Computer Vision 2Tavita Su’a

INFO410 & INFO350 S2 2015INFO410 & INFO350 S2 2015

INFORMATIONSCIENCE

Visual Computing

INFO410 S2 2015 COMPUTER VISION 2 (Tavita Su’a) SLIDE 2INFORMATION SCIENCE

Analysing images and producing descriptions that can be used to interact with the environment (Horn, 1986).

Applications include face detection, automatic number plate recognition and facial recognition

Typical tasks include Feature: Detection (using edges, corners or blobs as

descriptors) e.g. FAST Description e.g. SIFT Matching e.g. Cross Correlation

Recap

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Overview Images

Binary Grayscale Color

Image Processing Potential Exam Questions References

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What is an (digital) image?

http://www.jvs.org.uk/wp-content/uploads/2015/07/flower-meadow-20392-20902-hd-wallpapers.jpg

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Images

http://www.quora.com/What-is-the-difference-between-a-binary-image-and-a-gray-scale-image

Binary

Grayscale

Color

http://images4.fanpop.com/image/photos/21800000/3-Penguins-kingjfan3-21879304-1024-768.jpg

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Binary

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Grayscale

=

255 255 255 255 255 255 255 255 255 255 255 255

255 255 255 255 255 255 255 255 255 255 255 255

255 255 255 20 0 255 255 255 255 255 255 255

255 255 255 75 75 75 255 255 255 255 255 255

255 255 75 95 95 75 255 255 255 255 255 255

255 255 96 127 145 175 255 255 255 255 255 255

255 255 127 145 175 175 175 255 255 255 255 255

255 255 127 145 200 200 175 175 95 255 255 255

255 255 127 145 200 200 175 175 95 47 255 255

255 255 127 145 145 175 127 127 95 47 255 255

255 255 74 127 127 127 95 95 95 47 255 255

255 255 255 74 74 74 74 74 74 255 255 255

255 255 255 255 255 255 255 255 255 255 255 255

255 255 255 255 255 255 255 255 255 255 255 255

1 channel, 0 = black, 255 = white

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Color3 Channels – Red, Green, Blue

https://en.wikipedia.org/wiki/Grayscale#/media/File:Beyoglu_4671_tricolor.png

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Grayscale vs Color

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Imaging Process

The scene is• projected on

a 2D plane • sampled on

a regular grid, and each sample is

• quantized (rounded to the nearest integer)

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Digital Image3 resolutions

• Spatial (no. of pixels)• Intensity (no. of grey levels)• Temporal (number of frames per second)

Computers have limited resolutions• Quantization is required due to the limited

Intensity resolution• Sampling is required due to limited spatial

and temporal resolution

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Digital Image

continuous color input

disc

rete

col

or o

utpu

t

continuous colors mapped to a finite, discrete set of

colors.

continuous colors mapped to a finite, discrete set of

colors.

SamplingQuantization

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Digital Image is…A 2D rectilinear array of

samples

sampledreal image quantized sampled & quantized

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Image Noise• Light Variations• Camera Electronics• Surface Reflectance• Lens

Given a camera and a still scene, how can you reduce noise?

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Image as a function• We can think of an image

as a function, f,• f: R2 R• f (x, y) gives the

intensity I, at position (x, y)

• A color image is just three functions pasted together. We can write this as a “vector-valued” function ( , )

( , ) ( , )

( , )

r x y

f x y g x y

b x y

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Image Processing

An image processing operation typically defines a new image g in terms of an existing image f

We can transform the domain or the range of f• Range transformation:

• Domain transformation:

Filtering also generates new images from an existing image

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Point Processing• Simplest kind of range transformation

• independent of position (x,y):• for each original image intensity value I, function t()

returns a transformed intensity value t(I)• Function t() is applied to every pixel, the spatial

information (x,y) is ignored

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Point Processing

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Gamma Correction• Monitors have a intensity

to voltage response curve which is roughly a 2.5 power function

• Send v -> actually display a pixel which has intensity equal to v 2.5

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Gamma Correction• This example shows

the original, unprocessed image and subsequent processed images with varying values of gamma applied.

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Image filtering

Modify the pixels in an image based on some function of a local neighborhood of each pixel

Common method used is linear filtering: • Convolution and Cross Correlation• Replace each pixel by a linear combination (a weighted

sum) of its neighbors• The prescription for the linear combination is called the

kernel (aka mask or filter)

0.5

0.5 00

10

0 00

kernel

8

Modified image dataLocal image data

6 14

1 81

5 310

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Why filter images?

• Smooth• Sharpen• Intensify• Enhance• Noise Reduction

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Cross-correlation Method

Denoted By:

• Filtering an image: replace each pixel with a linear combination of its neighbors

• The filter or kernel or mask H[u,v] is the prescription for the weights in the linear combination

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Convolution Method

Denoted By:

• Flip the filter in both dimensions (top to bottom, left to right)

• Then apply cross-correlation

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Convolution Method

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Sample Exam Questions

1. What is quantization and sampling?2. What is the difference between a range

transformation and a domain transformation?3. Given a camera and a still scene, how can you

reduce noise?4. List and explain two uses of image filtering. 5. Identify and explain two types of image resolutions. 6. Briefly describe how a kernel or mask can be used

to filter an image.

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http://orig03.deviantart.net/9c26/f/2010/171/0/0/biomech_eye_by_kirkh.jpg http://www.slideshare.net/tokakhaled5209/ch2-43129446 http://web.pdx.edu/~

jduh/courses/Archive/geog481w07/Students/Ludwig_ImageConvolution.pdf http://www.cs.cornell.edu/courses/cs4670/2013fa/lectures/lectures.html http://www.cs.cmu.edu/afs/cs/academic/class/15385-s06/lectures/ppts/ http://vision.princeton.edu/courses/COS429/2014fa/ http://www.cambridgeincolour.com/tutorials/gamma-correction.htm http://www.csd.uwo.ca/courses/CS4487a/Lectures/lec03_image_proc.pdf

References