Medialogiske terminologier GT

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Medialogiske terminologierGT 2
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Agenda

• Why are digital images interesting?
• Applications
• Image definitions

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Why are digital images interesting?

• Humans are visual creatures in a
• visual world
• Images are (often) the primary sense
• Imagine you could only keep one sense
• A picture is worth a 1000 words
• Words are many times ambiguous
• So, if we want to build systems capable of human
  skills, then they should be capable of
  understanding images (many applications)
• Images in a computer are DIGITAL, as opposed to
  analog images in an (old) photo-camera

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Why should MED-students learn about digital
images?

• Because images are fun ?
• Understand the media Images
• Understand how images can be manipulated in order
  to create visual effects
• Rotation, scaling, blurring, etc.
• As done in standard image manipulation tools
• Remove parts of an image
• Combine graphics with real images
• Combine (part of) one image with another
• Generate control signals for an application
  (project)
• Understand how to find and follow (well defined)
  objects in an image
• Recognize objects (many industrial applications)

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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

• Special effects
• Advertising
• Movies

Andy Serkis
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Motion Capture
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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 device CCD-chip
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Where does an image come from?
Under exposed
Correct exposed

• Integration over time
• Exposure time
• Maximum charge
• Saturation
• Blooming

Over 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!

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Discrete image coordinate system
f(2,6)
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Digital Image Representation
Width

• 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 gt height)
• ROI region of interest
• To reduce the amount of data

Height
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Spatial Image Resolution

• Resolution
• The size of an area in a scene that is
  represented
• by 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 next time
• 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
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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 dont care about the file type
• The application will take care of it for you
• For example rotate
• Application
• image.x gt raw
• Rotate the raw image
• Rotated raw gt rotated_image.x
• But to write your own programs from scratch the
  images need to be in the raw format (without a
  header).

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What to remember

• A picture is worth a 1000 words
• Many applications (also for MED students)
• Definitions
• Images are discrete as opposed to continuous
• Pixel Grey-scale, intensity 8 bits 0,255
• Computer vision system
• Image acquisition, preprocessing, segmentation,
  representation, recognition
• No clear definitions!
• Image file types raw, compressed (lossy,
  lossless)

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Exercises

• Given a 512 x 512 x 8bit image. How many
  different images can be made?
• 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
• What other image applications can you think of?