UPSI Digital Repository (UDRep)
|
|
|
Abstract : Universiti Pendidikan Sultan Idris |
Bidang peruasan imej merupakan salah satu komponen yang kritikal di dalam
kebanyakan aplikasi penglihatan komputer dan sistem dapatan kembali maklumat.
Peruasan biasanya digunakan untuk memisahkan imej ke dalam kawasan yang
mempunyai kepentingan semantik, sekaligus menyediakan maklumat tertinggi pada
proses seterusnya mengenai struktur imej tersebut. Walau bagaimanapun, kaedah
peruasan sedia ada mempunyai masalah dalam menghasilkan peruasan yang
sempurna bagi sesetengah keadaan imej. Keadaan kualiti imej yang digunakan semasa
proses peruasan merupakan faktor yang dikenalpasti berdasarkan kajian ini. Oleh itu,
matlamat kajian adalah untuk melihat hubungan yang terdapat pada kualiti imej
dengan keberkesanan kaedah peruasan dalam memisahkan objek yang dipilih
daripada latar belakang imej dengan sempurna. Terdapat dua fasa utama di dalam
pembangunan kajian ini iaitu penilaian kualiti imej digital dan penggunaan algoritma
peruasan interaktif. Penilaian Kualiti Imej Digital (PKID) digunakan untuk mengukur
kualiti imej dengan menggunakan empat metrik yang dipilih. Kemudian, hasil
peruasan yang menggunakan empat peruasan interaktif diukur kualiti peruasannya
dengan pengukuran ketepatan sempadan dan objek. Akhir sekali, hubungan sekaitan
di antara skor kualiti imej dengan kualiti hasil peruasan dinilai bagi melihat sama ada
kualiti imej benar-benar memainkan peranan penting semasa proses peruasan imej.
Analisis daripada keputusan kajian ini menunjukkan algoritma peruasan
menghasilkan prestasi yang baik pada imej yang mempunyai skor PKID yang
sederhana. Namun demikian, kualiti hasil peruasan terus merosot apabila kualiti imej
semakin teruk terutamanya bagi imej yang mempunyai degradasi jenis hingar dan
mampatan Joint Photographic Experts Group (JPEG). Dapatan ini menunjukkan
masalah yang biasa dihadapi semasa proses peruasan dan hal ini mendorong kepada
cadangan kajian pada masa akan datang yang melibatkan pembenaman sistem PKID
ke dalam aplikasi-aplikasi yang menggunakan peruasan dalam kegunaan penglihatan
komputer. |
References |
Abbadi, N. K. El, Abdul, A., & Qazzaz, A. (2015). Detection and segmentation of human face. International Journal of Advanced Research in Computer and Communication Engineering, 4(2), 90–94.
Achanta, R. (2011). Finding objects of interest in images using saliency and superpixels. EPFL, 4908.
Adamek, T. (2006). Using contour information and segmentation for object registration, modeling and retrieval. Electronic Engineering. Dublin City University.
Adams, R., & Bischof, L. (1994). Seeded region growing. Transactions on Pattern Analysis and Machine Intelligence, 16(6), 641–647.
Ballard, D. H., & Brown, C. M. (1982). Computer vision. Englewood Cliffs, NJ: Prenice-Hall.
Bentley, J. L. (1975). Multidimensional binary search trees used for associative searching. Communications of the ACM, 18(9), 509–517.
Bevilacqua, A., & Bevilacqua, R. (2002). Effective object segmentation in a traffic monitoring application. Indian Conference on Computer Vision, Graphics and Image Processing, (pp. 125–130).
Bezdek, J. C., Hall, L. O., & Clarke, L. (1993). Review of MR image segmentation techniques using pattern recognition. Medical Physics, 20(4), 1033–1048.
Bhattacharyya, S., & Dutta, P. (2012). Handbook of Research on Computational Intelligence for Engineering, Science, and Business (Vol. I). IGI Global.
Boykov, Y. Y., & Jolly, M.P. (2001). Interactive graph cuts for optimal boundary & region segmentation of objects in ND images. Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001, 1(July), 105-112.
