Accurate Real Time Detection For Halal Logo Based On Fourier Magnitude Method

The research works carried out is the analysis of object detection methods and development of a new method in recognizing the approved Halal logo by JAKIM and their implementation in Android device. Currently, many irresponsible entrepreneurs use imitation ‘Halal’ logo on their products. Consequent...

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Main Author:	Ismail, Nurul Atiqah
Format:	Thesis
Language:	English English
Published:	2016
Subjects:	Q Science (General) QA76 Computer software
Online Access:	http://eprints.utem.edu.my/id/eprint/18179/1/Accurate%20Real%20Time%20Detection%20For%20Halal%20Logo%20Based%20On%20Fourier%20Magnitude%20Method%2024%20Pages.pdf http://eprints.utem.edu.my/id/eprint/18179/2/Accurate%20Real%20Time%20Detection%20For%20Halal%20Logo%20Based%20On%20Fourier%20Magnitude%20Method.pdf
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id	my-utem-ep.18179
record_format	uketd_dc
institution	Universiti Teknikal Malaysia Melaka
collection	UTeM Repository
language	English English
topic	Q Science (General) QA76 Computer software
spellingShingle	Q Science (General) QA76 Computer software Ismail, Nurul Atiqah Accurate Real Time Detection For Halal Logo Based On Fourier Magnitude Method
description	The research works carried out is the analysis of object detection methods and development of a new method in recognizing the approved Halal logo by JAKIM and their implementation in Android device. Currently, many irresponsible entrepreneurs use imitation ‘Halal’ logo on their products. Consequently, Muslim users find it hard to determine the validity of ‘Halal’ logo used. This research aims to classify between JAKIM Halal logo and fake Halal logo. Beside that, the objective of this research is to develop an algorithm Fractionalized Principle Magnitude to recognize all 50 approved Halal logo. This research is divided into a three stages. In the first stage, the evaluations of the existing object detection methods in Android Smartphone is conducted. The evaluated object detection methods are Scale Invariant Feature Transform (SIFT), Speed up Robust Feature (SURF), Feature from Accelerate Segment Test (FAST), Good Feature to Track (GFTT), Maximally Stable Extremal Regions (MSER), Oriented FAST and Rotated BRIEF (ORB) and Center Surrounded Extrama (CenSurE). The characteristic of each object detection method is studied and compared in order to identify the best object detection method that can be applied in the recognition of JAKIM Halal logo. The second stage is the recognition of JAKIM Halal logo using Android Smartphone. In this stage, object detection methods with good result from previous stage is evaluated and compared with a newly developed simple yet effective logo recognition method based template matching technique in recognizing JAKIM Halal logo from fake Halal logos on Android phones. The last stage is the final work to complete Malaysia Halal logo recognition system because JAKIM also approved other 50 Halal logo from around the world other than JAKIM Halal logo. So in the last stage, a novel logo recognition method based on Fourier magnitudes and k-nearest neighbor classifier is developed to recognize all 50 Halal logos that approved by JAKIM. This novel logo recognition method is called Fractionalized Principle Magnitude (FPM) and have been compared with other logo recognition method such as Histogram of Gradient (HOG), Hu Moment, Zernike Moment and Wavelet Co-occurrence Histogram (WCH). The comparison is carried out based on efficiency, consistency and accuracy performances of each method. From the results, it shows that FPM obtains the highest average performance of 90.4% compared to those of 75.2% for HOG, 44.4% for Hu Moment, 64.4% for Zernike, and 47.2% for WCH.
