IJRIT International Journal of Research in Information Technology, Volume 2, Issue 5, May 2014, Pg: 119-123

International Journal of Research in Information Technology (IJRIT) www.ijrit.com

ISSN 2001-5569

VisionVision-based hexagonal image processing Barun Kumar1, Er. Pooja Kaushik2 1

4th semester m. tech, Department of electronics and communication, MMEC MMU, Mullana Ambala, Haryana, India [email protected], [email protected]

2

Associate Professor, Department of electronics and communication, MMEC MMU, Mullana Ambala, Haryana, India [email protected]

Abstract Image processing is one of the most widely used techniques for medical sciences, transmitting data and security. Current medical images are in the form of square pixels. Sometimes, square pixels have less sharpness, low intensity, low contrasts. Radiologist feels difficult to recognize, which organ is affected. If radiologist has hexagonal pixels image and enhance the image then radiologist can solve this problem.

Keywords: Image processing, Hexagonal pixels, Mat lab.

1. Introduction In present world, image processing is very important. It is because of its huge application in medicine, industry and many other areas. Now, most of the work is done on the basis that the images have square pixels, and most of existing software and hardware are on this basis, however, recently the new hexagonal structure has been introduced for representing pixels of images. In terms of biomimic approach, the human retina has a hexagonal structure and therefore it is obvious that the human understanding of the hexagonal structure is better than the former square one. Hexagonal structure has many advantages over classic square structure







Having a higher degree of circular symmetry Uniform fittings of pixels Reducing the need for complex calculations in processing work Enhancing image quality consistent connectivity definition Having higher isotropy, because each hexagonal pixel, as compared to a square pixel, has the same distance from the center

2. Hexagonal Structure and Spiral Architecture Addressing In this section, introduce algorithm and explain the algorithm that converts representation of pixels in the traditional square structure and the virtual hexagonal structure. Barun Kumar,

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IJRIT International Journal of Research in Information Technology, Volume 2, Issue 5, May 2014, Pg: 119-123

2.1 Hexagonal Structure Because there is no hardware available for image acquisition and presentation with hexagonal pixel, in this subsection, describe an interesting approach to the construction of virtual hexagonal structure. In the first step, each square pixel is divided to 7 * 7 smaller pixels. Now these new pixels are called sub-pixels. Intensity level of each of these sub-pixels is similar to that of the original pixel from which they are formed. Each hexagonal pixel is composed of 56 sub pixels (Fig. 1). The intensity level of each hexagonal pixel is the average of 56 sub pixels. Sub-window image with dimension 8 * 9 is shown in Fig. 1. Only 56 sub pixels marked belong to hexagonal pixels. Note that the size of each of new hexagonal pixels is 12.5% larger than that of square one.

Fig.1 8* 9 sub -window image 2.1 Spiral Architecture Unlike the previous and classic structures of images, each pixel of which was numbered by rows and columns, A method of one dimensional addressing called the spiral architecture, which can protect addresses and data of hexagonal pixels. As shown in Fig. 2, the spiral architecture is inspired from anatomical consideration of the primate's vision system. For the whole image, following the spiral-like curve, as shown in Fig. 2, one can find out the location of any hexagonal pixel with a given spiral address starting from the central pixel of address 0. Fig. 2 obviously indicates that the location of each pixel is given by following:

anan−1...a1, ai = 0, 1... 6 , i =1, 2... n i p = a ×10

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IJRIT International Journal of Research in Information Technology, Volume 2, Issue 5, May 2014, Pg: 119-123

Fig.2 Hexagonal pixels labeled by spiral addresses Finally Image look like this

Fig.3 A cluster of 7 hexagonal pixels Barun Kumar,

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IJRIT International Journal of Research in Information Technology, Volume 2, Issue 5, May 2014, Pg: 119-123

3. Result If take any image and convert square pixels to Hexagonal pixels then image look like this:

4. Conclusions In this paper new hexagonal pixel structure was introduced. There are explained the conversion algorithms between the traditional square and virtual hexagonal structure representation. Also one of the applications of this new structure was addressed. There are compared the results of de-noising algorithm in square and hexagonal pixels. Using more complicated filtering algorithms, and also implementing other popular image processing algorithms such as edge detection, image segmentation, texture analysis etc. in the hexagonal pixel structure are our future works.

Acknowledgments I have to thank my thesis supervisors, [Er. Pooja Kaushik]. Without their assistance and dedicated involvement in every step throughout the process, this paper would have never been accomplished. The blessing, help and guidance given by her time to time shall carry me.

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IJRIT International Journal of Research in Information Technology, Volume 2, Issue 5, May 2014, Pg: 119-123

References F. Faille and M. Petrou, “Invariant image reconstruction from irregular samples and hexagonal grid splines,” Image and Vision Computing 28, pp. 1173–1183, 2010. [2] L. Middleton and J. Sivaswamy, “Edge detection in a Hexagonal-image processing framework,” Image and vision computing19, pp.1071-1081, 2001. [3] R. C. Gonzales and R. E. Woods, Digital Image Processing. Addison Wesley, 2002. [4] L. Middleton and J.Sivaswamy, “Hexagonal image processing,” A Practical Approach, London, 2005. [5] X. He, W. Jia, Q. Wu, N. Hur, T. Hintz and H. Wang, “Basic Transformations on Virtual Hexagonal Structure,” Proceedings of the International Conference on Computer Graphics, Imaging and Visualization, 2006. [6] T. Shigeki and M. Okutomi, “Comparison of image alignment on hexagonal and square lattices,” proceeding of 2010 IEEE 17th International conference on Image processing, Sep 26-29,Hong Kong, 2010. [7] X. Ho, T. Hintz, Q. Wu, H. Wang and W. Jia, “A new simulation of spiral architecture,” proceeding of International Conference on Image processing, computer vision and pattern Recognition, Las Vegas, June 2006. [8] R.M. Mersereau, “The processing of Hexagonally Sampled TwoDimensional Signals,” Proceedings of the IEEE. 67: pp. 930 949, 1979. [9] X. He and W. Jia, “hexagonal structure for intelligent vision,” proceeding of International Conference of Information and communication Technologies (IEEE), Karachi, pp.52-64, sep2005. [10] P. Sheridan, T. Hintz and D. Alexander, ”pseudo invariant Image Transformation on a hexagonal lattice,” Image and vision computing, Vol. 18, pp. 907-917, 2000. [11] S. Veni, “Vision based hexagonal image processing,” PHD Thesis, Department of Electronics And Communication Engineering, India, January 2012. [12] X.He, “2-D object Recognition with spiral Architecture,” PHD Thesis, university of technology, Sydney, 1999. [13] Mo. B. Nourian and M. R. Aahmadzadeh, “virtual hexagonal imagr structure”, Introducton of hexagonal image, 978-1-4673-6206-1/13 IEEE, 2013. [14] H. Wang, M. Wang, T. Hintz, et al., “VSA-based Fractal Image Compression, Journal of WSCG”, 2005. [1]

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