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Visual Information Systems

Visual Information Systems. module introduction. Lecture Plan. Part 1: MODULE OVERVIEW Part 2: Issues for ‘Visual Information Systems’(VIS) and the focus in this module Part 3: Image fundamentals. Module Overview.

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Visual Information Systems

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  1. Visual Information Systems module introduction

  2. Lecture Plan • Part 1: MODULE OVERVIEW • Part 2: Issues for ‘Visual Information Systems’(VIS) and the focus in this module • Part 3: Image fundamentals

  3. Module Overview • Many research issues in VIS is emerging subjects, research in VIS is still immature, suitable for an interactive research-based module • Interactive class and lab

  4. Module structure wk1 -- (3L) Introduction to the module and vision systems wk2 -- (2LP) Visual information retrieval wk3 -- (2LP) Case studies and fundamental questions wk4 -- (2LP) image content and content analysis (colour, texture and shape) wk5 -- (2LP) data processing and feature extraction wk6 -- (LSP) segmentation and annotation wk7 -- (LSP) system integration wk8 -- (LSP) Multiple processors wk9 -- (LSP) Classifier fusion processes and inferential methods wk10 - (TTP) Further issues L- Lecture; S – Seminar; P – Practice; T- Tutorial

  5. Seminar-based Module • Weeks 1-5: Lectures with further reading given; also case studies given for discussion; initial lab exercises • Weeks 6-10: • Mixture of lectures, seminars, group presentation and lab exercises • Interim viva and feedback (on literature, topics in lectures and projects) • Lab sessions and surgery hours (encourage using Java and other programme languages) • Individual meetings throughout the course

  6. Projects • Encourage working in group on large scale projects, but need individual contribution • Projects will be written up as your coursework • Any innovative work is encouraged to publish as technical reports, conference papers, and journal articles where appropriate • A list of possible projects: • In the project proposal of the year • Other suggestions are welcome • Full participation in all classes/labs is required to pass the module

  7. Lecture Notes and References • There is no set textbook for this module. Reading will be advised for each lecture: this will be available in the library, on-line or photocopies will be provided. • This module will be handled electronically • http://www.computing.surrey.ac.uk/CSM16 • Contact me anytime: • h.tang@surrey.ac.uk

  8. Useful references • Nick Efford, Digital Image Processing, A Practical Introduction using Java, Addison Wesley, ISBN 0201596237, May 2000 • Tim Morris (2004), Computer Vision and Image Processing, Palgrave MacMillan, ISBN 0333994515 • Del Bimbo (1999). “Visual Information Retrieval”, Morgan Kaufmann Publishers, Inc • Forsyth and Ponce (2003), “Computer Vision- A Modern Approach”, Part VII, Prentice Hall

  9. Assessment • Interim Viva (25%) • – week 9 • Report (40%) • – week 11 • Project Presentation and Viva (35%) • - week 12 or week after

  10. Important dates • Interim Viva – week 9 • 25th April 2005, week 11: coursework report due • week 12: oral examination during the week or week after

  11. Credit • Coursework • See project proposal • Please DO NOT use unauthorised materials • 15 credit module = approx. 150 hours of study • 30 hours of lectures / seminars / labs / tutorials • 5 hours one to one discussion • 15 hours further reading • 100 hours to be spent on coursework

  12. Figure 5-10 image B95-00016-01.3.S1.X5.4.jpg (above) and the its annotation window generated in I-Browse system

  13. Applications • Classical • robot • medical imaging • remote sensing • Astronomy • Today • image interpretation • biometry • GIS, (Earth/Planetary Observation, monitoring, exploration) • human genome project • Film and TV, DTV, News and sport • Creative media, art, museums

  14. domain specific? • The higher level interpretation, the more more domain knowledge and its management are required. Domain specific may simplify some of the technological challenges

  15. Sample applications - Biometry • Using personal characteristics to identify a person • fingerprints • face • iris • DNA • gait • etc

  16. Iris Scan • Striations on iris are individually unique • Obvious applications • security • PIN

  17. } fixed number of samples Locate the eye in the head image Radial resampling of iris Analysis Numerical description

  18. Our General Motivations • Intelligent computer • To simulate what people can and, to do what people cannot, to create what people can or cannot imagine • 10th dimension in creating new media, new knowledge and innovative computer • Vision capability • The understanding of single images and their relations with other images • Visual information is the most important but most difficult element

  19. Our General Motivations • Information and knowledge • Information can be useful only if they are located and organised. • People’s need for information remains the same, however “the form in which the information is expressed and the methods that are used to manage it are greatly influenced by technology, and this creates change” (Arms 2000) • Digital technology means it is even easier to produce, distribute and store materials • Information retrieval?

  20. Topics Related to VIS • Computer vision • Multimedia content processing • Human perception • Database technology • Domain knowledge and its management • HCI • Knowledge discovery • Multiple disciplinary research

  21. Key issues at VIS 2005 • 2D and 3D graphical visual data retrieval • Benchmarking of image databases • Content-based indexing and retrieval • Designing visual portals Fusion of pictorial and non-pictorial information • Gestural queries and visual queries • Hypermedia of picture and text Image and video archival and retrieval

  22. Key issues at VIS 2005 • Implementing visual metaphors • Mobile cartography (methodologies, cognition, systems, etc.) • Mobile visual information systems • Physical storage of image databases • Picture representation languagesProcessing, features extraction and aggregation • Semantic models for visual information

  23. Key issues at VIS 2005 • Storage and data management issuesVisual data-mining • Visual information handling in e-learning • Visual information system architectures • Visual query browsers • Visual query models and languages • Visualization of results in data mining • Visualizing pictorial and non-pictorial information

  24. Content of the Module • Scope of the VIS • The characteristics of the domain and sources of knowledge • Visual content • Primitive visual properties - preprocessing • Visual features – for visual perception; for indexing and searching , interpretation at different levels • interpretation of a single image and the similarity measure • Indexing, searching and large-scale data processing • From primitive content to semantics • Large scale problem, multiple classifier and inferential methods • Case studies

  25. Approach of the Study • adopt patterns of use and patterns of computation as the leading principles. • follow the data as they flow through the computational process and consider alternative processes with the same position in the flow • concentrate on generic computational methods but look at applications too

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