format	Thesis
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
author	Ismail, Nurul Atiqah
author_facet	Ismail, Nurul Atiqah
author_sort	Ismail, Nurul Atiqah
title	Accurate Real Time Detection For Halal Logo Based On Fourier Magnitude Method
title_short	Accurate Real Time Detection For Halal Logo Based On Fourier Magnitude Method
title_full	Accurate Real Time Detection For Halal Logo Based On Fourier Magnitude Method
title_fullStr	Accurate Real Time Detection For Halal Logo Based On Fourier Magnitude Method
title_full_unstemmed	Accurate Real Time Detection For Halal Logo Based On Fourier Magnitude Method
title_sort	accurate real time detection for halal logo based on fourier magnitude method
granting_institution	Universiti Teknikal Malaysia Melaka
granting_department	Faculty Of Electronic And Computer Engineering
publishDate	2016
url	http://eprints.utem.edu.my/id/eprint/18179/1/Accurate%20Real%20Time%20Detection%20For%20Halal%20Logo%20Based%20On%20Fourier%20Magnitude%20Method%2024%20Pages.pdf http://eprints.utem.edu.my/id/eprint/18179/2/Accurate%20Real%20Time%20Detection%20For%20Halal%20Logo%20Based%20On%20Fourier%20Magnitude%20Method.pdf
_version_	1747833915412840448
spelling	my-utem-ep.181792021-10-08T07:40:33Z Accurate Real Time Detection For Halal Logo Based On Fourier Magnitude Method 2016 Ismail, Nurul Atiqah Q Science (General) QA76 Computer software The research works carried out is the analysis of object detection methods and development of a new method in recognizing the approved Halal logo by JAKIM and their implementation in Android device. Currently, many irresponsible entrepreneurs use imitation ‘Halal’ logo on their products. Consequently, Muslim users find it hard to determine the validity of ‘Halal’ logo used. This research aims to classify between JAKIM Halal logo and fake Halal logo. Beside that, the objective of this research is to develop an algorithm Fractionalized Principle Magnitude to recognize all 50 approved Halal logo. This research is divided into a three stages. In the first stage, the evaluations of the existing object detection methods in Android Smartphone is conducted. The evaluated object detection methods are Scale Invariant Feature Transform (SIFT), Speed up Robust Feature (SURF), Feature from Accelerate Segment Test (FAST), Good Feature to Track (GFTT), Maximally Stable Extremal Regions (MSER), Oriented FAST and Rotated BRIEF (ORB) and Center Surrounded Extrama (CenSurE). The characteristic of each object detection method is studied and compared in order to identify the best object detection method that can be applied in the recognition of JAKIM Halal logo. The second stage is the recognition of JAKIM Halal logo using Android Smartphone. In this stage, object detection methods with good result from previous stage is evaluated and compared with a newly developed simple yet effective logo recognition method based template matching technique in recognizing JAKIM Halal logo from fake Halal logos on Android phones. The last stage is the final work to complete Malaysia Halal logo recognition system because JAKIM also approved other 50 Halal logo from around the world other than JAKIM Halal logo. So in the last stage, a novel logo recognition method based on Fourier magnitudes and k-nearest neighbor classifier is developed to recognize all 50 Halal logos that approved by JAKIM. This novel logo recognition method is called Fractionalized Principle Magnitude (FPM) and have been compared with other logo recognition method such as Histogram of Gradient (HOG), Hu Moment, Zernike Moment and Wavelet Co-occurrence Histogram (WCH). The comparison is carried out based on efficiency, consistency and accuracy performances of each method. From the results, it shows that FPM obtains the highest average performance of 90.4% compared to those of 75.2% for HOG, 44.4% for Hu Moment, 64.4% for Zernike, and 47.2% for WCH. 2016 Thesis http://eprints.utem.edu.my/id/eprint/18179/ http://eprints.utem.edu.my/id/eprint/18179/1/Accurate%20Real%20Time%20Detection%20For%20Halal%20Logo%20Based%20On%20Fourier%20Magnitude%20Method%2024%20Pages.pdf text en public http://eprints.utem.edu.my/id/eprint/18179/2/Accurate%20Real%20Time%20Detection%20For%20Halal%20Logo%20Based%20On%20Fourier%20Magnitude%20Method.pdf text en validuser https://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=100053 mphil masters Universiti Teknikal Malaysia Melaka Faculty Of Electronic And Computer Engineering 1. Agrawal, M., Konolige, K. and Blas, M. R., 2008. Censure: Center Surround Extremas for Real-time Feature Detection and Matching. Computer Vision–ECCV, Springer. 