Study and comparison of various image edge detection techniques

Edges characterize boundaries and are therefore a problem of fundamental importance in image processing. Image Edge detection significantly reduces the amount of data and filters out useless information, while preserving the important structural properties in an image. Since edge detection is in the forefront of image processing for object detection, it is crucial to have a good understanding of edge detection algorithms. In this paper the comparative analysis of various Image Edge Detection techniques is presented. The software is developed using MATLAB 7.0. It has been shown that the Canny s edge detection algorithm performs better than all these operators under almost all scenarios. Evaluation of the images showed that under noisy conditions Canny, LoG( Laplacian of Gaussian), Robert, Prewitt, Sobel exhibit better performance, respectively. . It has been observed that Canny s edge detection algorithm is computationally more expensive compared to LoG( Laplacian of Gaussian), Sobel, Prewitt and Robert s operator CITED BY (354) 1 Gra a, R. F. P. S. O. (2012). Segmenta o de imagens tor cicas de Raio-X (Doctoral dissertation, UNIVERSIDADE DA BEIRA INTERIOR). 2 ZENDI, M., & YILMAZ, A. (2013). DEGISIK BAKIS A ILARINDAN ELDE EDILEN G R NT GRUPLARININ SINIFLANDIRILMASI. Journal of Aeronautics & Space Technolog ies/Havacilik ve Uzay Teknolojileri Derg is i, 6(1). 3 TROFINO, A. F. N. (2014). TRABALHO DE CONCLUS O DE CURSO. 4 Juan Albarrac n, J. (2011). Dise o, an lis is y optimizaci n de un s istema de reconocimiento de im genes basadas en contenido para imagen publicitaria (Doctoral dissertation). 5 Bergues, G., Ames, G., Canali, L., Schurrer, C., & Fles ia, A. G. (2014, June). Detecci n de l neas en im genes con ruido en un entorno de medici n de alta precis i n. In Biennial Congress of Argentina (ARGENCON), 2014 IEEE (pp. 582-587).  (2013). Analisa Statistik terhadap perubahan beberapa faktor deteksi kemacetan melalui pemrosesan video beserta peng iriman notifikas i kemacetan. Jurnal Sarjana ITB bidang Teknik Elektro dan Informatika, 2(1). 7 Pier g , M., & Jaskowiec, J. Identyfikacja twarzy z wykorzystaniem Sztucznych Sieci Neuronowych oraz PCA. 8 Nugraha, K. A., Santoso, A. J., & Suselo, T. (2015, July). ALGORITMA BACKPROPAGATION PADA JARINGAN SARAF TIRUAN UNTUK PENGENALAN POLA WAYANG KULIT. In Seminar Nasional Informatika 2008 (Vol. 1, No. 4). 9 Cornet, T. (2012). Formation et D veloppement des Lacs de Titan: Interpr tation G omorpholog ique d'Ontario Lacus et Analogues Terrestres (Doctoral dissertation, Ecole Centrale de Nantes (ECN)(ECN)(ECN)(ECN)). 10 Li, L., Sun, L., Ning , G., & Tan, S. (2014). Automatic Pavement Crack Recognition Based on BP Neural Network. PROMET-Traffic&Transportation, 26(1), 11-22. 11 Quang Hong , N., Khanh Quoc, D., Viet Anh, N., Chien Van, T., ???, & ???. (2015). Rate Allocation for Block-based Compressive Sensing . Journal of Broadcast Eng ineering , 20(3), 398-407. 12 Swillo, S. (2013). Zastosowanie techniki wizyjnej w automatyzacji pomiar w geometrii i podnoszeniu jakosci wyrob w wytwarzanych w przemysle motoryzacyjnym. Prace Naukowe Politechniki Warszawskiej. Mechanika, (257), 3-128. 13 V zina, M. (2014). D veloppement de log iciels de thermographie infrarouge visant am liorer le contr le de la qualit de la pose de l enrob bitumineux. 14 Decourselle, T. (2014). Etude et mod lisation du comportement des gouttelettes de produits phytosanitaires sur les feuilles de vigne par imagerie ultra-rapide et analyse de texture (Doctoral dissertation, Univers it de Bourgogne). 15 Reja, I. D., & Santoso, A. J. (2013). Pengenalan Motif Sarung (Utan Maumere) Menggunakan Deteksi Tepi. Semantik, 3(1). 16 Feng , Y., & Chen, F. (2013). Fast volume measurement algorithm based on image edge detection. Journal of Computer Applications, 6, 064. 17 Krawczuk, A., & Dominczuk, J. (2014). The use of computer image analys is in determining adhesion properties . Applied Computer Science, 10(3), 68-77. 18 Hui, L., Park, M. W., & Brilakis , I. (2014). Automated Brick Counting for Fa ade Construction Progress Estimation.  19 Mahmud, S., Mohammed, J., & Muaidi, H. (2014). A Survey of Dig ital Image Processing Techniques in Character Recognition. IJCSNS, 14(3), 65. 20 Yazdanparast, E., Dos Anjos , A., Garcia, D., Loeuillet, C., Shahbazkia, H. R., & Vergnes, B. (2014). INsPECT, an Open-Source and Versatile Software for Automated Quantification of (Leishmania) Intracellular Parasites . 21 Furtado, L. F. F., Trabasso, L. G., Villani, E., & Francisco, A. (2012, December). Temporal filter applied to image sequences acquired by an industrial robot to detect defects in large aluminum surfaces areas. In MECHATRONIKA, 2012 15th International Symposium (pp. 1-6).  22 Zhang , X. H., Li, G., Li, C. L., Zhang , H., Zhao, J., & Hou, Z. X. (2015).  