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01-07-2020 | MATHEMATICAL THEORY OF IMAGES AND SIGNALS REPRESENTING, PROCESSING, ANALYSIS, RECOGNITION, AND UNDERSTANDING

A Study on the Effect of Canny Edge Detection on Downscaled Images

Authors: Yong Woon Kim, Innila Rose J, Addapalli V. N. Krishna

Published in: Pattern Recognition and Image Analysis | Issue 3/2020

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Abstract

Nowadays user devices such as phones, tablets etc. allows processing the images with help of high-end applications and softwares developed. Most of the times, the images are downscaled to make them compatible with these end devices. This leads to the loss of image quality. This loss of information on downscaling an image results in distortion of edges and while zoomed in results into a blurred image. As the edge detection is a basic step for many image processing applications such as object detection, object segmentation, object recognition, etc. It is necessary to know the impact of edge detection on downscaled image. In this paper, we are using Canny Edge detection method to detect the edges. The original images are downscaled using different interpolation methods. Canny Edge detection is applied on original images and downscaled images to compare the distortion in the edges. We used Structural Similarity Index Method for comparison. We are also comparing execution time taken by Canny Edge Detection on different interpolation methods to check for optimal interpolation method. We observed that the distortion in edges and time efficiency differ for different interpolation methods which are detailed below in the result section. As blurring is also a disadvantage of downscaling, we are applying Gaussian Blur on the images to compare the blurring due to Gaussian blur technique and blurring due to downscaling.

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12 Hard Stats That Proof The Power Of Images. https://www.searchenginepeople.com/blog/925-image-stats.html (Accessed Nov. 06, 2019).

H. Kim, M. Choi, B. Lim, and K. Mu Lee, “Task-aware image downscaling,” in Computer Vision − ECCV 2018, Part IV, Ed. by V. Ferrari, M. Hebert, C. Sminchisescu, and Y. Weiss, Lecture Notes in Computer Science (Springer, Cham, 2018), Vol. 11208, pp. 419–434. https://doi.org/10.1007/978-3-030-01225-0_25.

S. Safinaz, “An efficient algorithm for image scaling with high boost filtering,” Int. J. Sci. Res. Publ. 4 (5), pp. 181–189 (2014). Available: http://www.ijsrp.org/research-paper-0514/ijsrp-p2934.pdf

J. Kopf, A. Shamir, and P. Peers, “Content-adaptive image downscaling,” ACM Trans. Graphics 32 (6), Article 173, 1–8 (2013). https://doi.org/10.1145/2508363.2508370CrossRef

Y. Zhang, D. Zhao, J. Zhang, R. Xiong, and W. Gao, “Interpolation-dependent image downsampling,” IEEE Trans. Image Process. 20 (11), 3291–3296 (2011). https://doi.org/10.1109/TIP.2011.2158226MathSciNetCrossRefMATH

P. Ganesan and G. Sajiv, “A comprehensive study of edge detection for image processing applications,” in Proc. 2017 Int. Conf. on Innovations in Information, Embedded and Communication Systems (ICIIECS’17) (Coimbatore, India, 2017), pp. 1–6. https://doi.org/10.1109/ICIIECS.2017.8275968.

Y. Zheng, J. Rao, and L.Wu, “Edge detection methods in digital image processing,” in Proc. 5th Int. Conf. on Computer Science and Education (ICCSE 2010) (Hefei, China, 2010), pp. 471–473. https://doi.org/10.1109/ICCSE.2010.5593576.

P. S. Parsania and P. V. Virparia, “Computational time complexity of image interpolation algorithms,” Int. J. Comput. Sci. Eng. 6 (7), 491–496 (2018). https://doi.org/10.26438/ijcse/v6i7.491496CrossRef

D. Han, “Comparison of commonly used image interpolation methods,” in Proc. 2nd Int. Conf. on Computer Science and Electronics Engineering (ICCSEE 2013), Ser. Advances in Intelligent Systems Research (Atlantis Press, Paris, 2013), Vol. 34, pp. 1556–1559. https://doi.org/10.2991/iccsee.2013.391.

10.

S. K. Katiyar and P. V. Arun, “Comparative analysis of common edge detection techniques in context of object extraction,” arXiv preprint arXiv:1405.6132 (2014). Available: http://arxiv.org/abs/1405.6132.

11.

