Wednesday, 29 February 2012

Useful Image Processing References


REFERENCES
1.         Andrews H.C. and Hunt B.R, ‘Digital Image Restoration’, Prentice hall, Sydney, 1977.
2.         Bauer E. and Kohavi R. (1999), ‘An empirical comparison of voting classification algorithms: Bagging, Boosting, and variants’, Machine Learning, Vol. 36, No. 1–2, pp. 105–139.
3.        Batchelor B.G., Hill D.A. and Hodgson D.C. (1985), ‘Automated Visual Inspection’, IFS, Bedford, UK.
4.        Ballard D and Brown C. (1982), ‘Computer Vision’, Prentice Hall, NJ.
5.        Baxes A.G., ‘Digital Image Processing: A practical primer’, Prentice Hall, Englewood cliffs, NJ.
6.         Bellazzi R., Montani S.,Riva A. and Stefanelli M. (2001), ‘Web-based telemedicine systems for home-care: technical issues and experiences’, Computer Methods and Programs in Biomedicine, Vol. 64, Issue 3,  pp.175-187.
7.         Berman J.J. (2002), ‘Confidentiality issues for medical data miners’, Artificial Intelligence in Medicine, Vol. 26, Issues 1-2, pp.25-36.
8.         Berry M.J.A and Linoff G. (1997), ‘Data mining techniques for marketing, sales, and customer support’, Jonh Wiley, New York.
9.         Bezdek J.C., Hall L.O. and Clarke L.P. (1993), ‘Review of MR Image Segmentation techniques using pattern recognition’, Medical Physics, Vol.20, No.4, pp. 1033-1048.
10.       Bose T. (2004), ‘ Digital Signal and Image Processing’, Wiley, 2004.
11.       Bovik A.C. (2005), ‘Handbook of Image and Video Processing’, 2nd edition, Elsevier, 2005.
12.       Breiman L, Friedman J.H., Ohlsen R.A. and Stone C.J. (1984), ‘Classification and regression trees’, Monterey, Wadsworth.
13.       Castleman K.R. (1979), ‘Digital Image Processing’, Prentice Hall, Englewood Cliffs, NJ.
14.       Chen D.R., Chang R.F., Wu W.J., Moon W.K. and Wu W.L. (2003),
‘3-D breast ultrasound segmentation using active contour model’, Ultrasound in Medicine and Biology, Vol. 29, Issue 7, pp.1017-1026.
15.       Cohen L.D. (1991), ‘ On active contour models and baloons’, CVIGP-Image Understanding, Vol.53, pp. 211-18.
16.       Coleman G.B. And Andrews H.C. (1979), ‘Image Segmentation by Clustering’, In Proceedings of IEEE, Vol. 5, pp. 773-785.
17.       Crane R. (1997), ‘ A simplified approach to Image Processing: Classical and Modern techniques in C’, Prentice Hall PTR, NJ.
18.       Carbonell J.G. (1989), ‘Introduction: Paradigms for machine learning’, Artificial Intelligence, Vol. 40, Issues 1-3, September 1989, pp.1-9.
19.      Davies E.R. (1996), ‘Machine Vision: Theory, Algorithms, Practicalities’, 2nd edition, Academic Press, London.
20.       Deklerck R., Cornelius J. and Bister M. (1993), ‘Segmentation of medical images’, Image and Vision Computing, Vol. 11, pp. 486-503.
21.       Dougherty E.R. (1993), ‘Morphological Image Processing’, marcel Dekker, New York.
22.       Drozdek A., ‘Elements of Data Compression’, Thomas Brooks/Cole, New Delhi.
23.       Everitt, B.S. (1993), ‘Cluster analyses, Edward Arnold, London.
24.       Fukunaga K (1990), ‘Introduction to statistical pattern recognition’, 2nd ed. Boston, Academic Press.
25.      Fausett Laurene (), ‘Fundamentals of Neural Networks’, Prentice Hall, NJ.
26.       Fayyad U.M. and Uthurusamy R.(1996), ‘Data mining and knowledge discovery in databases (editorial)’, Commun. ACM, Vol. 39, No. 11,
pp. 24–26.
27.       Fayyad U., Piatetsky-Shapiro G. and Smyth P. (1996). Knowledge discovery and data mining: towards a unifying framework. In Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD-96), AAAI Press.
28.       Fayyad U., Piatetsky-Shapiro G. and Smyth P.(1996), ‘The KDD process for extracting useful knowledge from Volumes of data’, Communications of ACM, Vol. 39, No. 2, pp. 27–34.
29.      Gonzales R.C. and Woods R.E. (2002), ‘Digital image processing’, 2nd ed. Reading (MA), Addison-Wesley.
30.     Gonzales R.C. and Woods R.E., Eddins S.L. (2004), ‘Digital image processing using MATLAB’, Pearson Prentice Hall, NY.
31.       Fu K.S. and Mui J.K. (1981), ‘A survey on image segmentation’, Pattern Recognition, Vol. 13, Issue 1, pp.3-16.
32.       Fu K.S. (1976), ‘Digital pattern recognition’, Springer-verlag, heidelberg.
33.       Gupta U.G. (1993). ‘Validation and verification of knowledge-based systems: a survey’, Journal of Applied Intelligence, Vol. 3, pp. 343–363.
34.       Haralick R.M. and Shapiro L.G. (1985), ‘Image segmentation techniques’, Computer vision, Graphics and Image Processing, Vol. 29, pp. 100-132.
