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INTELEK Repository
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Online Access
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https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072
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2015-11-18 12:37:28
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Restricted Document
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12514
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UniSZA
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1: Amornraksa, T. and S. Tachaphetpiboon, 2006. Fingerprint recognition using DCT feature. Elect. Lett., 42: 522-523. 2: Ahmed, B.K., 2011. DCT image compression by run-length and shift coding techniques. J. Univ. Anbar Pure Sci., Vol. 5. 3: Cui, P. and R.B. Zhang, 2012. A semi-supervised coefficient selection method for face recognition. J. Harbin Eng. Univ., 33: 855-861, (In Chinese). 4: Cui, P. and R.B. Zhang, 2012. Face feature extraction method based on part of labeled data. J. Optoelectron. Laser, 23: 554-560, (In Chinese). 5: Dabbaghchian, S., M.P. Ghaemmaghami and A. Aghagolzadeh, 2010. Feature extraction using discrete cosine transform and discrimination power analysis with a face recognition technology.. Pattern Recognit., 43: 1431-1440. 6: Duda, R.O. and P.E. Hart, 1972. Use of the Hough transformation to detect lines and curves in pictures. Commun. ACM, 15: 11-15. 7: Dutta, S., A. Abhinav, P. Dutta, P. Kumar and A. Halder, 2012. An efficient image compression algorithm based on histogram based block optimization and arithmetic coding. Int. J. Comput. Theory Eng., 4: 954-957. 8: Ekenel, H.K. and R. Stiefelhagen, 2005. Local appearance based face recognition using discrete cosine transform. Proceedings of the 13th European Signal Processing Conference, September 4-8, 2005, Antalya, Turkey – 9: Mohamad, F.S., A.A. Manaf and S. Chuprat, 2010. Histogram matching for color detection: A preliminary study. Proceedings of the International Symposium in Information Technology, Volume 3, June 15-17, 2010, Kuala Lumpur, pp: 1679-1684 10: Ferreira, P.J.S.G. and A.J. Pinho, 2002. Why does histogram packing improve lossless compression rates? IEEE Signal Proces. Lett., 9: 259-261. 11: Hafed, Z.M. and M.D. Levine, 2001. Face recognition using the discrete cosine transform. Int. J. Comput. Vision, 43: 167-188. 12: He, L., H. Chen and L. Carin, 2010. Tree-structured compressive sensing with variational bayesian analysis. IEEE Signal Proces. Lett., 17: 233-236. 13: Jain, A.K., 1989. Fundamentals of Digital Image Processing. Prentice Hall, Englewood Cliffs, NJ., ISBN-10: 0133361659, Pages: 569 14: Jing, X.Y. and D. Zhang, 2004. A face and palmprint recognition approach based on discriminant DCT feature extraction. IEEE Trans. Syst. Man Cybernetics-Part B: Cybernetics, 34: 2405-2415. 15: Kao, W.C., M.C. Hsu and Y.Y. Yang, 2010. Local contrast enhancement and adaptive feature extraction for illumination-invariant face recognition. Pattern Recogn., 43: 1736-1747. 16: Moerland, T., 2003. Steganography and Steganalysis. Universiteit Leiden, Rhone-Alpes, France 17: Rani, N. and S. Bishnoi, 2014. Comparative analysis of image compression using DCT and DWT transforms. Int. J. Comput. Sci. Mobile Comput., 3: 990-996. 18: Pratt, W.K., 2001. Digital Image Processing: PIKS Inside. 3rd Edn., John Wiley and Sons, Inc., USA., ISBN-13: 978-0-471-37407-7, Pages: 656 19: Prince, S.J.D., 2012. Computer Vision: Models, Learning and Inference. Cambridge University Press, Cambridge, UK., ISBN-13: 9781107011793, pp: 345-346 20: Wang, Z., 1984. Fast algorithms for the discrete wavelet transform and for the discrete fourier transform. IEEE Trans. Acoust., Speech Signal Proces., 32: 803-816. 21: Pennebaker, W.B. and J.L. Mitchell, 1993. JPEG: Still Image Data Compression Standard. Springer Science & Business Media, USA., ISBN: 9780442012724, pp: 29-32 22: Zahraddeen, S., S.M. Fatma, A.Y. Abdulganiyu and A.S. Ben-Musa, 2014. A new Hough transform on face detection using histograms. Image Vision Comput. J. (In Press). 23: Zahraddeen, S., S.M. Fatma and A.Y. Abdulganiyu, 2014. An efficient discrete cosine transform and gabor filter based feature extraction for face recognition. Proceedings of the 6th International Conference on Postgraduate Education, December 17-18, 2014, Melaka -
