CS 376b Introduction to Computer Vision 02 / 08 / 2008 Instructor: Michael Eckmann.

CS 376bIntroduction to Computer Vision

02 / 08 / 2008

Instructor: Michael Eckmann

Michael Eckmann - Skidmore College - CS 376b - Spring 2008

Today’s Topics• PGM, PPM file formats (see webpages and examples)

• RGB color model• HW (required)• Image acquisition/formation and representation

– problems with digital images

– some definitions

• quantization problems

• compression comment

• frames of reference

• hole counting algorithm from ch. 1

Michael Eckmann - Skidmore College - CS 376 - Spring 2008

RGB Color Model• Color = rR + gG + bB

• r, g and b range from 0.0 to 1.0

• notice:

white's (r,g,b) = (1,1,1)

black's (r,g,b) = (0,0,0)

complementary colors are

those that add up to (1,1,1)

can anyone tell me 2?

• image from “Computer Graphics” by Hearn and Baker


individual Homework• write a C++ program to generate a semi random PPM image

(named verticalStripes.ppm) with 400 columns and 300 rows

– each stripe should

• be 10 pixels high and 400 pixels wide

• start with a random 3 byte RGB color (each R G and B should be a number 0 - 255)

– select the largest of the random R G B generated and gradually and regularly alter that channel from that number down to 0 along the row of 300 pixels

– so, 30*3 random numbers are to be generated (3 at a time)

• use one set of 3 as the R G and B value of of the leftmost pixel for 10 rows


team Homework• you should group into 2 teams of 2 and 1 team of 3 students

– each team should have at least one student who took CG to help with 2.2 part c)

• exercises from the text

– 2.2 (p. 25)

– 2.4 (p. 34)

– and ...


team Homework• 2.8 (p. 39)

• find two images (a face and a landscape) on the web with a license that allows you to use them legally (e.g. creative commons) provide attribution regardless whether the license requires it (and the exact url where you found it)

– use gimp (or another program) to save it in PPM ascii, PPM raw, GIF, TIFF and 5 different quality levels (10, 25, 40, 55, 70 and original) of JPEG – make sure to change name of JPEG files when saving to diff. quality levels

• always start from the original to produce the 5 other quality levels

– make a chart containing file size in bytes for each format and examine by eye the various images and make quality judgements (consider the overall picture and fine details) specify the #rows&cols


CCD cameras

• image figure 2.2 in “Computer Vision” by Shapiro and Stockman

• discrete cells convert light energy into electrical charges and they integrate the light energy falling on them during the time the shutter is open


Video• 30 frames (images) per second for NTSC video

– can be a different rate of images e.g. 15, 60

• almost always compressed due to the large size

– example:

• a 640 x 480 image with 3 bytes per pixel uncompressed is how big?

• a 90 minute video w/ 30 frames per second uncompressed with each frame as above is how big?

• COTS (commercial off the shelf) CCD video cameras are made for other purposes than for computer vision

– e.g. pixels may not be square (4:3 ratio)

• computer vision most likely prefers/assumes square pixels


Problems in digital images• geometric distortion

•due to an imperfect lens the light beams don't travel on the correct path

•e.g. barrel distortion for small focal lengths

• scattering

•medium that the light travels through is rarely a vacuum, hence the light bends (e.g. air, water, other liquids ...)

• blooming

•each CCD cell is close to their neighbors so, light can leak between neighboring cells

• CCD variations

•some cells might not be all equally sensitive due to variations in the manufacturing process


Problems in digital images• clipping or wrap-around

•when an analog value is converted to digital, high values may be “clipped” to the max digital value, wrap around is like a modulus operator

• quantization effects

•converting a continuous range to a discrete range (we'll see one type of problem with this shortly)

• chromatic distortion

•lens has the index of refraction (bending) different for different wavelengths (colors)

•let's look back at the lightbulb picture, what's the effect?


Problems in digital images

• images from “Computer Vision” by Shapiro and Stockman

• Blooming(left) & Barrel distortion (right)


some definitions• analog image

– “infinite” precision in space and intensity value

• digital image

– discrete 2D array of limited precision intensity values

• grey-scale image

– one intensity per pixel (e.g. if one byte intensity range is 0-255)

• multispectral image

– a vector of values at each pixel (for color, usually 3 values representing values for red, green and blue)

• binary image

– each pixel has value 0 or 1


some definitions• labelled image

– all pixel values are from some finite alphabet

– usually generated from a digital image based on some way to decide which label a pixel gets

– example on board

• picture function

– f(x,y) where x and y are spatial variables and f(x,y) is the intensity at x,y

from Shapiro & Stockman figure 2.9 in “Computer Vision”

quantization problems example


compression comment• run coding is used as part of some compression algorithms

• to give you a sense of how an image can be losslessly compressed using run length coding

– count runs of 0's and runs of 1's

• example using 1 byte (8 bits) greyscale image

– divide up your image into 8 “bit planes”

• each plane is a binary image

• run code each binary image

• note 127 -> 128 problem– solution --- first convert all to a “grey code” which has

the property that successive greyscale differences differ in only 1 bit


frames of reference• world (W)

• object (O)

• camera (C)

• real image (F)

• pixel (I)


frames of reference


hole counting

• external corners

• internal corners

• these are sufficient as long as the holes are “4-connected” and simple (e.g. no donuts)


hole counting algorithm

CS 376b Introduction to Computer Vision 02 / 08 / 2008 Instructor: Michael Eckmann.

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