Hubung kait penilaian kualiti imej digital dengan kualiti peruasan dalam kegunaan penglihatan komputer

Bidang peruasan imej merupakan salah satu komponen yang kritikal di dalamkebanyakan aplikasi penglihatan komputer dan sistem dapatan kembali maklumat.Peruasan biasanya digunakan untuk memisahkan imej ke dalam kawasan yangmempunyai kepentingan semantik, sekaligus menyediakan maklumat tertinggi padapr...

Full description

Saved in:

Bibliographic Details
Main Author:	Siti Tasnim Mahamud
Format:	thesis
Language:	zsm
Published:	2017
Subjects:	TA Engineering (General) Civil engineering (General)
Online Access:	https://ir.upsi.edu.my/detailsg.php?det=5204
Tags:	Add Tag No Tags, Be the first to tag this record!

id	oai:ir.upsi.edu.my:5204
record_format	uketd_dc
institution	Universiti Pendidikan Sultan Idris
collection	UPSI Digital Repository
language	zsm
topic	TA Engineering (General) Civil engineering (General)
spellingShingle	TA Engineering (General) Civil engineering (General) Siti Tasnim Mahamud Hubung kait penilaian kualiti imej digital dengan kualiti peruasan dalam kegunaan penglihatan komputer
description	Bidang peruasan imej merupakan salah satu komponen yang kritikal di dalamkebanyakan aplikasi penglihatan komputer dan sistem dapatan kembali maklumat.Peruasan biasanya digunakan untuk memisahkan imej ke dalam kawasan yangmempunyai kepentingan semantik, sekaligus menyediakan maklumat tertinggi padaproses seterusnya mengenai struktur imej tersebut. Walau bagaimanapun, kaedahperuasan sedia ada mempunyai masalah dalam menghasilkan peruasan yangsempurna bagi sesetengah keadaan imej. Keadaan kualiti imej yang digunakan semasaproses peruasan merupakan faktor yang dikenalpasti berdasarkan kajian ini. Oleh itu,matlamat kajian adalah untuk melihat hubungan yang terdapat pada kualiti imejdengan keberkesanan kaedah peruasan dalam memisahkan objek yang dipilihdaripada latar belakang imej dengan sempurna. Terdapat dua fasa utama di dalampembangunan kajian ini iaitu penilaian kualiti imej digital dan penggunaan algoritmaperuasan interaktif. Penilaian Kualiti Imej Digital (PKID) digunakan untuk mengukurkualiti imej dengan menggunakan empat metrik yang dipilih. Kemudian, hasilperuasan yang menggunakan empat peruasan interaktif diukur kualiti peruasannyadengan pengukuran ketepatan sempadan dan objek. Akhir sekali, hubungan sekaitandi antara skor kualiti imej dengan kualiti hasil peruasan dinilai bagi melihat sama adakualiti imej benar-benar memainkan peranan penting semasa proses peruasan imej.Analisis daripada keputusan kajian ini menunjukkan algoritma peruasanmenghasilkan prestasi yang baik pada imej yang mempunyai skor PKID yangsederhana. Namun demikian, kualiti hasil peruasan terus merosot apabila kualiti imejsemakin teruk terutamanya bagi imej yang mempunyai degradasi jenis hingar danmampatan Joint Photographic Experts Group (JPEG). Dapatan ini menunjukkanmasalah yang biasa dihadapi semasa proses peruasan dan hal ini mendorong kepadacadangan kajian pada masa akan datang yang melibatkan pembenaman sistem PKIDke dalam aplikasi-aplikasi yang menggunakan peruasan dalam kegunaan penglihatankomputer.
