CS292 Computational Vision and Language

Week 1 - 2

Visual Perception

• The main focus will be on the processing of the raw information that they provide.

• 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?

Colour image and video sequence

• colour can be conveyed by combining different colours of light, using three components (red, green and blue): R = r(x,y); G = g(x,y); B = b(x,y), where R, G, B are defined in a similar way to F.

• The vector (r(x,y), g(x,y), b(x,y)) defines the intensity and colour at the point (x,y) in the colour image.

• A video sequence is, in effect, a time-sampled representation of the original moving scene.

• Each frame in the sequence is a standard colour, or monochrome image and can be coded as such.

• a monochrome video sequence may be represented digitally as a sequence o 2-D arrays [F1, F2, F3..FN].

Java example on image representation and resolution, try this in the lab class

Image Resolution

• How many pixels– spatial resolution

• How many shades of grey/colours– amplitude resolution

• How many frames per second– temporal resolution

Spatial Resolution

n, n/2, n/4, n/8, n/16 and n/32 pixels per unit length

amplitude resolution-Shades of Grey

8, 4, 2 and 1 bit images.

Temporal Resolution

– how much does an object move between frames?

– Can motion be understood unambiguously?

• Nyquist’s Theorem– A periodic signal can be reconstructed if the

sampling interval is half the period– An object can be detected if two samples span

its smallest dimension

Colour Representation

• three primaries could approximate many colours

• red, green, blue• C= rR+gG+bB

• Other Colour Models– YMCK– HSI– YCrCb

Objectives of vision part

• Understand the fundamentals in machine perception– Understand components in vision systems

– Be familiar with common operations for processing images

– Be able to implement simple image processing operations

– Be able to implement simple object recognition

• Evaluate a vision system• additionally: encourage the students to practise

more basic and advanced Java programming

Week lectures Labs 1 Introduction and simple

operationsbrightness, contrast, enlarge, averaging, subtraction

2 (LP) Image processing and transform 1

brightness, contrast, enlarge, averaging, subtraction

3 (LP) Image processing and transform 2

Convolution and histogram

4 (LP) Segmentation (1) segmentation

5 (LP) Classification and Recognition Object recognition

6 (LP) Reading week

7 (LP) Language 1

8 (LP) Language 2

9 (LP) Language 3

10 (LP) Language 4

11 revision

Deadlines

• To Undergraduate Office• First assignment: week 5,    Monday 12th

Feb 2007, 12:00noon.

• Second assignment: week 7,    Monday 26th Feb 2007, 12:00noon

• Third assignment: week 10,  Monday 19th March 2007, 12:00noon

Assessment

Components of Assessment

Method(s) weighting

Coursework for vision part

Program results and short reports 35%

Coursework for language

part

report 15%

Examination A 2-hour examination (one question on vision, two on

language)

50%

Recommended Texts

• Nick Efford, Digital Image Processing, A Practical Introduction using Java (2000), Addison Wesley, ISBN 0201596237.

• Tim Morris (2004), Computer Vision and Image Processing, Palgrave MacMillan, ISBN 0333994515

• Patrick H Winston, (1992), Artificial Intelligence (Third Edition), Addison Wesley Publishers Co. ISBN 0201533774

• Rob Callan (2003), Artificial Intelligence, Palgrave MacMillan, ISBN 0333801369

• Linda G. Shapiro, George C. Stockman (2001), Computer Vision, Prentice-Hall, Inc, ISBN 0-13-030796-3