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Introduction to Digital Images Lecture on the image part (#1)

Automatic Perception (AP1)

Volker Krüger

Aalborg Media Lab

Aalborg University Copenhagen

vok@media.aau.dk

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Who am I?• Graduated 1996 as a Computer Scientist (Dipl. Inf.) from

the University of Kiel, Germany, Specialized in computer vision

• Ph.D. (Dr. Ing.) in Kiel, 2000• Work at the University of Maryland, Washington DC• Research interests: Computer vision, motion capture,

human gesture recognition, Biometrics, AI• Teaching since 1996

– Supervision– Signal (image) processing, pattern recognition

• I am with Medialogi since its start in Fall 2002

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Why are digital images interesting?

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The (rough) plan for the 15 lectures• Understand the media: Images

– #1 + all others

• Understand how images can be manipulated in order to create visual effects– #2-11

• Generate control signals for an application (project)– Video processing – #12-14

• Summary: #15• PE-course: Let me know your needs!

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General information• AP lectures

– Timing• What is most suitable• Lecture, break, lecture, exercises

– Feel free to interrupt! – Exams: How to pass– Slides: for main key words, images, videos,

graphs• Necessary to make notes!!!

– Questions and participation during the lectures– Leave your comments at the end of each lecture

at the exit!!

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General Information• Literature

– ”Digital image processing” Nick Efford– Will not use the Java part– Alternative: Gonzalez & Woods, Digital Image Processing

• Web– Literature, slides, PE-questions, key-words, Slides will be online

latest 24h before the lecture.

• Exercises– In the end of the slides, – We will talk about the exercises in the beginning of each lecture.– Florian Pilz is the student helper.

• SW– EyesWeb

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Change in Schedule• Thu, 9:00-10:30: Exercise

10:40-12:00 Lecture

• Tue: Please check the schedule after Thursday carefully for possible changes !!!

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Plan for today• Digital images

– Applications, definitions

• Color images

• Exercises

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Applications

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Digital Image• Why digital ?• Before 1920: Image transmission from USA to

Europe: more than a week: by ship!• Early 1920s: Bartline cable picture transmission

system: Transmission in three hours!

• Transmission via telegraph/wire, radio signals for newspapers

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Small Progress• Small progress in digital imaging until

1964

• Jet Propulsion Lab (JPL) in Pasadena, CA– Transmission and correction of lunar images

from Ranger 7.

• Not so good quality so the images had to be processed before they could be viewed

• Since then many applications…

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Examples: Image Correction

• Needed when image data is erroneous: – Bad transmission – Bits are missing: Salt & Pepper Noise

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Image Deblurring: Motion Blur

• Can be used when a camera or object is moved during exposure

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Deblurring

• Can be used when the camera was not focused properly!!

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Image manipulation• Image improvement, e.g. too dark image

• Rotate + scale

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Medical Image Processing

• Image Processing is widely used

• E.g. Analysis of microscopic images

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Medical Image Processing

• MR/CT Imaging of a human body

• Use for Brain Surgery

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Conveyer belt applications

• Checking and sorting– For example: checking bottles in the

supermarket

• Quality control– Does the object have the correct dimensions,

color, shape, etc.?– Is the object broken?

• Robot control– Find precise location of the object to be picked

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Biometrics

• Recognizing/verifying the identity of a person by analyzing one or more characteristics of the human body

• Characteristics:– Fingerprint, eye (retina, iris), ear, face, heat profile,

shape (3D face, hand), motion (gait, writing), …

• Applications: – Verifying: Access control (bio-passports)– Recognizing: Surveillance: 9/11

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Chroma keying

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Analysis of Sport Motions

Here: Analysis of motion of Sarah Hughes• 3D Tracking of body parts• Motion interpretation• Action recognition

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Motion Capture

Andy Serkis

• Special effects– Advertising

– Movies

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Motion Capture

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What you will be able to do at the end of this semester

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Why should MED-students learn about digital images?

• Discuss this during the break!

