Gaussian Mixture Model for Edge Detection Techniques

International Journal of Computer Science (IJCS) Published by SK Research Group of Companies (SKRGC)

Abstract

Edges can be defined as the rigid significant change of image intensity pixels usually appears at the boundary between different regions, edges can modeled according to the image intensity profiles and amplitude changes, such as; Step, Ramp, Ridge /Line, and Roof Edges. Edge detection plays an efficient role in digital image processing and practical aspects of various life fields. Image edge detection frequently minimizes the amount of data and gets rid of worthless information and preserves the essential image characteristics. Edge detection techniques can be grouped into two main categories, Gradient and Laplacian edge detection techniques. Gaussian Mixture Model (GMM) lately applied for edge detection purposes. GMM considered as an unsupervised classifier that required a probability density functions (pdf) of the given data to be calculated at the training step. In the related works, we considered researches that deal with Gaussian model only since it is our concern in this work to focus on its main characteristics and properties effects. In this paper, we discussed and analyzed various concepts related to edges, various edge detection techniques, and ultimately introduced a comparison between these techniques.

References

[1] Monica Avlash, Lakhwinder Kaur, ?Performances Analysis of Different Edge Detection Methods On Road Images?, International Journal of Advanced Research in Engineering and Applied Sciences, Vol. 2, No. 6, June 2013.

[2] Richard Szeliski, ?Computer Vision: Algorithms and Applications, book, 2010 Springer, book site: https://szeliski.org/Book/.

[3] Gonzales, R. C. and Woods, R. E., Digital Image Processing. Prentice-Hall, Upper Saddle River, NJ, 3rd edition, 2008.

[4] Raman Maini and Himanshu Aggarwal, ?Study and Comparison of Various Image Edge Detection Techniques?, International Journal of Image Processing (IJIP), Volume (3) : Issue (1), 2009

[5] E. R. DAVIES, ?Computer and Machine Vision: Theory, Algorithms, Practicalities?, Fourth edition 2012, Elsevier

[6] M. Toledo, Chapter 5 Edge Detection, lecture notice – Computer Vision, 2004, website: https://ece.uprm.edu/~mtoledo/6088/2004/handout_ch5.pdf

[7] N. Senthilkumaran and R. Rajesh, ?Edge Detection Techniques for Image Segmentation – A Survey of Soft Computing, International Journal of Recent Trends in Engineering, Vol. 1, No. 2, May 2009.

[8] Noor A. Ibraheem, Mokhtar M. hasan, Shaima M., ?Automatic Block Selection for Synthesizing Texture Images using Genetic Algorithms?, Baghdad Science Journal, University of Baghdad, Iraq, vol. 6(4):822-830, Dec. 2009.

[9] Edge Detection by Trucco, Chapter 4 and Jain ct al.,Chapter 5., website: https://www.cse.unr.edu/~bebis/CS791E/Notes/EdgeDetection.pdf

[10] Ireyuwa E. Igbinosa, ?Comparison of Edge Detection Technique in Image Processing Techniques?, International Journal of Information Technology and Electrical Engineering, Vol. 2, No. 1, February 2013.

[11] G.T. Shrivakshan, ?A Comparison of various Edge Detection Techniques used in Image Processing?, IJCSI International Journal of Computer Science Issues, Vol. 9, Issue 5, No 1, September 2012.

[12] Rashmi, Mukesh Kumar, and Rohini Saxena, ?Algorithm and Technique nn Various Edge Detection: A Survey?, Signal & Image Processing : An International Journal (SIPIJ), Vol.4, No.3, June 2013, DOI : 10.5121/sipij.2013.4306 65

[13] Mokhtar M. Hasan, and Pramod K. Mishra, ?Novel Algorithm for Multi Hand Detection and Geometric Features Extraction and Recognition?, International Journal of Scientific and Engineering Research, Vol. 3 (5), pp. 1-11, 2012.

[14] DemirGokalp, ?Learning Skin Pixels in Color Images Using Gaussian Mixture?, available https://www.cs.bilkent.edu.tr/~guvenir/courses/cs550/Workshop/Demir_Gokalp.pdf

[15] P. KaewTraKulPong and R. Bowden, ?An Improved Adaptive Background Mixture Model for Realtime Tracking with Shadow Detection?, Proc. 2nd European Workshop on Advanced Video Based Surveillance Systems, AVBS01, VIDEO BASED SURVEILLANCE SYSTEMS: Computer Vision and Distributed Processing, Sept 2001.

[16] Allili, M. S., ?A short tutorial on Gaussian Mixture Models?, Université du Québec en Outaouais. CRV2010, 2010.

[17] Yinghong Li, Zhengxi Li, Hongfang Tian?Yuquan Wang, ?Vehicle Detecting and Shadow Removing Based on Edged

Mixture Gaussian Model?, 18th IFAC World CongressMilano (Italy) August 28 - September 2, 2011

[18] Xuegang Hu, Jiamin Zheng, ?An Improved Moving Object Detection Algorithm Based on Gaussian Mixture Models?, Open Journal of Applied Sciences, 2016, 6, Pp. 449-456. Doi: https://dx.doi.org/10.4236/ojapps.2016.67045

[19] Shuying Zhao, Wenjun Tan, Shiguang Wen, and Yuanyuan Liu, ?An Improved Algorithm of Hand Gesture Recognition under Intricate Background?, the First International Conference on Intelligent Robotics and Applications (ICIRA 2008),: Part I. Springer-Verlag Berlin Heidelberg, pp. 786–794, 2008. Doi:10.1007/978-3-540-88513-9_85

[20] Rahman Farnoosh, Behnam Zarpak, ?Image Segmentation Using Gaussian Mixture Model?, International Journal of Engineering Science (IUST), pp. 29-32, Vol. 19(1-2), 2008.

[21] Demir Gokalp, ?Learning Skin Pixels in Color Images Using Gaussian Mixture?, available at: https://www.cs.bilkent.edu.tr/~guvenir/courses/cs550/Workshop/Demir_Gokalp.pdf

[22] Mokhtar M Hasan, Pramod K Mishra, ?Comparative Study for Construction Of Gesture Recognition System?, International Journal of Computer Science and Software Technology, vol. 4(1):15-21, 2011.

[23] Pramod K. Mishra Mokhtar M. Hasan, ?Superior Skin Color Model using Multiple of Gaussian Mixture Model?, British Journal of Science, vol. 6(1):1-14, 2012.

[24] MM Hasan, PK Mishra, ?Performance Evaluation of Modified Segmentation on Multi Block For Gesture Recognition System?, International Journal of Signal Processing, Image Processing and Pattern Recognition, vol. 4(4):17-28, 2011.

[25] Mokhtar M Hasan, Pramod K Mishra, ?Novel algorithm for multi hand detection and geometric features extraction and recognition?, vol. 3(5):1-12, 2012.

[26] MM Hasan, ?New Rotation Invariance Features Based on Circle Partitioning?, J Comput Eng Inf Technol 2: 2. doi: https://dx. doi. org/10.4172/2324, vol. 9307 (2). 2013.

[27] Mokhtar M Hasan, Pramod K Mishra, ?Direction analysis algorithm using statistical approaches?, Fourth International Conference on Digital Image Processing (ICDIP 2012), doi: https://dx.doi.org/10.1117/12.946046 .

[28] Mokhtar M Hasan, Pramod K Misra, ?Robust Gesture Recognition using Euclidian Distance?, IEEE International Conference on Computer and Computational Intelligence, 978-1.

Keywords

Digital image processing, Edge detection, Gradient methods, Laplacian methods, Gaussian Mixture Model.