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12514 https://intelek.unisza.edu.my/intelek/pages/view.php?ref=12514 https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072 Restricted Document Article Journal image/jpeg inches 96 96 norman 775 1424 17 17 2015-11-18 12:37:28 1424x775 6821-01-FH02-FIK-15-04150.jpg UniSZA Private Access A New discrete cosine transform on face recognition through histograms for an optimized compression Research Journal of Information Technology Images with reduced signal level that can be easily stored in a database for recognition purposes are highly demanded. A system with large capacity requires a highly compressive technique for its efficiency. In this study, more efficient image compression algorithm was disclosed which reduced the size of original image drastically. After the necessary preprocessing, the histogram plot of an image was obtained. This histogram was rotated by 45° to produce a reduced image. Therefore, the two-dimensional Discrete Cosine Transform (2DCT) was computed on the rotated histogram to produce a new matrix. However, the proposed framework was tested on ten subjects from Olivetti Research Laboratory (ORL) database and its performance was evaluated using compression parameters such as PSNR and MSE. The new framework results in better reconstruction error and better PSNR values than original JPEG compression level. The new DCT matrix is proposed to proceed for postprocessing in DCT domain, where several coding approaches are employed. It was believed that, this discovery is new in the field of signal processing. 7 2 Academic Journals Inc Academic Journals Inc 101-111 1: Amornraksa, T. and S. Tachaphetpiboon, 2006. Fingerprint recognition using DCT feature. Elect. Lett., 42: 522-523. 2: Ahmed, B.K., 2011. DCT image compression by run-length and shift coding techniques. J. Univ. Anbar Pure Sci., Vol. 5. 3: Cui, P. and R.B. Zhang, 2012. A semi-supervised coefficient selection method for face recognition. J. Harbin Eng. Univ., 33: 855-861, (In Chinese). 4: Cui, P. and R.B. Zhang, 2012. Face feature extraction method based on part of labeled data. J. Optoelectron. Laser, 23: 554-560, (In Chinese). 5: Dabbaghchian, S., M.P. Ghaemmaghami and A. Aghagolzadeh, 2010. Feature extraction using discrete cosine transform and discrimination power analysis with a face recognition technology.. Pattern Recognit., 43: 1431-1440. 6: Duda, R.O. and P.E. Hart, 1972. Use of the Hough transformation to detect lines and curves in pictures. Commun. ACM, 15: 11-15. 7: Dutta, S., A. Abhinav, P. Dutta, P. Kumar and A. Halder, 2012. An efficient image compression algorithm based on histogram based block optimization and arithmetic coding. Int. J. Comput. Theory Eng., 4: 954-957. 8: Ekenel, H.K. and R. Stiefelhagen, 2005. Local appearance based face recognition using discrete cosine transform. Proceedings of the 13th European Signal Processing Conference, September 4-8, 2005, Antalya, Turkey – 9: Mohamad, F.S., A.A. Manaf and S. Chuprat, 2010. Histogram matching for color detection: A preliminary study. Proceedings of the International Symposium in Information Technology, Volume 3, June 15-17, 2010, Kuala Lumpur, pp: 1679-1684 10: Ferreira, P.J.S.G. and A.J. Pinho, 2002. Why does histogram packing improve lossless compression rates? IEEE Signal Proces. Lett., 9: 259-261. 11: Hafed, Z.M. and M.D. Levine, 2001. Face recognition using the discrete cosine transform. Int. J. Comput. Vision, 43: 167-188. 