S. Vijayarani and M. Vinupriya, “Performance analysis of Canny and Sobel edge detection algorithms in image mining,” Int. J. Innov. Res. Comput. Commun. Eng. 1 (8), 1760–1767 (2013) [Online]. Available: http://www.rroij.com/open-access/performance-analysis-of-canny-and-sobel-edgedetection-algorithms-in-image-mining.php?aid=43752.

12.

J. Liu, S. He, and R. W. H. Lau, “L₀-regularized image downscaling,” IEEE Trans. Image Process. 27 (3), 1076–1085 (2018). https://doi.org/10.1109/TIP.2017.2772838MathSciNetCrossRefMATH

13.

H. Sharma, S. Saurav, S. Singh, A. K. Saini, and R. Saini, “Analyzing impact of image scaling algorithms on Viola-Jones face detection framework,” in Proc. 2015 Int. Conf. on Advances in Computing, Communications and Informatics (ICACCI 2015) (Kochi, India, 2015), pp. 1715–1718. https://doi.org/10.1109/ICACCI.2015.7275860.

14.

M. Póth, “Image interpolation techniques,” in 2nd Serbian-Hungarian Joint Symposium on Intelligent Systems (SISY2004) (Subotica, Serbia and Montenegro, 2004), pp. 1–6. http://uni-obuda.hu/conferences/sisy2004/Poth.pdf.

15.

P. A. Dilip, K. Rameshbabu, K. P. Ashok, and S. A. Shivdas, “Bilinear interpolation image scaling processor for VLSI architecure,” Int. J. Reconfigurable Embedded Syst. 3 (3), 104–113 (2014). http://ijres.iaescore.com/index.php/IJRES/article/view/1222.

16.

P. S. Parsania and P. V. Virparia, “A comparative analysis of image interpolation algorithms,” Int. J. Adv. Res. Comput. Commun. Eng. (IJARCCE) 5 (1), 29–34 (2016). https://doi.org/10.17148/ijarcce.2016.5107CrossRef

17.

S. Safinaz and A. V. Ravi Kumar, “VLSI realization of Lanczos interpolation for a generic video scaling algorithm,” in Proc. 2017 Int. Conf. on Recent Advances in Electronics and Communication Technology (ICRAECT 2017) (Bangalore, India, 2017), pp. 17–23. https://doi.org/10.1109/ICRAECT.2017.37.

18.

W. Dong, What is OpenCV’s INTER_AREA Actually Doing?, at Medium. https://medium.com/@wenrudong/what-is-opencvs-inter-area-actually-doing-282a626a09b3 (Accessed Nov. 05, 2019).

19.

R. Jain, R. Kasturi, and B. G. Schunck, Machine Vision (McGraw-Hill, New York, 1995).

20.

J. Canny, “A computational approach to edge detection,” IEEE Trans. Pattern Anal. Mach. Intell. PAMI-8 (6), 679–698 (1986). https://doi.org/10.1109/TPAMI.1986.4767851CrossRef

21.

Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: From error visibility to structural similarity,” IEEE Trans. Image Process. 13 (4), 600–612 (2004). https://doi.org/10.1109/TIP.2003.819861CrossRef

22.

M. Hassan and C. Bhagvati, “Structural similarity measure for color images,” Int. J. Comput. Appl. 43 (14), 7–12 (2012). https://doi.org/10.5120/6169-8590CrossRef

23.

F. Talbi, F. Alim, S. Seddiki, I. Mezzah, and B. Hachemi, “Separable convolution Gaussian smoothing filters on a Xilinx FPGA platform,” in Proc. 5th Int. Conf. on the Innovative Computing Technology (INTECH 2015) (Pontevedra, Spain, 2015), pp. 112–117. https://doi.org/10.1109/INTECH.2015.7173372.

24.

L. Kabbai, A. Sghaier, A. Douik, and M. Machhout, “FPGA implementation of filtered image using 2D Gaussian filter,” Int. J. Adv. Comput. Sci. Appl. 7 (7), 514–520 (2016). https://doi.org/10.14569/ijacsa.2016.070771CrossRef

Title: A Study on the Effect of Canny Edge Detection on Downscaled Images
Authors: Yong Woon Kim
Innila Rose J
Addapalli V. N. Krishna
Publication date: 01-07-2020
Publisher: Pleiades Publishing
Published in: Pattern Recognition and Image Analysis / Issue 3/2020
Print ISSN: 1054-6618
Electronic ISSN: 1555-6212
DOI: https://doi.org/10.1134/S1054661820030116

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