograms by density slicing and texture flow-field analysis. Medical Imaging, IEEE Transactions on, 20(12):1215-1227.
  19. Nothdurft, C. (1985). Sensitivity for structure gradient in texture discrimination tasks. Vision Research, 25:1957-1968.
  20. Nothdurft, C. (1990). Texton segregation by associated differences in global and local illuminance distribution. In Proc., R Soc Lond Ser B Biol Sci, pages 295-320.
  21. Nothdurft, C. (1991). Texture segmentation and pop-out from orientation contrast. Vision Research, 31:1073- 1078.
  22. Ojala, T., Pietikainen, M., and Maenpaa, T. (2002). Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 24(7):971-987.
  23. Picard, R. and Gorkani, M. (1992). Finding perceptually dominant orientations in natural textures. Spatial Vision, 8(2):221-253.
  24. Saha, S., Das, A., and Chanda, B. (2004). Cbir using perception based texture and colour measures. In Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on, volume 2, pages 985-988 Vol.2.
  25. Shiranita, K., Miyajima, T., and Takiyama, R. (1998). Determination of meat quality by texture analysis. Pattern Recognition Letters, 19(14):1319 - 1324.
  26. Sikora, T. (2001). The mpeg-7 visual standard for content description-an overview. Circuits and Systems for Video Technology, IEEE Transactions on, 11(6):696- 702.
  27. Smith, J. and Chang, S.-F. (1996). Automated binary texture feature sets for image retrieval. In Acoustics, Speech, and Signal Processing, 1996. ICASSP-96. Conference Proceedings., 1996 IEEE International Conference on, volume 4, pages 2239-2242 vol. 4.
  28. Tamura, H., Mori, S., and Yamawaki, T. (1978). Textural features corresponding to visual perception. IEEE Trans. Sys., Man and Cybernetics, 8(6):460-473.
  29. Ware, C. and Knight, W. (1992). Orderable dimensions of visual texture for data display: Orientation, size, and contrast. In Proc., ACM Conf. on Human Factors in Computing Sys. 7892, pages 203-209.
  30. Wu, P., Manjunanth, B., Newsam, S., and Shin, H. (1999). A texture descriptor for image retrieval and browsing. In Content-Based Access of Image and Video Libraries, 1999. (CBAIVL 7899) Proceedings. IEEE Workshop on, pages 3-7.
Download


Paper Citation


in Harvard Style

Maskey M. and Newman T. (2015). A Measure of Texture Directionality . In Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2015) ISBN 978-989-758-090-1, pages 432-438. DOI: 10.5220/0005312904320438


in Bibtex Style

@conference{visapp15,
author={Manil Maskey and Timothy Newman},
title={A Measure of Texture Directionality},
booktitle={Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2015)},
year={2015},
pages={432-438},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005312904320438},
isbn={978-989-758-090-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2015)
TI - A Measure of Texture Directionality
SN - 978-989-758-090-1
AU - Maskey M.
AU - Newman T.
PY - 2015
SP - 432
EP - 438
DO - 10.5220/0005312904320438