?adík, M., Herzog, R., Mantiuk, R., Myszkowski, K., & Seidel, H.-P. (2012). New measurements reveal weaknesses of image quality metrics in evaluating graphics artifacts. Proceedings of the 5th ACM SIGGRAPH Conference on Computer Graphics and Interactive Techniques in Asia, 1–10.
Caselles, V., Kimmel, R., & Sapiro, G. (1997). Geodesic active contours. International Journal of Computer Vision, 22(1), 61–79.
Chandler, D. M. (2013). Seven challenges in image quality assessment: past, present, and future research. ISRN Signal Processing, 2013, 1–53.
Chandler, D. M., & Larson, E. C. (2010). Most apparent distortion: full-reference image quality assessment and the role of strategy. Journal of Electronic Imaging, 19(1), 11006.
Chen, M. J., & Bovik, A. C. (2011). Fast structural similarity index algorithm. Journal of Real-Time Image Processing, 6, 281–287.
Cheng, M. M., Mitra, N. J., Huang, X., Torr, P., & Hu, S. M. (2014). Salient object detection and segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 409–416.
Chetouani, A., Beghdadi, A., & Deriche, M. (2010). Image distortion analysis and classification scheme using a neural approach. Visual Information Processing (EUVIP), 2010 2nd European Workshop, 183–186.
Cooray, S. (2003). Semi-automatic Video Object Segmentation for Multimedia Applications. Dublin City University.
Cormen, T. H. (2009). Introduction to algorithms. MIT press. Definition of a Digital Image. (n.d.). Diambil pada Februari 12, 2016, daripada https://www.bowdoin.edu/dam/digimage/index.shtml
Digital Image. (n.d.). Diambil pada Februari 12, 2016, daripada http://support.esri.com/other-resources/gis-dictionary/term/digital image
Dumic, E., Grgic, S., & Grgic, M. (2014). IQM2 - New image quality measure based on steerable pyramid wavelet transform and structural similarity index. Signal, Image and Video Processing, 8(6), 1159–1168.
Egiazarian, K., Astola, J., Ponomarenko, N., Lukin, V., Battisti, F., & Carli, M. (2006). A New Full-Reference Quality Metrics based on HVS, Proceedings of the Second International Workshop on Video Processing and Quality Metrics (Vol. 4, pp. 2–5).
Estrada, F. J., & Jepson, A. D. (2009). Benchmarking image segmentation algorithms. International Journal of Computer Vision, 85(2), 167–181.
Falcão, A. X., Udupa, J. K., & Miyazawa, F. K. (2000). An ultra-fast user-steered image segmentation paradigm: Live wire on the fly. IEEE Transactions on Medical Imaging, 19(1), 55–62.
Fan, J., Yau, D. K., Elmagarmid, A. K., & Aref, W. G. (2001). Automatic image segmentation by integrating color-edge extraction and seeded region growing. IEEE Transactions on Image Processing, 10(10), 1454–1466.
Friedland, G., Jantz, K., & Rojas, R. (2005). SIOX: Simple interactive object extraction. Multimedia, Seventh IEEE International Symposium On. IEEE, (pp. 253–260).
Gao, X., Wang, T., & Li, J. (2005). A content-based image quality metric. International Workshop on Rough Sets, Fuzzy Sets, Data Mining, and Granular-Soft Computing, (pp. 231–240). Springer Berlin Heidelberg.
Ge, F., Wang, S., & Liu, T. (2007). New benchmark for image segmentation evaluation. Journal of Electronic Imaging, 16(3), 33011.
Glasbey, C. A., & Horgan, G. W. (1995). Image Analysis for the Biological Science (Vol. 1). Chichester: Wiley.
Gonzalez, R. C., & Woods, R. E. (2002). Digital Image Processing. Prentice Hall.
Greig, D. M., Porteous, B. T., & Seheult, A. H. (1989). Exact maximum a posteriori estimation for binary images. Journal of the Royal Statistical Society. Series B (Methodological), 51(2), 271–279.
Gupta, R., Bansal, D., & Singh, C. (2014). A Survey on Various Objective Image Quality Assessment Techniques, 7, 99–104.
He, L., Gao, F., Hou, W., & Hao, L. (2013). Objective image quality assessment: A survey. International Journal of Computer Mathematics, (March 2014), 1–15.