format	thesis
qualification_name
qualification_level	Master's degree
author	Siti Tasnim Mahamud
author_facet	Siti Tasnim Mahamud
author_sort	Siti Tasnim Mahamud
title	Hubung kait penilaian kualiti imej digital dengan kualiti peruasan dalam kegunaan penglihatan komputer
title_short	Hubung kait penilaian kualiti imej digital dengan kualiti peruasan dalam kegunaan penglihatan komputer
title_full	Hubung kait penilaian kualiti imej digital dengan kualiti peruasan dalam kegunaan penglihatan komputer
title_fullStr	Hubung kait penilaian kualiti imej digital dengan kualiti peruasan dalam kegunaan penglihatan komputer
title_full_unstemmed	Hubung kait penilaian kualiti imej digital dengan kualiti peruasan dalam kegunaan penglihatan komputer
title_sort	hubung kait penilaian kualiti imej digital dengan kualiti peruasan dalam kegunaan penglihatan komputer
granting_institution	Universiti Pendidikan Sultan Idris
granting_department	Fakulti Seni, Komputeran dan Industri Kreatif
publishDate	2017
url	https://ir.upsi.edu.my/detailsg.php?det=5204
_version_	1747833170200363008
spelling	oai:ir.upsi.edu.my:52042020-09-08 Hubung kait penilaian kualiti imej digital dengan kualiti peruasan dalam kegunaan penglihatan komputer 2017 Siti Tasnim Mahamud TA Engineering (General). Civil engineering (General) Bidang peruasan imej merupakan salah satu komponen yang kritikal di dalamkebanyakan aplikasi penglihatan komputer dan sistem dapatan kembali maklumat.Peruasan biasanya digunakan untuk memisahkan imej ke dalam kawasan yangmempunyai kepentingan semantik, sekaligus menyediakan maklumat tertinggi padaproses seterusnya mengenai struktur imej tersebut. Walau bagaimanapun, kaedahperuasan sedia ada mempunyai masalah dalam menghasilkan peruasan yangsempurna bagi sesetengah keadaan imej. Keadaan kualiti imej yang digunakan semasaproses peruasan merupakan faktor yang dikenalpasti berdasarkan kajian ini. Oleh itu,matlamat kajian adalah untuk melihat hubungan yang terdapat pada kualiti imejdengan keberkesanan kaedah peruasan dalam memisahkan objek yang dipilihdaripada latar belakang imej dengan sempurna. Terdapat dua fasa utama di dalampembangunan kajian ini iaitu penilaian kualiti imej digital dan penggunaan algoritmaperuasan interaktif. Penilaian Kualiti Imej Digital (PKID) digunakan untuk mengukurkualiti imej dengan menggunakan empat metrik yang dipilih. Kemudian, hasilperuasan yang menggunakan empat peruasan interaktif diukur kualiti peruasannyadengan pengukuran ketepatan sempadan dan objek. Akhir sekali, hubungan sekaitandi antara skor kualiti imej dengan kualiti hasil peruasan dinilai bagi melihat sama adakualiti imej benar-benar memainkan peranan penting semasa proses peruasan imej.Analisis daripada keputusan kajian ini menunjukkan algoritma peruasanmenghasilkan prestasi yang baik pada imej yang mempunyai skor PKID yangsederhana. Namun demikian, kualiti hasil peruasan terus merosot apabila kualiti imejsemakin teruk terutamanya bagi imej yang mempunyai degradasi jenis hingar danmampatan Joint Photographic Experts Group (JPEG). Dapatan ini menunjukkanmasalah yang biasa dihadapi semasa proses peruasan dan hal ini mendorong kepadacadangan kajian pada masa akan datang yang melibatkan pembenaman sistem PKIDke dalam aplikasi-aplikasi yang menggunakan peruasan dalam kegunaan penglihatankomputer. 