Based on 2D Delaunay Triangulation. Mathematical Problems in Eng ineering , 501, 137193. 23 Hasan, H. M. Image Based Vehicle Traffic Measurement. 24 Taneja, N. PERFORMANCE  USED FOR QUALITATIVE ANALYSIS OF MEMBRANE FILTER. 25 Mathur, A., & Mathur, R. (2013). Content Based Image Retrieval by Multi Features us ing Image Blocks. International Journal of Advanced Computer Research, 3(4), 251. 26 Pandey, A., Pant, D., & Gupta, K. K. (2013). A Novel Approach on Color Image Refocusing and Defocusing . International Journal of Computer Applications, 73(3), 13-17. 27 S le, I. (2014). The determination of the twist level of the Chenille yarn using novel image processing methods: Extraction of axial grey-level characteristic and multi-step gradient based thresholding . Dig ital Signal Processing , 29, 78-99. 28 Azzabi, T., Amor, S. B., & Nejim, S. (2014, November). Obstacle detection for Unmanned Surface Vehicle. In Electrical Sciences and Technolog ies in Maghreb (CISTEM), 2014 International Conference on (pp. 1-7).  Elias , E. P., & Varghese, S. M. (2012). Personalised product design using virtual interactive techniques. . 30 Kim, J. H., & Lattimer, B. Y. (2015). Real-time probabilis tic class ification of fire and smoke using thermal imagery for intelligent firefighting robot. Fire Safety Journal, 72, 40-49. 31 N ez, J. M. Edge detection for Very High Resolution Satellite Imagery based on Cellular Neural Network. Advances in Pattern Recognition, 55. 32 Capobianco, J., Pallone, G., & Daudet, L. (2012, October). Low Complexity Transient Detection in Audio Coding Using an Image Edge Detection Approach. In Audio Eng ineering Society Convention 133. Audio Eng ineering Society. 33 zt rk, S., & Akdemir, B. (2015). Comparison of Edge Detection Algorithms for Texture Analys is on Glass Production. Procedia-Social and Behavioral Sciences, 195, 2675-2682. 34 Ahmed, A. M., & Elramly, S. Hyperspectral Data Compression Based On Weighted Prediction. 35 Jayas, D. S. A. Manickavasagan, HN Al-Shekaili, G. Thomas, MS Rahman, N. Guizani &. 36 Khashu, S., Vijayanagar, S., Manikantan, K., & Ramachandran, S. (2014, February). Face Recognition using Dual Wavelet Transform and Filter-Transformed Flipping . In Electronics and Communication Systems (ICECS), 2014 International Conference on (pp. 1-7).  Brown, R. C. (2014).  Intelligent Roadway Image Segmentation using an Adaptive Reg ion of Interest (Doctoral dissertation, Virg inia Polytechnic Institute and State Univers ity). 38 Huang , L., Zuo, X., Fang , Y., & Yu, X. A Segmentation Algorithm for Remote Sensing Imag ing Based on Edge and Heterogeneity of Objects . 39 Park, J., Kim, Y., & Kim, S. (2015). Landing Site Searching and Selection Algorithm Development Using Vis ion System and Its Application to Quadrotor. Control Systems Technology, ), 488-503. 40 Sikchi, P., Beknalkar, N., & Rane, S. Real-Time Cartoonization Using Raspberry Pi. 41 Bachmakov, E., Molina, C., & Wynne, R. (2014, March). Image-based spectroscopy for environmental monitoring . In SPIE Smart Structures and Materials+ Nondestructive Evaluation and Health Monitoring (pp. 90620B-90620B). International Society for Optics and Photonics . 42 Kulyukin, V., & Zaman, T. (2014). Vis ion-Based Localization and Scanning of 1D UPC and EAN Barcodes with Relaxed Pitch, Roll, and Yaw Camera Alignment Constraints . International Journal of Image Processing (IJIP), 8(5), 355. 43 Sandhu, E. M. S., Mutneja, E. V., & Nishi, E. Image Edge Detection by Using Rule Based Fuzzy Class ifier. 44 Tarwani, K. M., & Bhoyar, K. K. Approaches to Gender Class ification using Facial Images. 45 Kuppili, S. K., & Prasad, P. M. K. (2015). Design of Area Optimized Sobel Edge Detection. In Computational Intelligence in Data Mining-Volume 2 (pp. 647-655). Springer India. 46 Singh, R. K., Shaw, D. K., & Alam, M. J. (2015). Experimental Studies of LSB Watermarking with Different Noise. Procedia Computer Science, 54, 612-620. 47 Xu, Y., Da-qiao, Z., Da-wei, D., Bo, W., & Chao-nan, T. (2014, July). A speed monitoring method in steel pipe of 3PE-coating process based on industrial Charge-coupled Device. In Control Conference (CCC), 2014 33rd Chinese (pp. 2908-2912).  Yasiran, S. S., Jumaat, A. K., Malek, A. A., Hashim, F. H., Nasrir, N., Hassan, S. N. A. S., ... & Mahmud, R. (1987). Microcalcifications Segmentation using Three Edge Detection Techniques on Mammogram Images. 49 Roslan, N., Reba, M. N. M., Askari, M., & Halim, M. K. A. (2014, February). Linear and non-linear enhancement for sun g lint reduction in advanced very high resolution radiometer (AVHRR) image. In IOP Conference Series : Earth and Environmental Science (Vol. 18, No. 1, p. 012041). IOP Publishing . 50 Gupta, P. K. D., Pattnaik, S., & Nayak, M. (2014). Inter-level Spatial Cloud Compression Algorithm. Defence Science Journal, 64(6), 536-541. 51 Foster, R. (2015). A comparison of machine learning techniques for hand shape recogn