Analysis of a robust edge detection system in different color spaces
using color and depth images
Mousavi S.M.H.1, Lyashenko V.2, Prasath V.B.S.3
1 Department of Computer Engineering, Faculty of Engineering, Bu Ali Sina University, Hamadan, Iran,
2 Department of Informatics (INF), Kharkiv National University of Radio Electronics, Kharkiv, Ukraine,
3 Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati OH 45229 USA
Edge detection is very important technique to reveal significant areas in the digital image, which could aids the feature extraction techniques. In fact it is possible to remove un-necessary parts from image, using edge detection. A lot of edge detection techniques has been made already, but we propose a robust evolutionary based system to extract the vital parts of the image. System is based on a lot of pre and post-processing techniques such as filters and morphological operations, and applying modified Ant Colony Optimization edge detection method to the image. The main goal is to test the system on different color spaces, and calculate the system’s performance. Another novel aspect of the research is using depth images along with color ones, which depth data is acquired by Kinect V.2 in validation part, to understand edge detection concept better in depth data. System is going to be tested with 10 benchmark test images for color and 5 images for depth format, and validate using 7 Image Quality Assessment factors such as Peak Signal-to-Noise Ratio, Mean Squared Error, Structural Similarity and more (mostly related to edges) for prove, in different color spaces and compared with other famous edge detection methods in same condition. Also for evaluating the robustness of the system, some types of noises such as Gaussian, Salt and pepper, Poisson and Speckle are added to images, to shows proposed system power in any condition. The goal is reaching to best edges possible and to do this, more computation is needed, which increases run time computation just a bit more. But with today’s systems this time is decreased to minimum, which is worth it to make such a system. Acquired results are so promising and satisfactory in compare with other methods available in validation section of the paper.
Edge detection, ant colony optimization (ACO), color spaces, depth image, kinect V.2, image quality assessment (IQA), image noises
Mousavi SMH, Lyashenko V, Prasath VBS. Analysis of a robust edge detection system in different color spaces using color and depth images. Computer Optics 2019; 43(4): 632-646. DOI: 10.18287/2412-6179-2019-43-4-632-646.
- Davis, L.S. A survey of edge detection techniques / L.S. Davis // Computer Graphics and Image Processing. – 1975. – Vol. 4, Issue 3. – P. 248-270.
- Fogel, D.B. Evolutionary computation: the fossil record / D.B. Fogel. – Wiley-IEEE Press, 1998.
- Dasarathy, B.V. Edge preserving filters – Aid to reliable image segmentation / B.V. Dasarathy, H. Dasarathy // SOUTHEASTCON'81; Proceedings of the Region 3 Conference and Exhibit. – 1981. – P. 650-654.
- Ren, Ch.-X. Enhanced local gradient order features and discriminant analysis for face recognition / Ch.-X. Ren, [et al.] // IEEE Transactions on Cybernetics. – 2016. – Vol. 46, Is-sue 11. – P. 2656-2669.
- Liu, Y. Moving object detection and tracking based on background subtraction / Y. Liu, H. Ai, G.-Y. Xu // Pro-ceedings of SPIE. – 2001. – Vol. 4554. – P. 62-66.
- Leondes, C.T. Mean curvature flows, edge detection, and medical image segmentation / C.T. Leondes. – In: Computational methods in biophysics, biomaterials, biotechnology and medical systems / C.T. Leondes. – Boston, MA: Springer-Verlag US, 2003. – P. 856-870.
- Pflug, A. Ear biometrics: a survey of detection, feature extraction and recognition methods / A. Pflug, B. Christoph // IET Biometrics. – 2012. – Vol. 1, Issue 2. – P. 114-129.
- Rosenberger, M. Multispectral edge detection algorithms for industrial inspection tasks / M. Rosenberger // 2014 IEEE International Conference on Imaging Systems and Techniques (IST). – 2014. – P. 232-236.
- Tkalcic, M. Colour spaces: perceptual, historical and applicational background / M. Tkalcic, J.F. Tasic // The IEEE Region 8 EUROCON 2003. Computer as a Tool. – 2003. – Vol. 1. – P. 806-823.