Hildebrandt, P. W., Hutchinson, I. G., Wilson, S. E., Williams, G. L., Mintz, J., Brown, S. G., … Dunn, M. H. (2008). Visual communication system. U.S. Patent No. 7,427,983. Washington, DC: U.S. Patent and Trademark Office.
Huang, T. (1996). Computer Vision: Evolution And Promise. 19th CERN School of Computing, 21–25.
Ilushkina, N., & Avdeev, O. (n.d.). Image Quality Measures for Wavelet-Based Compression Algorithm. Diambil pada Mac 2, 2015, daripada http://ticsp.cs.tut.fi/images/e/e0/Cr1036.pdf?
Itti, L., Koch, C., & Niebur, E. (1998). A model of saliency-based visual attention for rapid scene analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(11), 1254–1259.
Jain, A. K., Duin, R. P. W., & Mao, J. (2000). Statistical pattern recognition: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(1), 4–37.
Kass, M., Witkin, A., & Terzopoulos, D. (1988). Snakes: active contour model. International Journal on Computer Vision, 1, 321–331.
Larson, E. C., & Chandler, D. M. (2008). Unveiling relationships between regions of interest and image fidelity metrics. Electronic Imaging 2008, (pp. 68222A– 68222A–16). International Society for Optics and Photonics.
Lehman, A. (2005). JMP for Basic Univariate and Multivariate Statistics: A Step-by- Step Guide. SAS Institute Inc., Cary, NC, 481.
Li, C., & Bovik, A. (2009). Three-component weighted structural similarity index. IS&T/SPIE Electronic Imaging, 1(1), 1–9.
Li, Y., Sun, J., Tang, C.K., & Shum, H.Y. (2004). Lazy snapping. ACM Transactions on Graphics, 23(3), 303.
Li-Baboud, Y.-S., Cardone, A., Chalfoun, J., Bajcsy, P., & Elliott, J. (2013). Understanding the Impact of Image Quality on Segmentation Accuracy. SPIE Newsroom, 4–6.
Liu, A., Lin, W., Member, S., & Narwaria, M. (2012). Image Quality Assessment Based on Gradient Similarity, 21(4), 1500–1512.
Lukin, V. V., Ponomarenko, N. N., Krivenko, S. S., Egiazarian, K. O., & Astola, J. T. (2008). Image Filter Effectiveness Characterization Based on HVS, Electronic Imaging 2008, 68140Z-68140Z. International Society for Optics and Photonics
Maintz, T. (2005). Digital and Medical Image Processing. Universiteit Utrecht. Diambil daripada http://www.cs.uu.nl/docs/vakken/ibv/reader/chapter10.pdf
Mannan, F. (n.d.). Interactive Image Segmentation. Diambil daripada http://www.cs.mcgill.ca/~fmanna/ecse626/InteractiveImageSegmentation_Repo rt.pdf
Martin, D., Fowlkes, C., Tal, D., & Malik, J. (2001). A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001, 2(July), 416–423.
McGuinness, K., & O’Connor, N. E. (2011). Toward automated evaluation of interactive segmentation. Computer Vision and Image Understanding, 115(6), 868–884.
Mehnert, A., & Jackway, P. (1997). An improved seeded region growing algorithm. Pattern Recognition Letters, 18(10), 1065–1071.
Mittal, A., Moorthy, A. K., & Bovik, A. C. (2012). No-reference image quality assessment in the spatial domain. Image Processing, IEEE Transactions, 21(12), 4695–4708.
Mohammadi, P. (2014). Subjective and Objective Quality Assessment of image: A survey. arXiv Preprint, (June), 1–50
Moorthy, A. K., & Bovik, A. C. (2011). Blind image quality assessment: From natural scene statistics to perceptual quality. IEEE Transactions on Image Processing, 20(12), 3350–3364.
Morris, O. J., Lee, M. de J., & Constantinides, A. G. (1986). Graph theory for image analysis: an approach based on the shortest spanning tree. Communications, Radar and Signal Processing, IEEE Proceedings F, 133(2), 146–152.
Najemnik, J., & Geisler, W. S. (2005). Optimal eye movement strategies in visual search. Nature, 434(7031), 387–91.