2. Ahsan Ahmad Ursani, Kidiyo Kpalma and Joseph Ronsin, 2008. Texture features based on local Fourier histogram: self-compensation against rotation. Journal of Electronic Imaging 17(3). Arth, C., Leistner, C., & Bischof, H., 2007. Robust Local Features and their Application in Self-Calibration and Object Recognition on Embedded Systems. IEEE Conference on Computer Vision and Pattern Recognition, pp.1–8.Vedaldi, 2007. An Open Implementation of the SIFT Detector and Descriptor, Technical Report 070012, Computer Science Dept., Univ. of California Los Angeles. 3. AA Ursani, K Kpalma, J Ronsin,. 2007. Texture features based on Fourier transform and Gabor filters: an empirical comparison. Machine Vision ICMV. 4. Bagheri, M., Madani, M., Sahba, R. and Sahba, A., 2011. Real Time Object Detection Using a Novel Adaptive Color Thresholding Method. International ACM workshop on Ubiquitous Meta user interfaces, pp.13-16. 5. Bastos, R., & Dias, J. M. S., 2005. Fully automated texture tracking based on natural features extraction and template matching. Proceedings of the 2005 ACM SIGCHI International Conference on Advances in Computer Entertainment Technology - ACE ’05, pp.180–183. 6. Bay, H., Ess, A., Tuytelaars, T., and Van Gool, L., 2008. Speeded-Up Robust Features (SURF), Computer Vision and Image Understanding, 110(3), pp.346–359. 7. Calonder, M., Lepetit, V., Strecha, C., and Fua, P., 2010. BRIEF : Binary Robust Independent Elementary Features. Computer Vision-ECCV 2010, 6314, pp.778–792. 8. Chem-Loon Lim, Bamak Honarvar, Kim-han Thung, Raveendran paramesran, 2011. Fast computation exact Zernike moments using cascade digital filter. Information Science, 181(7), pp. 3638-3651. 9. Chen, W. C. S. & Zhang, D., 2007. Fast And Robust Fuzzy C-Means Clustering Algorithms Incorporating Local Information for Image Segmentation. Pattern Recognition, 40, pp.825-838. 10. Cheng-Min Lin; Jyh-Horng Lin; Chyi-Ren Dow; Chang-Ming Wen, 2011. Benchmark Dalvik and Native Code for Android System, Second International Conference on Innovations in Bio-inspired Computing and Applications (IBICA), pp.320-323. 11. Cheung, W., & Hamarneh, G., 2009. N-SIFT: N-Dimensional Scale Invariant Feature Transform. IEEE Transactions on Image Processing, 18(9), pp.2012-2021. 12. Chong, C.-W., Raveendran, P., & Mukundan, R., 2003. A comparative analysis of algorithms for fast computation of Zernike moments. Pattern Recognition, 36(3), pp.731– 742. 13. Dalal, N.; Triggs, B., 2005. Histograms of oriented gradients for human detection, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1, pp.886- 893. 14. Den Hollander, R.J.M., Hanjalic, A., 2003. Logo recognition in video stills by string matching, International Conference on Image Processing, pp.517–520. 15. Di Stefano, L.; Mattoccia, S.; Mola, M., 2003. An efficient algorithm for exhaustive template matching based on normalized cross correlation, 12th International Conference on Image Analysis and Processing, 2003.Proceedings. , pp.322-327. 16. Donoser, M., and Bischof, H. 2006. Efficient Maximally Stable Extremal Region (MSER) Tracking. IEEE Computer Society Conference on Computer Vision and Pattern Recognition 1, pp.553–560. 17. Ebrahimi, M., & Mayol-Cuevas, W. W., 2009. SUSurE: Speeded Up Surround Extrema feature detector and descriptor for realtime applications. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp.9–14. 18. Flusser, J., 2000. On the independence of rotation moment invariants. Pattern Recognition, 33(9), pp.1405–1410. 19. Feng-Cheng Huang; Shi-Yu Huang; Ji-Wei Ker; Yung-Chang Chen, 2012, High- Performance SIFT Hardware Accelerator for Real-Time Image Feature Extraction. IEEE Transactions on in Circuits and Systems for Video Technology. 22(3), pp.340-351. 20. Gall, J., Yao, A., Razavi, N., and Member, S., 2011. Hough Forests for Object Detection , Tracking , and Action Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(11), pp.2188–2202. 21. Gauglitz, S., Turk, M., & Höllerer, T., 2011. Improving Keypoint Orientation Assignment. Procedings of the British Machine Vision Conference, pp.93.1–93.11. 22. Glenn S. Iwerks, Hanan Samet and Kenneth P. Smith., 2006. maintenance of K-nn and spatial join queries on continuously moving points. ACM Transactions on Database System. 