Abstract In image processing, segmentation of textural regions from non-textural background has not been given a significant attention, however, considered to be an important problem in texture analysis and segmentation task. In this paper, we have proposed a new method, which fits under the framework of mathematical morphology. The entire procedure is based on recently developed textural descriptor termed as Morphological Texture Contrast (MTC). In this work authors have employed the bright and dark top-hat transformations to handle the bright and dark features separately. Both bright and dark features so extracted are subjected to MTC operator for identification of the texture components which in turn are used to enhance the textured parts of the original input image. Subsequently, our method is employed to segment the bright and dark textured regions separately from the two enhanced versions of the input image. Finally, the partial segmentation results so obtained are combined to constitute the final segmentation result. The method has been formulated, implemented and tested on benchmark textured images. The experimental results along with the performance measures have established the efficacy of the proposed method.

References

[1] Chen J., Zhao G., Salo M., Rahtu E., and Pietikainen M., “Automatic Dynamic Texture Segmentation Using Local Descriptors and Optical Flow,” IEEE Transactions on Image Processing, vol. 22, no. 1, pp. 326-39, 2013.

[2] Davarpanah S., Khalid F., and Abdullah L., “BGLBP-Based Image Background Extraction Method,” The International Arab Journal of Information Technology, vol. 13, no. 6A, pp. 908-914, 2016.

[3] Dougherty E., “Morphological Segmentation for Textures and Particles,” in Proceedings of Digital image Processing Methods, New York, pp. 43-102, 1994.

[4] Gonzalez R., Digital Image Processing, Pearson Education, 2009.

[5] Jain A. and Farrokhnia F., “Unsupervised Texture Segmentation Using Gabor Filters,” Texture Segmentation from Non-Textural Background Using Enhanced MTC 745 Pattern Recognition, vol. 24, no. 12, pp. 1167- 1186, 1991.

[6] Kashyap R., Chellappa R., and Ahuja N., “Decision Rules for Choice of Neighbors in Random Field Models of Images,” Computer Graphics and Image Processing, vol. 15, no. 4, pp. 301-318, 1981.

[7] Konishi S. and Yuille A., “Statistical Cues for Domain Specific Image Segmentation with Performance Analysis,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Hilton Head Island, pp. 125-132, 2000.

[8] Malik J., Belongie S., Shi J., and Leung T., “Textons Contours and Regions: Cue Integration in Image Segmentation,” in Proceedings of the 7th IEEE International Conference on Computer Vision, Kerkyra, pp. 918-925, 1999.

[9] Manjunath B. and Chellappa R., “Unsupervised Texture Segmentation Using Markov Random Field Models,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 13, no. 5, pp. 478-482, 1991.

[10] Mao J. and Jain A., “Texture Classification and Segmentation Using Multiresolution Simultaneous Autoregressive Models,” Pattern Recognition, vol. 25, no. 2, pp. 173-188, 1992.

[11] Mukhopadhyay S. and Chanda B., “An Edge Preserving Noise Smoothing Technique Using Multiscale Morphology,” Signal Processing, vol. 82, no. 4, pp. 527-544, 2002.

[12] Mukhopadhyay S. and Chanda B., “Multiscale Morphological Segmentation of Gray-Scale Images,” IEEE Transactions on Image Processing, vol. 12, no. 5, pp. 533-549, 2003.

[13] Ojala T., Pietikainen M., and Maenpaa T., “Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 7, pp. 971-87, 2002.

[14] Otsu N., “A Threshold Selection Method from Gray-Level Histograms,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 9, no. 1, pp. 62-66, 1979.

[15] Pentland A., “Fractal-Based Description of Natural Scenes,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 6, pp. 661-674, 1984.

[16] Rafi M. and Mukhopadhyay S., “Segmentation of Synthetic Textures Employing Gabor Filter Magnitude in a Multi Channeling Environment,” in Proceedings of 13th International Conference on Signal-Image Technology and Internet-Based Systems, Jaipur, pp. 269-274, 2017.

[17] Zingman I., Saupe D., and Lambers K., “Morphological Operators for Segmentation of High Contrast Textured Regions in Remotely Sensed Imagery,” in Proceedings of IEEE International Geoscience and Remote Sensing Symposium, Munich, pp. 3451-3454, 2012.

[18] Zingman I., Saupe D., and Lambers K., “A Morphological Approach for Distinguishing Texture and Individual Features in Images,” Pattern Recognition Letters, vol. 47, pp. 129- 138, 2014. Mudassir Rafi did his B.Sc with Honours in Geology from Aligarh Muslim University, Aligarh, India and M.C.A. from Jamia Millia Islamia, New Delhi, India in 2006 and 2010 respectively. He is currently pursuing Ph.D. in image processing from Indian Institute of Technology (ISM), Dhanbad, India. His research interest includes Salient Object detection, Texture analysis, Texture and Image segmentation. Susanta Mukhopadhyay did his B.Sc. with Honours in Physics from Presidency College, Calcutta, B.Tech and M.Tech in Radiophysics and Electronics from the University of Calcutta and Ph.D. in Image Processing from the Indian Statistical Institute, Calcutta in 1988, 1992 and 2003 respectively. During 2001–2003 and 2004–2007 he worked at the Sanford Burnham Prebys Medical Discovery Institute, La Jolla, California, USA as postdoctoral researcher and Nanyang Technological University, Singapore, as research fellow respectively. His research area and interest include image processing, fMRI, image compression, image encryption and image watermarking. He is currently working as Associate Professor in the Department of Computer Science and Engineering, IIT, Dhanbad, India.