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Chapter 2 : Imaging and Image Representation

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Title: Chapter 2 : Imaging and Image Representation


1
Chapter 2 Imaging and Image Representation
  • Computer Vision Lab.
  • ??????? 3??
  • ???

2
Contents
  • 2.6 Richness and Problems of Real Imagery
  • 2.7 3D Structure form 2D Image
  • 2.8 Five Frames of Reference
  • 2.9 Other Types of Sensors
  • 2.1 Sensing Light
  • 2.2 Image Device
  • 2.3 Problems in Digital Images
  • 2.4 Picture Function And Digital Image
  • 2.5 Digital Image
  • Formats

3
2.1 Sensing Light
  • Device sense and produce different types of
    electromagnetic radio wave, X-ray, microwaves,
    etc.
  • Human Eye 400 nm(violet) 800 nm (red)
  • Snakes and CCD sensers longer then 800 nm
    (infrared)
  • Device to detect very short length X-ray
  • Device to detect very long radio waves
  • Different Wave lengths of radiation have
    Different Properties
  • X-ray Penetrate human bone
  • Infrared Not penetrate even clouds

4
2.1 Sensing Light
  • Simple model of common photography

(Sun or Flash bulb)
Illuminated by single source
Sense it via chemical on film
Reflects radiation Toward camera
  • Wavelengths in the light range result very near
    the surface objects

5
Contents
  • 2.1 Sensing Light
  • 2.2 Image Device
  • 2.2.1 CCD Cameras
  • 2.2.2 Image Formation
  • 2.2.3 Video Cameras
  • 2.2.3 The Human Eye
  • 2.3 Problems in Digital Images
  • 2.4 Picture Function And Digital Image
  • 2.5 Digital Image Formats
  • 2.6 Richness and Problems of Real Imagery
  • 2.7 3D Structure form 2D Image
  • 2.8 Five Frames of Reference
  • 2.9 Other Types of Sensors

6
CCD Cameras
  • Most flexible and common input device for
    machine-vision systems
  • Much like a 35mm file camera Commonly used
    family photo, image plane
  • Tiny solid state cells convert energy into
    electrical charge
  • All cells fist cleared 0, and integrate their
    response
  • Image Plane acts as digital memory read row by
    row by Input Process
  • If 500(rows) X 500(cols) / byte sized Gray value
    ¼ of million bytes obtained
  • Frame grabber contain memory for the image size
    and control camera

7
CCD Cameras
  • Frame Buffer
  • Centrol role of Image processong
  • High speed image store available Actually Store
    several Images or their derivatives
  • Digital Image refer to pixel values as Ir,c
  • I array name
  • R row
  • C Colume

8
2.2.2 Image Formation
  • Conceptualized as projection of each point
    through center of projection or lens center on
    image plane
  • Actual lenses compound with two refracting
    surface
  • Two effects cause blurring of image and limit
    sharpness and the size of the smallest scene
    details that sensed
  • Light collector
  • ? Circle of Confusion Because Different
    Banding of different color of lights the cone of
    rays actually results in a finite or blurred spot
  • CCD sensor array is discrete units each sensor
    cell integrates the rays received neighboring
    point

9
2.2.2 Image Formation
  • Arrangement of CCD sensor cell
  • Linear
  • Only need to measure width and where imaging and
    inspecting continuous web
  • Flatbed scanner
  • Use Cylindrical lens commonly used
  • Circular
  • Analog dial watches or speedometers
  • Scanned image of needle
  • ROSA
  • Provides hardware solution integrated all the
    light energy
  • Quantizing the power of spectrum image
  • Chip manufacturing technology

10
2.2.3 Video Camera
  • Record sequence of images at a rate of 30 /sec
  • To provide smooth human perception, 60 half
    frames /sec (interlacing)
  • CCD camera technology for machine vision
    suffered from display standard
  • Interlacing of odd and even frames need to give
    smooth picture
  • 4 3 size ratio

11
The Human Eye
  • Spherical camera with 20mm focal length lens
    focusing the image on the retina
  • Iris controls amount of light passing through the
    lens by the size of pupil
  • Has one hundred million receptor cells
  • Fovea has dense concentration of color receptors,
    called cones
  • Away from center, density cones decrease while
    density of black and white receptor, called rod,
    increases
  • Three different type of cone is sensitive
    different wavelengths of light
  • Ability to smooth perceive a seamless and stables
    3D world
  • Saccades of the eyes are necessary for proper
    human visual perception