- Chaves-González, J.M. Detecting skin in face recognition systems: A colour spaces study / J.M. Chaves-González, [et al.] // Digital Signal Processing. – 2010. – Vol. 20, Issue 3. – P. 806-823.
- Gonzalez, R.C. Digital image processing / R.C. Gonzalez, R.E. Woods. – 3rd ed. – Upper Saddle River, NJ: Prentice-Hall, Inc., 2016.
- Public-domain test images for homeworks and projects. – URL: https://homepages.cae.wisc.edu/~ece533/images/ (re-quest date 04.04.2019).
- C/Python/Shell programming and image/video processing/compression. – URL: http://www.hlevkin.com/06testimages.htm (request date 04.04.2019).
- Gonzales, R.C. Digital image processing / R.C. Gonzales, R.E. Woods. – Boston, MA: Addison and Wesley Publishing Company, 1992.
- Jain, A.K. Fundamentals of digital image processing / A.K. Jain. – Englewood Cliffs, NJ: Prentice Hall, 1989.
- Hasinoff, S.W. Photon, poisson noise / S.W. Hasinoff. – In: Computer vision / ed. by K. Ikeuchi. – Boston, MA: Springer US, 2014. – P. 608-610.
- Jaybhay, J. A study of speckle noise reduction filters / J. Jaybhay, R. Shastri // Signal & Image Processing: An International Journal (SIPIJ). – 2015. – Vol. 6.
- Zhang, Zh. Microsoft kinect sensor and its effect / Zh. Zhang // IEEE multimedia. – 2012. – Vol. 19, Issue 2. – P. 4-10.
- Xtion PRO. – URL: https://www.asus.com/3D-Sensor/Xtion_PRO/ (request date 04.04.2019).
- Keselman, L. Intel realsense stereoscopic depth-cameras / L. Keselman, [et al.]. – URL: https://arxiv.org/abs/1705.05548 (request date 04.04.2019).
- Primesense Carmine 1.09. – URL: http://xtionprolive.com/primesense-carmine-1.09 (request date 04.04.2019).
- Canny, J. A computational approach to edge detection / J. Canny. – In: Readings in computer vision: issues, problems, principles, and paradigms / ed. by M.A. Fischler, O. Firschein. – San Francisco, CA: Morgan Kaufmann Publishers, Inc., 1987. – P. 184-203.
- Haralick, R.M. Digital step edges from zero crossing of second directional derivatives / R.M. Haralick // IEEE Transactions on Pattern Analysis and Machine Intelligence. – 1984. – Vol. 1. – P. 58-68.
- Lindeberg, T. Scale selection properties of generalized scale-space interest point detectors / T. Lindeberg // Journal of Mathematical Imaging and Vision. – 2013. – Vol. 46, Issue 2. – P. 177-210.
- Roberts, L.G. Machine perception of three-dimensional solids : Diss. Ph.D. Thesis / Roberts Lawrence G. – Cambridge, MA, 1963.
- Prewitt, J.M.S. Object enhancement and extraction / J.M.S. Prewitt // Picture Processing and Psychopictorics. – 1970. – Vol. 10, Issue 1. – P. 15-19.
- Sobel, I. A 3x3 isotropic gradient operator for image processing, presented at a talk at the Stanford Artificial Project / I. Sobel, G. Feldman. – In: Pattern classification and scene analysis / ed. by R. Duda, P. Hart. – John Wiley & Sons, 1968. – P. 271-272.
- Shih, M.-Y. A wavelet-based multiresolution edge detection and tracking / M.-Y. Shih, D.-Ch. Tseng // Image and Vision Computing. – 2005. – Vol. 23, Issue 4. – P. 441-451.
- Lee, J. Morphologic edge detection / J. Lee, R. Haralick, L. Shapiro // IEEE Journal on Robotics and Automation. – 1987. – Vol. 3, Issue 2. – P. 142-156.
- Rajab, M.I. Application of region-based segmentation and neural network edge detection to skin lesions / M.I. Rajab, M.S. Woolfson, S.P. Morgan // Computerized Medical Imaging and Graphics. – 2004. – Vol. 28, Issue 1. – P. 61-68.
