CONTENTS 1. Introduction 1.1 Course Outline 1 1.2 Introduction ...

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Indicative syllabus content: • Sampling and properties of digital images • Digital Filtering • Edge detection and image segmentation • Non-linear filtering • Statistical Processing • Image analysis • Geometric Operations • Image Transforms • Image restoration • Image compression techniques • Compression in the temporal domain • Computer vision / image recognition Teaching and Learning Methods: The module is of 12 taught week’s duration in semester 1 with a 1 hour theory lecture and two hours supervised laboratory time per week. Teaching methods will include illustrated lectures, seminars, presentations and practical demonstrations. Practical work will involve supervised laboratory work that may involve both group projects and individual investigations. Written coursework may involve essays, derivations and calculations or computer-based exercises. Assessment Rationale: The examination is unseen and will test students’ ability to define concepts, derive results, describe applications and procedures and to analyse and comment on specific case studies.(Learning outcomes 1,2,4,5,6) Written <strong>Course</strong>work may consist of an essay or a number of problems requiring a detailed and accurate analysis at the appropriate level, with justifications and appropriate assumptions. Students may consult a range of sources and assignments are timed to enable valuable feedback.(Learning outcomes 1,2,4,5,6,8) Laboratory work is assessed by written reports from practical assignments. The student must demonstrate an ability to: • Understand and interpret an experimental brief and establish the appropriate experimental design • Produce relevant and operational MATLAB programmes for a range of specific tasks. • Undertake reliable collection of data using suitable instrumentation. • Analyse the data in an accurate manner, and clearly present the relevant results. • Present sound discussions and conclusions on the work, in the light of other published results or expected outcomes. (Learning outcomes 1,2,3,5,6,7,8) Assessment criteria: The extent to which the student is able to demonstrate an ability to: • Derive important relationships between concepts from the theory. • Prove important theoretical results from first principles or as directed. • Interpret the terminology relevant to image quality. • Use appropriate procedures from theory to analyse data and interpret findings. • Plan, carry out and report on experimental investigations. • Comment critically on results obtained, with due regard for experimental errors and the significance of the result. • Use research/reference literature as appropriate. DPI_Hbook 90 ©University of Westminster
Assessment Methods and Weightings: Marks are distributed in the module as follows: Written Examination 40% <strong>Course</strong> work 20% Written Reports from practical assignments 40% Sources: Essential Reading • R.C. Gonzales and R.E. Woods, Digital Image Processing, Addison-Wesley Publishing Company,(2002) • R.C. Gonzales and R.E. Woods, Digital Image Processing Using MATLAB, Pearson Education Inc,(2004) • R. Davies, Machine Vision: Theory, algorithms, practicalities –2 nd edition Academic Press, (1997) Further Reading • H.E. Burdick (1997) Digital Imaging: Theory and Applications McGraw-Hill, new York, USA. • K.R. Castelman (1996) Digital Image Processing, Prentice-Hall International, Inc., London, England. • W.K.Pratt (1991) Digital Image Processing, 2 nd edition, Wiley, New York, USA. • M. Sonka, V Hlavac and R. Boyle, Image Processing, Analysis and Machine Vision, Chapman and Hall Computing, London, England (1993) • J.L.C. Sanz, (ed) Image Technology : advances in image processing, multimedia and machine vision, Springer (1996) • A.R. Weeks, Fundamentals of Electronic Image Processing, SPIE Optical Engineering Press, Washington, USA (1996) • J. C. Russ, The Image Processing Handbook, - 4th edition, CRC Press, Inc., USA(2002) Papers in Journals: Reference to appropriate issues of Journal of Imaging Science (RPS), Journals and proceedings published by: The Institute of Electrical Engineers, the Institute of Physics, IS&T and SPIE. Further Information: MATLAB Image Processing Toolbox Manual Scion Image Manual (PDF) www.prenhall.com/Gonzaleswoodseddins www.mathworks.com DPI_Hbook 91 ©University of Westminster
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CONTENTS 1. Introduction 1.1 Course
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THE UNIVERSITY OF WESTMINSTER SCHOO
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(xii) (xiii) (xiv) To educate gradu
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student will have begun their resea
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2.4 POSSIBLE PHOTOSCIENCE ROUTE Yea
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2.6 Calendar Weeks are numbered in
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3. Learning Outcomes 3.1 Year One T
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• Describe informatively the fund
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4. Subject Benchmarks Subject bench
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Skills Chart for BSc(HONS) Photogra
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5.4 Personal Development Planning A
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• Meetings should be held at leas
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6.5 Technical Support The dedicated
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9. Teaching and Learning Strategies
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Do you appreciate both halves of yo
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9.5.4 Blackboard and the Student Re
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piece of work, you should have two
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10.6 Reporting Practical Work Unles
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eference, or government publication
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11. Award Regulations BSc (Hons) Ph
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Page 49 and 50: Full Module Title: DIGITAL IMAGE MA
Page 51 and 52: Full Module Title: METHODS IN PHOTO
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Page 63 and 64: Full Module Title: COMMERCIAL PHOTO
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Page 67 and 68: M.R.Pointer and G.G.Attridge (1997)
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Page 71 and 72: Full Module Title: CONSTRUCTED PHOT
Page 73 and 74: Full Module Title: MATHS FOR IMAGIN
Page 75 and 76: Full Module Title: MATLAB Programmi
Page 77 and 78: Full Module Title: PROJECT PLANNING
Page 79 and 80: Full Module Title: MAJOR PROJECT Sh
Page 81 and 82: Full Module Title: IMAGE QUALITY VE
Page 83 and 84: P.G.Engeldrum (2000) Psychometric S
Page 85 and 86: Laboratory work is assessed by prac
Page 87 and 88: Assessment Rationale: • Emphasis
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Page 91: Full Module Title: DIGITAL IMAGE PR
Page 95 and 96: For the purposes of gaining credits
Page 97 and 98: Student Module Profile From the 200
Page 99 and 100: Curriculum Vitae Name: Current Posi
Page 101 and 102: Triantaphillidou S., Image Quality
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