Ning, J., Zhang, L., Zhang, D., & Wu, C. (2009). Interactive image segmentation by maximal similarity based region merging. Pattern Recognition, 43(2), 445–456. Olabarriaga, S. D., & Smeulders, A. W. (2001). Interaction in the segmentation of medical images: A survey. Medical Image Analysis, 5(2), 127–142.
Pappas, T. N., Safranek, R. J., & Chen, J. (2005). Perceptual criteria for image quality evaluation. Handbook of Image and Video Processing, 908, 939–959.
Pearson, K. (1895). Note on regression and inheritance in the case of two parents. Proceedings of the Royal Society of London, 58, 240–242.
Ponomarenko, N., Battisti, F., Egiazarian, K., Astola, J., & Lukin, V. (2009). Metrics performance comparison for color image database. Proc. Fourth International Workshop on Video Processing and Quality Metrics for Consumer Electronics (Vol. 27).
Ponomarenko, N., Ieremeiev, O., Lukin, V., Egiazarian, K., & Carli, M. (2011). Modified image visual quality metrics for contrast change and mean shift accounting. CAD Systems in Microelectronics (CADSM), 2011 11th International Conference The Experience of Designing and Application, 305- 311.
Ponomarenko, N., Jin, L., Ieremeiev, O., Lukin, V., Egiazarian, K., Astola, J., … Kuo, C. J. (2015). Image database TID2013 : Peculiarities , results and perspectives. Signal Processing : Image Communication, 30, 57–77.
Ponomarenko, N., Krivenko, S., & Egiazarian, K. (2010). Weighted MSE based metrics for characterization of visual quality of image denoising methods. Proceedings of the 8th International Workshop on Video Processing and Quality Metrics for Consumer Electronics (VPQM'14).
Ponomarenko, N., Lukin, V., Zelensky, A., Egiazarian, K., Astola, J., Carli, M., & Battisti, F. (2009). TID2008 – A database for evaluation of full-reference visual quality assessment metrics. Advances of Modern Radioelectronics, 10(4), 30-45.
Portilla, J., Strela, V., Wainwright, M. J., & Simoncelli, E. P. (2003). Image Denoising using Scale Mixtures of Gaussians in the Wavelet Domain. IEEE Transactions on Image Processing, 12(11), 1338–1351.
Protiere, A., & Sapiro, G. (2007). Interactive image segmentation via adaptive weighted distances. IEEE Transactions on Image Processing, 16(4), 1046– 1057.
Rofsky, N. M. (2015). The importance of image quality: In the eyes of the beholder? Journal of Magnetic Resonance Imaging, 41(4), 861–865.
Rother, C., Kolmogorov, V., & Blake, A. (2004). “GrabCut”: Interactive foreground extraction using iterated graph cuts. ACM Transactions on Graphics, 23(3), 309.
Rubner, Y., Tomasi, C., & Guibas, L. J. (2000). The earth mover’s distance as a metric for image retrieval. International Journal of Computer Vision, 40(2), 99– 121.
Saad, M. A., Bovik, A. C., & Charrier, C. (2012). Blind image quality assessment: A natural scene statistics approach in the DCT domain. IEEE Trans. Image Processing, 21(8), 3339–3352.
Salembier, P., & Garrido, L. (2000). Binary partition tree as an efficient representation for image processing, segmentation, and information retrieval. IEEE Transactions on Image Processing, 9(4), 561–576.
Shapiro, L., & Stockman, G. (2001). Computer Vision. Prentice Hall.
Sharma, N., & M. Aggarwal, L. (2010). Automated medical image segmentation techniques. Journal of Medical Physics, 35(1), 3.
Sheikh, H. R., Sabir, M. F., & Bovik, A. C. (2006). A statistical evaluation of recent full reference image quality assessment algorithms. IEEE Transactions on Image Processing : A Publication of the IEEE Signal Processing Society, 15(11), 3440–51.
Shi, R., Ngan, K. N., & Li, S. (2013). The objective evaluation of image object segmentation quality. Advanced Concepts for Intelligent Vision Systems, Acivs 2013, (pp. 470–479).
