Journal Title : International Journal of Modern Trends in Engineering and Science

Author’s Name : Ponshankar. B, Swaranambigai. R

Volume 01 Issue o3 March 2014

ISSN no:  2348-3121 

Page No:73-77

AbstractBinary Image processing is extremely useful in object recognition, tracking, motion detection, machine intelligence, image analysis, understanding video processing and identification and authentication systems. Binary image processing (BIP) has been commonly implemented using DSP. However, it is inefficient and difficult to use such processors for binary image processing. High-speed implementation of binary image processing operations can be efficiently realized by using chips specialized for binary image processing.  Mathematical Morphology (MM) is a nonlinear image processing framework used to manipulate or analyze the shape of functions or objects. MM is set theory-based methods of image analysis and plays an important role in many digital image processing algorithms and applications, e.g., noise filtering, object extraction, analysis or pattern recognition. The methods, originally developed for binary images, were soon extended and now apply to many different image representations. Nowadays, image processing systems have constraints on speed or hardware resources. In addition, in embedded or mobile applications, this system consumes low power and low memory. The cryptography involves efficient algorithms related to encryption and decryption.

Keywords— Binary image processing (BIP), Mathematical morphology (MM), encryption and decryption.


[1] Bin Zhang, Kuizhi Mei and Nanning Zheng, “Reconfigurable Processor for Binary Image Processing”, in IEEE transactions on circuits and systems for video technology, Vol. 23, No. 5, may (2013).
[2] P. Dokladal, H. Hedberg, and V. Owall, “Binary morphology with spatially variant structuring elements algorithm and architecture,” IEEE Trans. Image Process., Vol. 18, No. 3, pp. 562–572, Mar. (2009).
[3] Hugo Hedberg, Fredrik Kristensen, Viktor Öwall, “Low-Complexity Binary Morphology Architectures With Flat Rectangular Structuring Elements,” in IEEE transactions on circuits and systems Vol. 55, No. 8, september (2008).
[4] Huijuan Yang and Alex C. Kot, “Image authentication with tampering localization by embedding cryptographic signature and block identifier,” in IEEE signal processing letters, Vol. 13, No. 12, december (2006).
[5] Min Wu, and Bede Liu, “Data Hiding in Binary Image for Authentication and Annotation”, in IEEE Transactions On Multimedia, Vol. 6, No. 4, August (2004).
[6] E. C. Pedrino, O. Morandin, Jr., and V. O. Roda, “Intelligent FPGA based system for shape recognition,” in Proc. 7th Southern Conf. Programmable Logic, (2011), pp. 197–202.
[7] E. C. Pedrino, J. H. Saito, and V. O. Roda, “Architecture for binary mathematical morphology reconfigurable by genetic programming,” in proc 6th Southern Programmable Logic Conf., 2010, pp. 93–98.
[8] E. N. Malamas, A. G. Malamos, and T. A. Varvarigou, “Fast implementation of binary morphological operations on hardware-efficient systolic architectures,” J. VLSI Signal Process., Vol. 25, No. 1, pp. 79-93 (2000).
[9] S. Chien and L. Chen, “Reconfigurable morphological image processing accelerator for video object segmentation,” J. Signal Process, Syst, Vol. 62, No. 1, pp. 77–96, (2011).
[10] N. Bouaynaya, M. Charif-Chefchaouni, and D. Schonfeld, “Spatiallyvariant morphological restoration and skeleton representation,” IEEE Trans. Image Process., Vol. 15, pp. 3579–3591, (2006).
[11] Ganesh Chandra, D.L. Gupta, Dr. A.K. Malviya, Satyendra Singh, Vinod Kumar Yadav, “Public Key Cryptosystem Technique Ellipse curve Cryptography with Generator g for Image Encryption”, Vinod Kumar Yadav et al ,Int.J.Computer Technology & Applications,Vol 3 (1), 298-302,(2012).

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