35.      Jain A.K. (1989), ‘Fundamentals of Digital Image Processing’, Pearson Education, New Delhi.
36.      Jahne B. (1991), ‘Digital Image Processing: Concepts, Algorithms, and Scientific Applications’, Springer-verlog, NY.
37.      Jensen A. and Cour-Harbo A.L. (), ‘Ripples in Mathematics – The Discrete Wavelet Transform’, Springer-Verlog, NY.
38.       Kass M., Witkin A., Terzopoulos D. (1988), ‘Snakes: Active Contour Models’, In the Proceedings of 1st International Conference on Computer Vision, London, pp. 321-331.
39.       Koivunen V. and Pietikanin M. (1990), ‘Combined edge and region-based method for range image segmentation’, Proceedings of SPIE,
Vol. 1381, pp. 501-512.
40.       LaMin F. (1994), ‘Neural networks in Computer Intelligence’, McGraw Hill, Singapore.
41.      Lim J.S. (1990), ‘2D signal and Image processing’, Prentice Hall, Englewood Cliffs, NJ.
42.       Maintz J.B.A. and Viergever M.A. (1998), ‘A survey of Medical image registration’, Medical Image analysis, Vol. 2, pp. 1-36.
43.      Maria P. and Panagiota B. (2003), ‘Image Processing – The Fundamentals’, John Wiley & Sons, NY.
44.      Maria P. and Sevilla  P.G. (2006), ‘Image Processinf dealing with Texture’, Wiley, USA.
45.       McInerney T. and Terzopoulos D. (1996), ‘Deformable models in medical image analysis: a survey’, Medical Image Analysis, Vol. 1,
Issue 2, pp. 91-108.
46.       Mitra S., Pal S.K. and Mitra P. (2002), ‘Data mining in soft computing framework: A survey’, IEEE Transactions Neural Networks, Vol. 13,
No. 1, pp. 3–14.
47.       Mitchell T. (1997), ‘Machine learning’,  McGraw-Hill,  New York.
48.       Newman T.S. and Jain A.K. (1995). ‘A Survey of Automated Visual Inspection’, Computer Vision, graphics and Image Processing,Vol. 61, No. 2, pp 231-262.
49.      Naguyen H.T. and Walker E.A. (1999), ‘A First Course in Fuzzy Logic’, CRC Press, Boca Raton, FL.
50.       Olabarriaga S.D. and Smeulders A.W.M. (2001), ‘Interaction in the segmentation of medical images: A survey’, Medical Image Analysis, Vol. 5, Issue 2, pp. 127-142.
51.       Pal N.R. and Pal S.K. (1993), ‘A review on image segmentation techniques’, Pattern Recognition, Vol. 26, Issue 9, pp. 1277-1294.
52.       Parker J.R. (1997), ‘Algorithms for Image Processing and Computer Vision’, Wiley, NY.
53.       Pavlidis T. (1977), ‘Structured Pattern Recognition’, Springer-Verlog, Berlin.
54.      Pittas I. (1993), ‘Digital Image Processing Algorithms’, Prentice Hall International, UK.
55.      Pham D.T. and Alcock R.J. (2003), ‘Smart Inspection Systems: Techniques and Applications of Intelligent Vision’, Academic Press, UK.
56.      Pratt W.K. (1978), ‘Digital Image Processing’, Wiley, New York.
57.      Rosenfeld A. and Kak A.C. (1982),’Digital Picture Processing’, 2nd edition, Academic Press, New York.
58.      Russ J.C. (1995), ‘The Image Processing Handbook’, 2nd Edition, CRC press, Boca Raton, FL.
59.      Sayood K. (1996), ‘Introduction to Data Compression’, Morgan Kaumann Publishers, USA.
60.      Solomon D.(2004), ‘Data Compression – The complete Reference’, Springer-Verlag, NY.
61.      Serra J. (1982), ‘Image Analysis and Mathematical Morphology’, Academic Press, UK.
62.      Sharma G., ‘Digital Colour Imaging Handbook’, CRC Press, USA.
63.      Sing Tze Bow (2002), ‘Pattern Recognition and Image Preprocessing’, 2nd edition, Marcel Dekker, NY.
64.       Sonka M., Hlavac V. and Boyle R. (1998), ‘Image Processing, Analysis, and Machine Vision’, Thomson Learning, New Delhi.
65.       The Mathworks, Inc., ‘Image Processing Toolbox: Users Guide’, version 4, Natick, Massachusetts.
66.       Umbaugh S.E. (1998), ‘Computer Vision and image Processing: A practical approach using CVIP tools’, Prentice Hall, NJ
67.       Vincent L. and Soille P. (1991), ‘Watersheds in digital spaces: An efficient algorithm based on immersion simulations’, IEEE Transactions on PAMI, Vol. 13, No. 6, pp. 583-598.
68.      Webb A. (1999), ‘Statistical Pattern recognition’, Arnold, London.
69.       Witten I.H. and Frank E. (2000), ‘Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations’, Morgan Kaufmann, San Francisco, CA.
70.       Woods J.W. (2006), ‘Multidimensional Signal, Image, and Video Processing and Coding’, Academic Press, USA.
71.       Xu C. and Prince J.L. (1998), ‘Snakes, Shapes, and gradient Vector Flow’, IEEE Transactions on Image Processing, Vol. 7, pp. 359-69.
72.       Zdeh L.A. (1965), ‘Fuzzy Sets’, Journal of Information Control, Vol. 8, pp. 338–353.
73.       Zhang Y.J. (1996), ‘A survey on evaluation methods for image segmentation’, Pattern Recognition, Vol. 29, Issue 8, pp. 1335-1346.
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