31(3). pp 485-536. 23. Gao; K., Lin; X., Zhang; Y., Tang; S., Zhang; D., 2009. Logo detection based on spatialspectral saliency and parital spatial context, Proc. of 2009 IEEE International Conference on Multimedia and Expo, pp.322-329. 24. Gonzalez, R.C. and Woods, R., 2002. Digital Image Processing second edition. Upper Saddle River: Printice-Hall. 25. Gurbuz, Ali Cafer, 2009. Shape detection in images exploiting sparsity, 24th International Symposium on Computer and Information Sciences, pp.70-75. 26. Guangyu Zhu; Doermann, D., 2009. Logo Matching for Document Image Retrieval, 10th International Conference on Document Analysis and Recognition, pp.606-610. 27. He, N., Cao, J., and Song, L., 2008. Scale Space Histogram of Oriented Gradients for Human Detection. International Symposium on Information Science and Engineering, pp.167–170. 28. H Yu, M Li, HJ Zhang, J Feng, 2001. Texture feature based on local Fourier transform. International Conference Image Processing. 29. Heinly, Jared, Enrique Dunn, and Jan-Michael Frahm., 2012. Comparative evaluation of binary features. Computer Vision–ECCV 2012. Springer Berlin Heidelberg, pp.759-773. 30. Hesson, A.; Androutsos, D., 2008. Logo and trademark detection in images using Color Wavelet Co-occurrence Histograms. IEEE International Conference on Acoustics, Speech and Signal Processing, pp.1233-1236. 31. Hwang, S.-K., and Kim, W.-Y., 2006. A novel approach to the fast computation of Zernike moments. Pattern Recognition, 39(11), pp.2065–2076. 32. JAKIM. 2011. Halal originates from an Arabic phrase that means allowed or permitted by Islamic Law, [online]. Available: http://www.halal.gov.my/v3/index.php/en/about-halal- certification/halal-definition [Accessed on 20 April 2012]. 33. Jeong, K. and Moon, H., 2011. Object Detection Using FAST Corner Detector Based on Smartphone Platforms. First ACIS/JNU International Conference on Computers, Networks, Systems and Industrial Engineering (CNSI), pp.111-115. 34. Ji Yuan, Mao Li, Huang Qingqing, Gao Yan, 2011. Research on Object Shape Detection from Image with High-Level Noise Based on Fuzzy Generalized Hough Transform, International Conference on Multimedia and Signal Processing. pp.209-212. 35. Juan, L., and Gwun, O., 2009. A Comparison of SIFT, PCA-SIFT and SURF. International Journal of Image Processing (IJIP), 3(4), pp.143-152. 36. J.Wang, L.Duan, Z.Li, J.Liu, H.Lu and J.S Jin,. 2006. A Robust Method for TV Logo Tracking in Video Streams. IEEE International Conference on Multimedia Expo, pp. 1041- 1044. 37. Kernels, B. (2013). Android Developers. Retrieved from http://source.android.com/source/building-kernels.html [Accessed on 20 April 2013]. 38. Khotanzad, a., and Hong, Y. H., 1990. Invariant image recognition by Zernike moments. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12(5), pp.489–497. 39. Khairul Muzzammil Saipullah, Shao-Hu Peng, Hyun-Soo Kim, Deok-Hwan Kim,. 2010. Texture Classification By Implementing Blur, Scale and Grey Shift Insensitive Texture Descriptor Based on Local Fourier Transform. Proc. IWAIT, pp. 74. 40. Khairul Muzzammil, Shao-hu Peng, Hyun-Soo Kim and Deok-Hwan Kim,. 2009. Texture Feature Extractor Based on 2D Local Fourier Transform, Proc. KIPS Spring Conf., pp. 34- 39. 41. Khairul Muzzammil Saipullah and Deok-Hwan Kim, 2012.A robust texture feature extraction using the localized angular phase. Multimed Tools and Application. 59(3), pp. 717-746. 42. Kleban, J., Xie, X., Ma, W., 2008. Spatial Pyramid Mining For Logo Detection In Natural Scenes, pp.1077–1080. 43. K. Mikolajczyk, T. Tuytelaars, C.Schmid, et. al., 2005. A comparison of affine region detectors. International Journal of Computer Vision, 65(1-2), pp.43-72. 44. K. C. Otiniano-Rodr´ıguez, G. C´amara-Ch´avez, D. Menotti, 2012. Hu and Zernike Moments for Sign Language Recognition. IPCV’12 - The 2012 International Conference on Image Processing, Computer Vision, and Pattern Recognition. pp. 1-5. 45. Kyrkou, C.; Theocharides, T., 2011. A Flexible Parallel Hardware Architecture for AdaBoost-Based Real-Time Object Detection, IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 19(6), pp.1034-1047. 46. Lee, Y.-H., Chandrian, P., and Li, B., (2011). Efficient Java Native Interface for Android Based Mobile Devices. IEEE International Conference on Trust, Security and Privacy in Computing and Communications, pp.1202–1209. 