Shi, R., Ngan, K. N., Li, S., Paramesran, R., & Li, H. (2015). Visual quality evaluation of image object segmentation: subjective assessment and objective measure. IEEE Transactions on Image Processing, 24(12), 5033–5045.
Singh, P., & Chandler, D. M. (2013). F-MAD: A feature-based extension of the most apparent distortion algorithm for image quality assessment. IS&T/SPIE Electronic Imaging, 86530I-86530I. International Society for Optics and Photonics.
Sless, D. (1981). Learning and visual communication. London: Croom Helm.
Soundararajan, R., & Bovik, A. C. (2013). Survey of information theory in visual quality assessment. Signal, Image and Video Processing, 7(3), 391–401.
Sprawls, P. (1995). Image characteristics and quality. The Physical Principles of Medical Imaging (Online Textbook). Diambil pada Januari 21, 2016 daripada http://www. sprawls. org/ppmi2/IMAGCHAR/(accessed on August 2, 2010).
Sykes, A. O. (2007). An introduction to regression analysis. American Statistician, 61(1), 101–101.
Tang, H., & Joshi, N. (2011). Learning a blind measure of perceptual image quality. Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference, 1, 305–312.
Thung, K., & Raveendran, P. (2009). A survey of image quality measures. Technical Postgraduates (TECHPOS), 2009 International Conference for IEEE, 1–4.
Tong, Y., Konik, H., Cheikh, F., & Tremeau, A. (2010). Full Reference Image Quality Assessment Based On Saliency Map Analysis. Journal of Imaging Science and Technology, 54(3), 30501–30503.
Tseng, L. Y., & Yang, S. B. (2001). A genetic approach to the automatic clustering problem. Pattern Recognition, 34(2), 415–424.
Wang, Z., & Bovik, A. C. (2002). A universal image quality index. Signal Processing Letter, IEEE, 9(3), 81–84.
Wang, Z., & Bovik, A. C. (2006). Modern image quality assessment. Synthesis Lectures on Image, Video, and Multimedia Processing, 2(1), 1-156.
Wang, Z., & Li, Q. (2011). Information content weighting for perceptual image quality assessment. IEEE Transactions on Image Processing : A Publication of the IEEE Signal Processing Society, 20(5), 1185–98.
Wang, Z., Bovik, A. C., & Lu, L. (2002). Why is Image Quality Assessment So Difficult?. Acoustics, Speech, and Signal Processing (ICASSP), 2002 IEEE International Conference, (Vol.4, pp. IV-3313).
Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004). Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing : A Publication of the IEEE Signal Processing Society, 13(4), 600–12.
Wang, Z., Simoncelli, E. P., & Bovik, A. C. (2003). Multi-scale Structural Similarity for Image Quality Assessment. In IEEE Asilomar Conference on Signals, Systems and Computers (Vol. 2, pp. 9–13).
Wyszecki, G., & Styles, W. G. (2000). Colour Science: Concepts and methods, quantitative data and formulae (Edisi ke-2). Wiley.
Yang, S., & Mitra, S. (2005). Statistical and adaptive approaches for optimal segmentation in medical images. Handbook of Biomedical Image Analysis, 267– 314.
Yang, Y. H., Buckley, M. J., Dudoit, S., & Speed, T. P. (2002). Comparison of methods for image analysis on cDNA microarray data. Journal of Computational and Graphical Statistics, 11(1), 108–136.
Yau, C. (2013). R Tutorial with Bayesian Statistics Using OpenBUGS.
Ye, P., & Doermann, D. (2012). No-reference image quality assessment using visual codebooks. IEEE Transactions on Image Processing : A Publication of the IEEE Signal Processing Society, 21(7), 3129–38.
Yuan, T., Zheng, X., Hu, X., Zhou, W., & Wang, W. (2014). A method for the evaluation of image quality according to the recognition effectiveness of objects in the optical remote sensing image using machine learning algorithm. PloS One, 9(1), e86528.
Zadeh, L. (1978). Fuzzy Sets as a Basis for Possibility. Fuzzy Sets and Systems, (Vol. 1, pp. 3–28). |
This material may be protected under Copyright Act which governs the making of photocopies or reproductions of copyrighted materials. You may use the digitized material for private study, scholarship, or research. |