2017 thesis https://ir.upsi.edu.my/detailsg.php?det=5204 https://ir.upsi.edu.my/detailsg.php?det=5204 text zsm closedAccess Masters Universiti Pendidikan Sultan Idris Fakulti Seni, Komputeran dan Industri Kreatif Abbadi, N. K. El, Abdul, A., & Qazzaz, A. (2015). Detection and segmentation ofhuman face. International Journal of Advanced Research in Computer andCommunication Engineering, 4(2), 9094.Achanta, R. (2011). Finding objects of interest in images using saliency andsuperpixels. EPFL, 4908.Adamek, T. (2006). Using contour information and segmentation for objectregistration, modeling and retrieval. Electronic Engineering. Dublin CityUniversity.Adams, R., & Bischof, L. (1994). Seeded region growing. Transactions on PatternAnalysis and Machine Intelligence, 16(6), 641647.Ballard, D. H., & Brown, C. M. (1982). Computer vision. Englewood Cliffs, NJ:Prenice-Hall.Bentley, J. L. (1975). Multidimensional binary search trees used for associativesearching. Communications of the ACM, 18(9), 509517.Bevilacqua, A., & Bevilacqua, R. (2002). Effective object segmentation in a trafficmonitoring application. Indian Conference on Computer Vision, Graphics andImage Processing, (pp. 125130).Bezdek, J. C., Hall, L. O., & Clarke, L. (1993). Review of MR image segmentationtechniques using pattern recognition. Medical Physics, 20(4), 10331048.Bhattacharyya, S., & Dutta, P. (2012). Handbook of Research on ComputationalIntelligence 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 IEEEInternational Conference on Computer Vision. ICCV 2001, 1(July), 105-112.?adk, M., Herzog, R., Mantiuk, R., Myszkowski, K., & Seidel, H.-P. (2012). Newmeasurements reveal weaknesses of image quality metrics in evaluatinggraphics artifacts. Proceedings of the 5th ACM SIGGRAPH Conference onComputer Graphics and Interactive Techniques in Asia, 110.Caselles, V., Kimmel, R., & Sapiro, G. (1997). Geodesic active contours.International Journal of Computer Vision, 22(1), 6179.Chandler, D. M. (2013). Seven challenges in image quality assessment: past, present,and future research. ISRN Signal Processing, 2013, 153.Chandler, D. M., & Larson, E. C. (2010). Most apparent distortion: full-referenceimage quality assessment and the role of strategy. Journal of ElectronicImaging, 19(1), 11006.Chen, M. J., & Bovik, A. C. (2011). Fast structural similarity index algorithm.Journal of Real-Time Image Processing, 6, 281287.Cheng, M. M., Mitra, N. J., Huang, X., Torr, P., & Hu, S. M. (2014). Salient objectdetection and segmentation. IEEE Transactions on Pattern Analysis andMachine Intelligence, 409416.Chetouani, A., Beghdadi, A., & Deriche, M. (2010). Image distortion analysis andclassification scheme using a neural approach. Visual Information Processing(EUVIP), 2010 2nd European Workshop, 183186.Cooray, S. (2003). Semi-automatic Video Object Segmentation for MultimediaApplications. Dublin City University.Cormen, T. H. (2009). Introduction to algorithms. MIT press.Definition of a Digital Image. (n.d.). Diambil pada Februari 12, 2016, daripadahttps://www.bowdoin.edu/dam/digimage/index.shtmlDigital Image. (n.d.). Diambil pada Februari 12, 2016, daripadahttp://support.esri.com/other-resources/gis-dictionary/term/digital imageDumic, E., Grgic, S., & Grgic, M. (2014). IQM2 - New image quality measure basedon steerable pyramid wavelet transform and structural similarity index. Signal,Image and Video Processing, 8(6), 11591168.Egiazarian, K., Astola, J., Ponomarenko, N., Lukin, V., Battisti, F., & Carli, M.