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Websites• The course website is:

www.media.aau.dk/med3

coursesip

Site contains:

Plan for the lectures, links to

• slides,

• Software

• Exercises

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Image definitions

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Where does an image come from?

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Where does an image come from?

Charged coupled deviceCCD-chip

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Where does an image come from?

• Integration over time– Exposure time– Maximum charge

• Saturation• Blooming

Under exposed

Over exposed

Correct exposed

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Where does an image come from?

• Image elements, picture elements, pels, pixels

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Imaging system• Image acquisition

• Illumination– Passive: sun– Active: ordinary lamp, X-ray, radar, IR

• Camera lens– Focus the light on the CCD chip

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Digital Image Representation

• Image is seen as a discrete function f(x,y) as opposed to a continuous function (show)

• x and y cannot take on any value!

y

x

f(x,y)

Origin

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Discrete image coordinate system

y

x

f(x,y)

Origin

y

xf(0,0)

f(?,?)

f(2,6)

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Digital Image Representation

• An image f(x,y) is represented

as an Array• Width =

number of pixels in x-direction• Height = number of pixels in y-direction• Size (width x height, width > height)• ROI = region of interest

– To reduce the amount of data

Width

Hei

ght ROI

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Spatial Image Resolution:• Resolution

– The size of an area in a scene that is representedby one pixel in the image

• Different Resolutions are possible (256x256….16x16)

• Lower resolution leads to data reduction!

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Digital Image Representation

• Pixel representation (bits)– A few words on bits and bytes: One bit: {0,1}

• One byte = eight bits– One pixel: one byte = eight bits = one number: [0,255] (show)– Grey-scale, intensity, black/white: 8 bits = [0,255]– Binary image: 1 bit {0,1}. Black and white: visualized as: 8 bit

{0,255} – Colors: after the break

• Image representation (2D image versus 3D data)– (show: 2D-gel: crop lower left corner)

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Gray-level Resolution: Quantization

• Different gray-level resolutions: 256, 128, …, 2• Less gray-levels leads to data reduction.• For 256, 128, 64 gray-levels: Difference hardly visible

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Working with images….• Image manipulation

– Simple operations, e.g., scale image• Image processing

– Improve the image, e.g., remove noise• Image analysis

– Analyze the image, e.g., find the person in the image• Machine vision

– Industry, e.g., Quality control, Robot control• Computer vision

– Everything: multiple cameras, video-processing, etc.

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Fundamental Steps in Computer Vision

Knowledge baseProblemdomain Image

acquisition

Preprocessing

SegmentationRepresentationand description

Recognitionand InterpretationResult

Point 1: 22,33Point 2: 24, 39…..

Actor sitting

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Image file types• image.jpg, image.tif, image.gif, image.png, image.ppm,

…. • Raw:

– No data is lost – Header + data (234 235 32 21…)– For example: image.pgm – The file can be viewed

• Lossless compression:– No data is lost, but the file cannot be viewed– For example: image.gif

• Lossy compression:– Better compression– Some data is lost (optimized from the HVS’ point of view)– The file cannot be viewed– For example: image.jpg

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Image file types• Normally you don’t care about the file type

– The application will take care of it for you:– For example: rotate

• Application– image.x => raw– Rotate the raw image– Rotated raw => rotated_image.x

• But to write your own programs from scratch the images need to be in the raw format (without a header).

• EyesWeb will do this for you

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What have you learned today?

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Exercises• Questions to the lecture?• What was good about the lecture and what could have

been better?• Discuss the P0-Projects in the light of what you have

heard, today.• What other image applications can you think of?• Given a 512 x 512 x 8bit image. How is the memory

size reduced when you: – Decrease the grayscale resolution repeatedly by 2– Decrease the size of the image repeatedly by 2

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PE-Questions1) Name a few application areas where computer

vision can be applied2) What is the difference between discrete images

and continuous images3) What is a CCD chip?4) What does it mean that a pixel is saturated?5) How do you calculate the amount of memory

needed to store a raw image?6) What is Quantization?7) How do you convert from a binary string, for

example [1 0 1 1 0 0 1 0], to a standard number (base 10)?