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 of this module Part 2: Issues for ‘Visual Information Systems’(VIS) and the focus of this module Part 3: Visual Information Retrieval Part 3: Visual Information Retrieval

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 -- Introduction to the module and vision systems wk2 -- Visual information retrieval wk3 -- Case studies and fundamental questions wk4 --data processing and feature extraction wk5 -- image content and content analysis (colour, texture and shape) wk6 -- segmentation and annotation wk7 -- Patten Recognition wk8 -- Multiple processors wk9 -- Classifier fusion processes and inferential methods wk10 - 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. Most of the notes are available electronically Most of the notes are available 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 Rafael Gonzalez, Richard Woods, Digital Image Processing (International Edition), 2nd Edition, ISBN , Prentice Hall, 2002 Linda G. Shapiro, George C. Stockman (2001), Computer Vision, Prentice-Hall, Inc, ISBN

Assessment and Important dates first coursework, 20%, based on the lab tasks first coursework, 20%, based on the lab tasks due: week 7, Monday 26th Feb 2007, 12:00noon due: week 7, Monday 26th Feb 2007, 12:00noon first viva, 20%: week 10, Monday 19th March 2007 first viva, 20%: week 10, Monday 19th March 2007 second coursework, 40%, based on project second coursework, 40%, based on project Due: week 11, 23rd April 2007, 12noon Due: week 11, 23rd April 2007, 12noon Final viva, 20%, week 12, 30th April 2007 Final viva, 20%, week 12, 30th April 2007

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 searching for start-of-art literature 15 hours searching for start-of-art literature 100 hours to be spent on coursework 100 hours to be spent on coursework

Visual information systems? So, what does it mean “visual information”? Can a computer see as human? How much information need to be understood for processing? It might be easier to firstly think about the applications of visual information systems

Robotics/industry inspection /military

Police surveillance, genome research, biometrics, security

Remote sensing, astronomy, GIS, Earth/Planetary observation, monitoring, exploration

Medical imaging

Aware home / Intelligent environments, ubiquitous computing/sensing /eldercare technologies

Digital special effects film and TV, DTV, news and sport, creative media, art, museums

What is the fundamental question? One of the ultimate challenges of a vision system is getting a machine to recognise objects in the world and to understand what actions is taking place within its visual field. One of the ultimate challenges of a vision system is getting a machine to recognise objects in the world and to understand what actions is taking place within its visual field.

Difficulties in emulating human perception process The processing capability of human visual systems is often taken for granted The processing capability of human visual systems is often taken for granted The subtlety and difficulty of describing the exact operation of the subconscious functions presents significant difficulty in developing algorithms to emulate human visual behaviour The subtlety and difficulty of describing the exact operation of the subconscious functions presents significant difficulty in developing algorithms to emulate human visual behaviour

Another problem is that a machine cannot see too far

Visual Perception The basic approach : understand how sensory stimuli are created by the world, and then ask what must the world have been like to produce this particular stimulus? The basic approach : understand how sensory stimuli are created by the world, and then ask what must the world have been like to produce this particular stimulus?

Image and pixels

x n 0 m y f(x,y) A digital image consisting of an array of m x n pixels in the x th column and the y th row has an intensity equal to f(x,y). (r(x,y), g(x,y), b(x,y))

Vision System Overview Feature Extraction, representation of properties Labels or other forms of description Pre-processing, enhancement Object classification and Recognition Image classification and Recognition Captured data Knowledge representation

Feature Extraction, representation of properties

Image Analysis Common image analysis techniques include template matching, pattern recognition using feature extraction

Classification, recognition and retrieval

Our General Motivations Vision capability The understanding of single images and their relations with other images Scalability Autonomous Arbitrary recognition and inference Visual information is the most important but most difficult element

Topics Related to VIS Computer vision, computer/human perception Computer vision, computer/human perception Multimedia content processing Multimedia content processing Database technology Database technology Domain knowledge and its management Domain knowledge and its management Knowledge discovery Knowledge discovery human computer interaction human computer interaction computer graphics and computer animation computer graphics and computer animation artificial intelligence artificial intelligence pattern recognition, machine learning pattern recognition, machine learning robotics robotics

Content of the Module 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

Today’s exercise

Brightness Adjustment Add a constant to all values g(x,y) = f(x,y) + k g(x,y) = f(x,y) + k Where f is the original images and g the changed image; k is a constant, i.e.,50

Contrast Adjustment Scale all values by a constant g(x,y) = a* f(x,y) g(x,y) = a* f(x,y) (a = 1.5)

Subtraction

Average of two images