Visual Information Systems module introduction

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

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

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

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

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

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. 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 This module will be handled electronically Contact me anytime: Contact me anytime:

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

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

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

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

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

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

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

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

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

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

Our General Motivations Intelligent computer To simulate what people can and, to do what people cannot, to create what people can or cannot imagine 10 th 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

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) 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 Digital technology means it is even easier to produce, distribute and store materials Information retrieval? Information retrieval?

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

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

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

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

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 arge-scale data processing Indexing, searching and large-scale data processing From primitive content to semantics From primitive content to semantics Large scale problem, multiple classifier and inferential methods Case studies

Approach of the Study adopt patterns of use and patterns of computation as the leading principles. 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 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 concentrate on generic computational methods but look at applications too