CS292 Computational Vision and Language Week 1 - 2.
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Transcript of CS292 Computational Vision and Language Week 1 - 2.
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CS292 Computational Vision and Language
Week 1 - 2
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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?
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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].
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Java example on image representation and resolution, try this in the lab class
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Image Resolution
• How many pixels– spatial resolution
• How many shades of grey/colours– amplitude resolution
• How many frames per second– temporal resolution
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Spatial Resolution
n, n/2, n/4, n/8, n/16 and n/32 pixels per unit length
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amplitude resolution-Shades of Grey
8, 4, 2 and 1 bit images.
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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
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Colour Representation
• three primaries could approximate many colours
• red, green, blue• C= rR+gG+bB
• Other Colour Models– YMCK– HSI– YCrCb
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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
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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
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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
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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%
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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