(2006). A New Full-Reference Quality Metrics based on HVS, Proceedings ofthe Second International Workshop on Video Processing and Quality Metrics(Vol. 4, pp. 25).Estrada, F. J., & Jepson, A. D. (2009). Benchmarking image segmentation algorithms.International Journal of Computer Vision, 85(2), 167181.Falco, A. X., Udupa, J. K., & Miyazawa, F. K. (2000). An ultra-fast user-steeredimage segmentation paradigm: Live wire on the fly. IEEE Transactions onMedical Imaging, 19(1), 5562.Fan, J., Yau, D. K., Elmagarmid, A. K., & Aref, W. G. (2001). Automatic imagesegmentation by integrating color-edge extraction and seeded region growing.IEEE Transactions on Image Processing, 10(10), 14541466.Friedland, G., Jantz, K., & Rojas, R. (2005). SIOX: Simple interactive objectextraction. Multimedia, Seventh IEEE International Symposium On. IEEE, (pp.253260).Gao, X., Wang, T., & Li, J. (2005). A content-based image quality metric.International Workshop on Rough Sets, Fuzzy Sets, Data Mining, andGranular-Soft Computing, (pp. 231240). Springer Berlin Heidelberg.Ge, F., Wang, S., & Liu, T. (2007). New benchmark for image segmentationevaluation. 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 posterioriestimation for binary images. Journal of the Royal Statistical Society. Series B(Methodological), 51(2), 271279.Gupta, R., Bansal, D., & Singh, C. (2014). A Survey on Various Objective ImageQuality Assessment Techniques, 7, 99104.He, L., Gao, F., Hou, W., & Hao, L. (2013). Objective image quality assessment: Asurvey. International Journal of Computer Mathematics, (March 2014), 115.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 ofComputing, 2125.Ilushkina, N., & Avdeev, O. (n.d.). Image Quality Measures for Wavelet-BasedCompression Algorithm. Diambil pada Mac 2, 2015, daripadahttp://ticsp.cs.tut.fi/images/e/e0/Cr1036.pdf?Itti, L., Koch, C., & Niebur, E. (1998). A model of saliency-based visual attention forrapid scene analysis. IEEE Transactions on Pattern Analysis and MachineIntelligence, 20(11), 12541259.Jain, A. K., Duin, R. P. W., & Mao, J. (2000). Statistical pattern recognition: Areview. IEEE Transactions on Pattern Analysis and Machine Intelligence,22(1), 437.Kass, M., Witkin, A., & Terzopoulos, D. (1988). Snakes: active contour model.International Journal on Computer Vision, 1, 321331.Larson, E. C., & Chandler, D. M. (2008). Unveiling relationships between regions ofinterest and image fidelity metrics. Electronic Imaging 2008, (pp. 68222A68222A16). 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), 19.Li, Y., Sun, J., Tang, C.K., & Shum, H.Y. (2004). Lazy snapping. ACM Transactionson 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. SPIENewsroom, 46.Liu, A., Lin, W., Member, S., & Narwaria, M. (2012). Image Quality AssessmentBased on Gradient Similarity, 21(4), 15001512.Lukin, V. V., Ponomarenko, N. N., Krivenko, S. S., Egiazarian, K. O., & Astola, J. T.(2008). Image Filter Effectiveness Characterization Based on HVS, ElectronicImaging 2008, 68140Z-68140Z. International Society for Optics and PhotonicsMaintz, T. (2005). Digital and Medical Image Processing. Universiteit Utrecht.Diambil daripada http://www.cs.uu.nl/docs/vakken/ibv/reader/chapter10.pdfMannan, F. (n.d.). Interactive Image Segmentation. Diambil daripadahttp://www.cs.mcgill.ca/~fmanna/ecse626/InteractiveImageSegmentation_Report.pdfMartin, D., Fowlkes, C., Tal, D., & Malik, J. (2001). A database of human segmentednatural images and its application to evaluating segmentation algorithms andmeasuring ecological statistics. Proceedings Eighth IEEE InternationalConference on Computer Vision. ICCV 2001, 2(July), 416423.McGuinness, K., & OConnor, N. E. (2011). Toward automated evaluation ofinteractive segmentation. Computer Vision and Image Understanding, 115(6),868884.Mehnert, A., & Jackway, P. (1997). An improved seeded region growing algorithm.Pattern Recognition Letters, 18(10), 10651071.Mittal, A., Moorthy, A. K., & Bovik, A. C. (2012). No-reference image qualityassessment in the spatial domain. Image Processing, IEEE Transactions,21(12), 46954708.Mohammadi, P. (2014). Subjective and Objective Quality Assessment of image: Asurvey. arXiv Preprint, (June), 