Visual Information SystemsVisual Information Systems

module introductionmodule introduction

Lecture PlanLecture Plan

Part 1: MODULE OVERVIEWPart 1: MODULE OVERVIEW Part 2: Issues for ‘Visual Information Part 2: Issues for ‘Visual Information

Systems’(VIS) and the focus in this Systems’(VIS) and the focus in this modulemodule

Part 3: Image fundamentalsPart 3: Image fundamentals

Module OverviewModule Overview

Many research issues in VIS is Many research issues in VIS is emerging subjects, research in VIS is emerging subjects, research in VIS is still immature, suitable for an still immature, suitable for an interactive research-based moduleinteractive research-based module

Interactive class and labInteractive class and lab

Module structureModule 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 ModuleSeminar-based Module

Weeks 1-5: Lectures with further reading Weeks 1-5: Lectures with further reading given; also case studies given for discussion; given; also case studies given for discussion; initial lab exercisesinitial lab exercises

Weeks 6-10: Weeks 6-10: Mixture of lectures, seminars, group presentation Mixture of lectures, seminars, group presentation

and lab exercisesand lab exercises Interim viva and feedback (on literature, topics in Interim viva and feedback (on literature, topics in

lectures and projects)lectures and projects) Lab sessions and surgery hours (encourage Lab sessions and surgery hours (encourage

using Java and other programme languages)using Java and other programme languages) Individual meetings throughout the courseIndividual meetings throughout the course

ProjectsProjects Encourage working in group on large scale Encourage working in group on large scale

projects, but need individual contributionprojects, but need individual contribution Projects will be written up as your courseworkProjects will be written up as your coursework Any innovative work is encouraged to publish Any innovative work is encouraged to publish

as technical reports, conference papers, and as technical reports, conference papers, and journal articles where appropriatejournal articles where appropriate

A list of possible projects:A list of possible projects: In the project proposal of the yearIn the project proposal of the year Other suggestions are welcomeOther suggestions are welcome Full participation in all classes/labs is required to Full participation in all classes/labs is required to

pass the modulepass the module

Lecture Notes and ReferencesLecture Notes and References

There is no set textbook for this module. There is no set textbook for this module. Reading will be advised for each lecture: Reading will be advised for each lecture: this will be available in the library, on-line this will be available in the library, on-line or photocopies will be provided.or photocopies will be provided.

This module will be handled electronicallyThis module will be handled electronically http://www.computing.surrey.ac.http://www.computing.surrey.ac.ukuk/CSM16/CSM16

Contact me anytime:Contact me anytime: h.tang@surrey.ac.h.tang@surrey.ac.ukuk

Useful referencesUseful 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

AssessmentAssessment

Interim Viva (25%) Interim Viva (25%) – – week 9week 9

Report (40%) Report (40%) – – week 11week 11

Project Presentation and Viva (35%)Project Presentation and Viva (35%) - week 12 or week after- week 12 or week after

Important datesImportant dates

Interim Viva – week 9Interim Viva – week 9

2525thth April 2005, week 11: coursework report April 2005, week 11: coursework report duedue

week 12: oral examination during the week or week 12: oral examination during the week or week afterweek after

CreditCredit

CourseworkCoursework See project proposalSee project proposal Please Please DO NOTDO NOT use unauthorised materials use unauthorised materials

15 credit module = approx. 150 hours of 15 credit module = approx. 150 hours of studystudy 30 hours of lectures / seminars / labs / tutorials30 hours of lectures / seminars / labs / tutorials 5 hours one to one discussion5 hours one to one discussion 15 hours further reading15 hours further reading 100 hours to be spent on coursework100 hours to be spent on coursework

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

ApplicationsApplications ClassicalClassical

robotrobot medical imagingmedical imaging remote sensingremote sensing AstronomyAstronomy

TodayToday image interpretationimage interpretation biometrybiometry GIS, (Earth/Planetary Observation, monitoring, GIS, (Earth/Planetary Observation, monitoring,

exploration)exploration) human genome projecthuman genome project Film and TV, DTV, News and sportFilm and TV, DTV, News and sport Creative media, art, museumsCreative media, art, museums

domain specific?domain specific?

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

Sample applications Sample applications - Biometry- Biometry

Using personal characteristics to Using personal characteristics to identify a personidentify a person fingerprintsfingerprints faceface irisiris DNADNA gaitgait etcetc

Iris ScanIris Scan

Striations on iris are individually Striations on iris are individually uniqueunique

Obvious applicationsObvious applications securitysecurity PIN PIN

} fixed number of samples

Locate the eye in the head image

Radial resampling of iris

Numerical descriptionAnalysis

Our General MotivationsOur 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

Our General MotivationsOur General Motivations Information and knowledge

Information can be useful only if they are located and organised.

People’s need for information remains the People’s need for information remains the same, however “the form in which the same, however “the form in which the information is expressed and the methods information is expressed and the methods that are used to manage it are greatly that are used to manage it are greatly influenced by technology, and this creates influenced by technology, and this creates change” (Arms 2000)change” (Arms 2000)

Digital technology means it is even easier Digital technology means it is even easier to produce, distribute and store materialsto produce, distribute and store materials

Information retrieval?Information retrieval?

Topics Related to VISTopics Related to VIS

Computer visionComputer vision Multimedia content processingMultimedia content processing Human perceptionHuman perception Database technologyDatabase technology Domain knowledge and its Domain knowledge and its

managementmanagement HCIHCI Knowledge discoveryKnowledge discovery Multiple disciplinary researchMultiple disciplinary research

Key issues at VIS 2005Key issues at VIS 2005

2D and 3D graphical visual data 2D and 3D graphical visual data retrievalretrieval

Benchmarking of image databasesBenchmarking of image databases Content-based indexing and retrievalContent-based indexing and retrieval Designing visual portals Fusion of Designing visual portals Fusion of

pictorial and non-pictorial pictorial and non-pictorial informationinformation

Gestural queries and visual queriesGestural queries and visual queries Hypermedia of picture and text Image Hypermedia of picture and text Image

and video archival and retrievaland video archival and retrieval

Key issues at VIS 2005Key issues at VIS 2005

Implementing visual metaphorsImplementing visual metaphors Mobile cartography (methodologies, Mobile cartography (methodologies, cognition, systems, etc.)cognition, systems, etc.)

Mobile visual information systemsMobile visual information systems Physical storage of image databasesPhysical storage of image databases Picture representation languagesPicture representation languagesProcessing, features extraction and Processing, features extraction and aggregationaggregation

Semantic models for visual Semantic models for visual informationinformation

Key issues at VIS 2005Key issues at VIS 2005 Storage and data management issuesStorage and data management issuesVisual data-miningVisual data-mining

Visual information handling in e-Visual information handling in e-learninglearning

Visual information system architecturesVisual information system architectures Visual query browsersVisual query browsers Visual query models and languagesVisual query models and languages Visualization of results in data miningVisualization of results in data mining Visualizing pictorial and non-pictorial Visualizing pictorial and non-pictorial informationinformation

Content of the ModuleContent 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 arge-scale data processing

From primitive content to semanticsFrom primitive content to semantics Large scale problem, multiple classifier and inferential

methods Case studies

Approach of the StudyApproach of the Study

adopt patterns of use and patterns of adopt patterns of use and patterns of computation as the leading principles. computation as the leading principles.

follow the data as they flow through the follow the data as they flow through the computational process and consider computational process and consider alternative processes with the same alternative processes with the same position in the flow position in the flow

concentrate on generic computational concentrate on generic computational methods but look at applications toomethods but look at applications too