12: He, L., H. Chen and L. Carin, 2010. Tree-structured compressive sensing with variational bayesian analysis. IEEE Signal Proces. Lett., 17: 233-236. 13: Jain, A.K., 1989. Fundamentals of Digital Image Processing. Prentice Hall, Englewood Cliffs, NJ., ISBN-10: 0133361659, Pages: 569 14: Jing, X.Y. and D. Zhang, 2004. A face and palmprint recognition approach based on discriminant DCT feature extraction. IEEE Trans. Syst. Man Cybernetics-Part B: Cybernetics, 34: 2405-2415. 15: Kao, W.C., M.C. Hsu and Y.Y. Yang, 2010. Local contrast enhancement and adaptive feature extraction for illumination-invariant face recognition. Pattern Recogn., 43: 1736-1747. 16: Moerland, T., 2003. Steganography and Steganalysis. Universiteit Leiden, Rhone-Alpes, France 17: Rani, N. and S. Bishnoi, 2014. Comparative analysis of image compression using DCT and DWT transforms. Int. J. Comput. Sci. Mobile Comput., 3: 990-996. 18: Pratt, W.K., 2001. Digital Image Processing: PIKS Inside. 3rd Edn., John Wiley and Sons, Inc., USA., ISBN-13: 978-0-471-37407-7, Pages: 656 19: Prince, S.J.D., 2012. Computer Vision: Models, Learning and Inference. Cambridge University Press, Cambridge, UK., ISBN-13: 9781107011793, pp: 345-346 20: Wang, Z., 1984. Fast algorithms for the discrete wavelet transform and for the discrete fourier transform. IEEE Trans. Acoust., Speech Signal Proces., 32: 803-816. 21: Pennebaker, W.B. and J.L. Mitchell, 1993. JPEG: Still Image Data Compression Standard. Springer Science & Business Media, USA., ISBN: 9780442012724, pp: 29-32 22: Zahraddeen, S., S.M. Fatma, A.Y. Abdulganiyu and A.S. Ben-Musa, 2014. A new Hough transform on face detection using histograms. Image Vision Comput. J. (In Press). 23: Zahraddeen, S., S.M. Fatma and A.Y. Abdulganiyu, 2014. An efficient discrete cosine transform and gabor filter based feature extraction for face recognition. Proceedings of the 6th International Conference on Postgraduate Education, December 17-18, 2014, Melaka -
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| spellingShingle |
A New discrete cosine transform on face recognition through histograms for an optimized compression
|
| summary |
Images with reduced signal level that can be easily stored in a database for recognition purposes are highly demanded. A system with large capacity requires a highly compressive technique for its efficiency. In this study, more efficient image compression algorithm was disclosed which reduced the size of original image drastically. After the necessary preprocessing, the histogram plot of an image was obtained. This histogram was rotated by 45° to produce a reduced image. Therefore, the two-dimensional Discrete Cosine Transform (2DCT) was computed on the rotated histogram to produce a new matrix. However, the proposed framework was tested on ten subjects from Olivetti Research Laboratory (ORL) database and its performance was evaluated using compression parameters such as PSNR and MSE. The new framework results in better reconstruction error and better PSNR values than original JPEG compression level. The new DCT matrix is proposed to proceed for postprocessing in DCT domain, where several coding approaches are employed. It was believed that, this discovery is new in the field of signal processing.
|
| title |
A New discrete cosine transform on face recognition through histograms for an optimized compression
|
| title_full |
A New discrete cosine transform on face recognition through histograms for an optimized compression
|
| title_fullStr |
A New discrete cosine transform on face recognition through histograms for an optimized compression
|
| title_full_unstemmed |
A New discrete cosine transform on face recognition through histograms for an optimized compression
|
| title_short |
A New discrete cosine transform on face recognition through histograms for an optimized compression
|
| title_sort |
new discrete cosine transform on face recognition through histograms for an optimized compression
|