47. Lei, B., Thing, V. L. L., Chen, Y., and Lim, W.-Y., 2012. Logo Classification with Edge- Based DAISY Descriptor. IEEE International Symposium on Multimedia, pp.222–228. 48. Lowe, D. G., 1999. Object Recognition from Local Scale-Invariant Features. The proceedings of the seventh IEEE international conference on Computer vision, pp.1150- 1157. 49. Luo, J., Ma, Y., Takikawa, E., Lao, S., Kawade, M., and Lu, B. L., 2007. Person-Specific SIFT Features for Face Recognition. IEEE International Conference on In Acoustics, Speech and Signal Processing. 2, pp. 589-593. 50. Matas, J., Chum, O., Urban, M., and Pajdla, T., 2004. Robust wide-baseline stereo from maximally stable extremal regions. Image and Vision Computing, 22(10), pp.761–767. 51. Motilal Agrawal, Kurt Konolige, M. R. B., 2008. CenSurE: Center Surround Extremas for Realtime Feature Detection and Matching. Computer Vision – ECCV, 5305, pp.102–115. 52. Natalie Baddour, 2009. Operational and convolution properties of two-dimensional Fourier transforms in polar coordinates. J. Opt. Soc. Am. A. 26, pp.1767-1777. 53. Ojala, T., Pietikäinen, M. and Harwood, D., 1996. A Comparative Study of Texture Measures with Classification Based on Featured Distributions. Pattern Recognition, 29, pp.51-59. 54. Rahman, M.; Ren, J.; Kehtarnavaz, N., 2009. Real-time implementation of robust face detection on mobile platforms, IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2009., pp.1353-1356. 55. Stefan Romberg , Lluis Garcia Pueyo , Rainer Lienhart , Roelof van Zwol, 2011. Scalable logo recognition in real-world images, Proceedings of the 1st ACM International Conference on Multimedia Retrieval, pp.1-8. 56. Rosten, E., Porter, R., and T. Drummond, 2010. Faster and better: a machine learning approach to corner detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(1), pp.105–19. 57. Rosten, E., and T. Drummond, 2006. Machine learning for high-speed corner detection. In Proc. 9th European Conference on Computer Vision (ECCV’06), pp. 34-38. 58. Rublee, E.; Rabaud, V.; Konolige, K.; Bradski, G., 2011. ORB: An efficient alternative to SIFT or SURF, Computer Vision (ICCV), 2011 IEEE International Conference on, pp.2564-2571. 59. S. Sanyal and S. H. Sengamedu, 2007. Logoseeker: a system for detecting and matching logos in natural images, in Proc. of ACM Multimedia. 60. Sarvaiya, J. N., Patnaik, S., and Bombaywala, S., 2009. Image Registration by Template Matching Using Normalized Cross-Correlation. International Conference on Advances in Computing, Control, and Telecommunication Technologies, (3), pp.819–822. 61. Senthilkumaran, N. and Rajesh, R., 2009. Edge Detection Techniques for Image Segmentation-A Survey of Soft Computing Approaches. International Journal of Recent Trends in Engineering, 1, pp.250-254. 62. Shu, Xianhua, Zhenjun Du, and Rong Chen, 2009. Research on mobile location service design based on Android. 5th International Conference on. IEEE Wireless Communications, Networking and Mobile Computing. pp.125-128. 63. Shao-Hu Peng, Hyun-Soo Kim and Deok-Hwan Kim,. 2009. Speeded Up Feature Extraction for CT image Based on Integral Image Technique. IFMI, pp. 319-324. 64. Shu-Kuo and Zen Chen, 2011. Robust Logo Recognition For Mobile Phone Applications. Journal of Information Science and Engineering. 27, pp.545-559. 65. Shi, J.; Tomasi, C., 1994. Good features to track, Computer Vision and Pattern Recognition, 1994. Proceedings CVPR '94., 1994 IEEE Computer Society Conference on , pp.593-600. 66. Viola, P. and Jones, M. J., 2004. Robust Real-Time Face Detection. International Journal of Computer Vision, 57, pp.137–154. 67. Viola, P. and Jones, M., 2001. Rapid object detection using a boosted cascade of simple features, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1, pp.I-511-I-518. 68. Wang, H., & Chen, Y., 2009. Logo Detection in Document Images Based on Boundary Extension of Feature Rectangles. 10th International Conference on Document Analysis and Recognition, (c), pp.1335–1339. 69. Ye Bin, Peng Jia-Xiong, 2002. Invariant analysis of improved Zernike moments, J. opt. A: Pure Appl. Opt., 4(6), pp. 606-614. 70. Zhou, H., Yuan, Y., & Shi, C., 2009. Object Tracking Using SIFT Features and Mean Shift. Computer Vision and Image Understanding, 113(3), pp.345-352.

Accurate Real Time Detection For Halal Logo Based On Fourier Magnitude Method

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