150Moorthy, A. K., & Bovik, A. C. (2011). Blind image quality assessment: From naturalscene statistics to perceptual quality. IEEE Transactions on Image Processing,20(12), 33503364.Morris, O. J., Lee, M. de J., & Constantinides, A. G. (1986). Graph theory for imageanalysis: an approach based on the shortest spanning tree. Communications,Radar and Signal Processing, IEEE Proceedings F, 133(2), 146152.Najemnik, J., & Geisler, W. S. (2005). Optimal eye movement strategies in visualsearch. Nature, 434(7031), 38791.Ning, J., Zhang, L., Zhang, D., & Wu, C. (2009). Interactive image segmentation bymaximal similarity based region merging. Pattern Recognition, 43(2), 445456.Olabarriaga, S. D., & Smeulders, A. W. (2001). Interaction in the segmentation ofmedical images: A survey. Medical Image Analysis, 5(2), 127142.Pappas, T. N., Safranek, R. J., & Chen, J. (2005). Perceptual criteria for image qualityevaluation. Handbook of Image and Video Processing, 908, 939959.Pearson, K. (1895). Note on regression and inheritance in the case of two parents.Proceedings of the Royal Society of London, 58, 240242.Ponomarenko, N., Battisti, F., Egiazarian, K., Astola, J., & Lukin, V. (2009). Metricsperformance comparison for color image database. Proc. Fourth InternationalWorkshop 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 shiftaccounting. CAD Systems in Microelectronics (CADSM), 2011 11thInternational 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, 5777.Ponomarenko, N., Krivenko, S., & Egiazarian, K. (2010). Weighted MSE basedmetrics for characterization of visual quality of image denoising methods.Proceedings of the 8th International Workshop on Video Processing andQuality 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 visualquality assessment metrics. Advances of Modern Radioelectronics, 10(4), 30-45.Portilla, J., Strela, V., Wainwright, M. J., & Simoncelli, E. P. (2003). ImageDenoising using Scale Mixtures of Gaussians in the Wavelet Domain. IEEETransactions on Image Processing, 12(11), 13381351.Protiere, A., & Sapiro, G. (2007). Interactive image segmentation via adaptiveweighted distances. IEEE Transactions on Image Processing, 16(4), 10461057.Rofsky, N. M. (2015). The importance of image quality: In the eyes of the beholder?Journal of Magnetic Resonance Imaging, 41(4), 861865.Rother, C., Kolmogorov, V., & Blake, A. (2004). GrabCut: Interactive foregroundextraction using iterated graph cuts. ACM Transactions on Graphics, 23(3),309.Rubner, Y., Tomasi, C., & Guibas, L. J. (2000). The earth movers distance as ametric for image retrieval. International Journal of Computer Vision, 40(2), 99121.Saad, M. A., Bovik, A. C., & Charrier, C. (2012). Blind image quality assessment: Anatural scene statistics approach in the DCT domain. IEEE Trans. ImageProcessing, 21(8), 33393352.Salembier, P., & Garrido, L. (2000). Binary partition tree as an efficientrepresentation for image processing, segmentation, and information retrieval.IEEE Transactions on Image Processing, 9(4), 561576.Shapiro, L., & Stockman, G. (2001). Computer Vision. Prentice Hall.Sharma, N., & M. Aggarwal, L. (2010). Automated medical image segmentationtechniques. Journal of Medical Physics, 35(1), 3.Sheikh, H. R., Sabir, M. F., & Bovik, A. C. (2006). A statistical evaluation of recentfull reference image quality assessment algorithms. IEEE Transactions onImage Processing : A Publication of the IEEE Signal Processing Society,15(11), 344051.Shi, R., Ngan, K. N., & Li, S. (2013). The objective evaluation of image objectsegmentation quality. Advanced Concepts for Intelligent Vision Systems, Acivs2013, (pp. 470479).Shi, R., Ngan, K. N., Li, S., Paramesran, R., & Li, H. (2015). Visual