- Akbari, A.S. Fuzzy-based multiscale edge detection / A.S. Akbari, J.J. Soraghan // Electronics Letters. – 2003. – Vol. 39, Issue 1. – P. 30-32.
- Tian, J. An ant colony optimization algorithm for image edge detection / J. Tian, W. Yu, S. Xie // 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence). – 2008. – P. 751-756.
- Rajeswari, R. A modified ant colony optimization based approach for image edge detection / R. Rajeswari, R. Rajesh // 2011 International Conference on Image Information Processing. – 2011.
- Mousavi, S.M.H. An edge detection system for polluted images by gaussian, salt and pepper, poisson and speckle noises / S.M.H. Mousavi, M. Kharazi // 4th National Conference on Information Technology,Computer & TeleCommunication. – 2017.
- Chen, G.-H. Edge-based structural similarity for image quality assessment / G.-H. Chen, [et al.] // 2006 IEEE International Conference on Acoustics Speech and Signal Processing. – 2006. – Vol. 2. – P. II-II.
- Wang, Z. Image quality assessment: from error visibility to structural similarity / Z. Wang, [et al.] // IEEE transactions on image processing. – 2004. – Vol. 13, Issue 4. – P. 600-612.
- Lehmann, E.L. Theory of point estimation / E.L. Lehmann, G. Casella. – Springer Science & Business Media, 2006.
- Agaian, S.S. A new measure of image enhancement / S.S. Agaian, K.P. Lentz, A.M. Grigoryan // IASTED International Conference on Signal Processing & Communication. – 2000.
- Attar, A. Image quality assessment using edge based features / A. Attar, A. Shahbahrami, R.M. Rad // Multimedia Tools and Applications. – 2016. – Vol. 75, Issue 12. – P. 7407-7422.
- Zhang, M. Non-shift edge based ratio (NSER): An image quality assessment metric based on early vision features / M. Zhang, X. Mou, L. Zhang // IEEE Signal Processing Letters. – 2011. – Vol. 18, Issue 5. – P. 315-318.
- López-Randulfe, J. A quantitative method for selecting de-noising filters, based on a new edge-sensitive metric / J. López-Randulfe, [et al.] // 2017 IEEE International Conference on Industrial Technology (ICIT). – 2017. – P. 974-979
- Huang, T. A fast two-dimensional median filtering algorithm / T. Huang, G. Yang, G. Tang // IEEE Transactions on Acoustics, Speech, and Signal Processing. – 1979. – Vol. 27, Issue 1. – P. 13-18.
- Polesel, A. Image enhancement via adaptive unsharp masking / A. Polesel, G. Ramponi, V.J. Mathews // IEEE Transactions on Image Processing. – 2000. – Vol. 9, Issue 3. – P. 505-510.
- Wang, Z. Image quality assessment: from error visibility to structural similarity / Z. Wang, [et al.] // IEEE Transactions on Image Processing. – 2004. – Vol. 13, Issue 4. – P. 600-612.
- Karaboga, D. An idea based on honey bee swarm for numerical optimization / D. Karaboga. – Technical Report-TR06. – Erciyes University, Turkey, 2005.
- Yang, X.-S. A new metaheuristic bat-inspired algorithm / X.-S. Yang. – In: Nature inspired cooperative strategies for optimization (NICSO 2010) / ed. by J.R. González, D.A. Pelta, C. Cruz, G. Terrazas, N. Krasnogor. – Berlin, Heidelberg: Springer, 2010. – P. 65-74.
- Kennedy, J. Particle swarm optimization / J. Kennedy. – In: Encyclopedia of Machine Learning / ed. by C. Sammut, G.I. Webb. – Boston, MA: Springer US, 2010. – P. 760-766.
- Hossein Mousavi, S.M. Galaxy gravity optimization (GGO) an algorithm for optimization, inspired by comets life cycle / S.M. Hossein Mousavi, S.Y. Mirinezhad, M.H. Dezfoulian // 2017 Artificial Intelligence and Signal Processing Conference (AISP). – 2017. – P. 306-315.
- Atashpaz-Gargari, E. Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition / E. Atashpaz-Gargari, C. Lucas // 2007 IEEE Congress on Evolutionary Computation. – 2007. – P. 4661-4667.
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