qualityevaluation of image object segmentation: subjective assessment and objectivemeasure. IEEE Transactions on Image Processing, 24(12), 50335045.Singh, P., & Chandler, D. M. (2013). F-MAD: A feature-based extension of the mostapparent distortion algorithm for image quality assessment. IS&T/SPIEElectronic Imaging, 86530I-86530I. International Society for Optics andPhotonics.Sless, D. (1981). Learning and visual communication. London: Croom Helm.Soundararajan, R., & Bovik, A. C. (2013). Survey of information theory in visualquality assessment. Signal, Image and Video Processing, 7(3), 391401.Sprawls, P. (1995). Image characteristics and quality. The Physical Principles ofMedical Imaging (Online Textbook). Diambil pada Januari 21, 2016 daripadahttp://www. sprawls. org/ppmi2/IMAGCHAR/(accessed on August 2, 2010).Sykes, A. O. (2007). An introduction to regression analysis. American Statistician,61(1), 101101.Tang, H., & Joshi, N. (2011). Learning a blind measure of perceptual image quality.Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference, 1,305312.Thung, K., & Raveendran, P. (2009). A survey of image quality measures. TechnicalPostgraduates (TECHPOS), 2009 International Conference for IEEE, 14.Tong, Y., Konik, H., Cheikh, F., & Tremeau, A. (2010). Full Reference Image QualityAssessment Based On Saliency Map Analysis. Journal of Imaging Science andTechnology, 54(3), 3050130503.Tseng, L. Y., & Yang, S. B. (2001). A genetic approach to the automatic clusteringproblem. Pattern Recognition, 34(2), 415424.Wang, Z., & Bovik, A. C. (2002). A universal image quality index. Signal ProcessingLetter, IEEE, 9(3), 8184.Wang, Z., & Bovik, A. C. (2006). Modern image quality assessment. SynthesisLectures on Image, Video, and Multimedia Processing, 2(1), 1-156.Wang, Z., & Li, Q. (2011). Information content weighting for perceptual imagequality assessment. IEEE Transactions on Image Processing : A Publication ofthe IEEE Signal Processing Society, 20(5), 118598.Wang, Z., Bovik, A. C., & Lu, L. (2002). Why is Image Quality Assessment SoDifficult?. Acoustics, Speech, and Signal Processing (ICASSP), 2002 IEEEInternational Conference, (Vol.4, pp. IV-3313).Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004). Image qualityassessment: From error visibility to structural similarity. IEEE Transactions onImage Processing : A Publication of the IEEE Signal Processing Society, 13(4),60012.Wang, Z., Simoncelli, E. P., & Bovik, A. C. (2003). Multi-scale Structural Similarityfor Image Quality Assessment. In IEEE Asilomar Conference on Signals,Systems and Computers (Vol. 2, pp. 913).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 optimalsegmentation in medical images. Handbook of Biomedical Image Analysis, 267314.Yang, Y. H., Buckley, M. J., Dudoit, S., & Speed, T. P. (2002). Comparison ofmethods for image analysis on cDNA microarray data. Journal ofComputational and Graphical Statistics, 11(1), 108136.Yau, C. (2013). R Tutorial with Bayesian Statistics Using OpenBUGS.Ye, P., & Doermann, D. (2012). No-reference image quality assessment using visualcodebooks. IEEE Transactions on Image Processing : A Publication of theIEEE Signal Processing Society, 21(7), 312938.Yuan, T., Zheng, X., Hu, X., Zhou, W., & Wang, W. (2014). A method for theevaluation of image quality according to the recognition effectiveness of objectsin the optical remote sensing image using machine learning algorithm. PloSOne, 9(1), e86528.Zadeh, L. (1978). Fuzzy Sets as a Basis for Possibility. Fuzzy Sets and Systems, (Vol.1, pp. 328).

Hubung kait penilaian kualiti imej digital dengan kualiti peruasan dalam kegunaan penglihatan komputer

Similar Items