12
Contents
  • 2.6 Richness and Problems of Real Imagery
  • 2.7 3D Structure form 2D Image
  • 2.8 Five Frames of Reference
  • 2.9 Other Types of Sensors
  • 2.1 Sensing Light
  • 2.2 Image Device
  • 2.3 Problems in Digital Images
  • 2.4 Picture Function And Digital Image
  • 2.5 Digital Image
  • Formats

13
2.3.1/5 Geometric Distortion and Clipping or
Warp-Around
  • The lens is imperfect the beams are not bent
    exactly as intended
  • Barrel distortion for small focal length lenses
    straight line s at the periphery of the scene
    appear to bow away
  • Barrel distortion is often observed when short
    focal length lenses are used
  • Dark grid intersections at left were actually
    brightest of scene.
  • In A/D conversion the bright values were clipped
    to lower values.

14
2.3.3 Blooming
  • Because Discrete detectors, such as CCD cells are
    not perfectly insulated from other
  • blooming leakage spreads out from a very bright
    region on the image plane, resulting in a bright
    flower in image

15
2.3.2/4 Scattering and CCD Variations
  • Scattering
  • Beam of radiation bent and dispersed by the
    medium through
  • Aerial and satellite images are particularly
    susceptible to such effect
  • Caused by water vapor temperature gradients
  • CCD Variations
  • Imperfect manufacturing
  • Variations in the responses of the different
    cells to identical light density
  • Precise interpretation of intensity
  • I2r,c Sr,cI1r,c Tr,c
  • Have some dead cells
  • Software remedy assign average response of the
    neighbors

16
2.3.67 Chromatic Distortion and Quantization
Effects
  • Chromatic Distortion
  • Different wavelengths of light are vent
    differently by the lens
  • Scene spot may actually image a few pixels apart
    on the detector
  • Example Very sharp black white boundary
    periphery of scene in ramp of intensity change
  • Quantization Effects
  • The digitization process collects a sample of
    intensity from discrete area maps it to one of
    the discrete set of gray value
  • Susceptible mixing and rounding problems

17
Contents
  • 2.6 Richness and Problems of Real Imagery
  • 2.7 3D Structure form 2D Image
  • 2.8 Five Frames of Reference
  • 2.9 Other Types of Sensors
  • 2.1 Sensing Light
  • 2.2 Image Device
  • 2.3 Problems in Digital Images
  • 2.4 Picture Function And Digital Image
  • 2.4.1 Type of Images
  • 2.4.2 Image Quantization and Spatial Measurement
  • 2.5 Digital Image
  • Formats

18
2.4.1 Types of Images
  • Convenient concepts of analog image and digital
    images
  • Digital image 2D rectangular array of discrete
    values
  • Image space and intensity range are quantized
    into a discrete set of values
  • Permitting the image to be stored in 2D computer
    memory structure
  • Common record intensity 8bit(0255)
  • 2 analog image F(x,y) which has infinite
    precision in spatial parameters x and y and
    infinite precision in intensity at each spatial
    point (x, y)
  • 3 digital image Ir,c represented by a
    discrete 2D array of intensity samples, each of
    which is represented a limited precision

19
2.4.1 Types of Images
  • Mathematical model of an image as a function of
    two real spatial parameters is enormously useful

20
2.4.1 Types of Images
  • 4 picture function f(x,y) of a picture as
    a function of two spatial variables x, and y and
    x and y are real values defining points of
    picture and f(x, y) is usually also real value
  • 5 gray scale image Monochrome digital
    image Ir,c with one intensity value has 3
    elements
  • 6 multispectral image 2D image Mx,y has
    a vector of values at each spatial point or pixel
    (If image is color, vector has 3 elements)
  • 7 binary image digital image with all
    pixel values 0 or 1
  • 8 labeled image digital image Lr,c
    whose pixel values are symbols from finite
    alphabet (Related concepts thematic image and
    pseudo-colored image)

21
2.4.2 Image Quantization and Spatial Measurement
  • 9 nominal resolution of the CCD sensor
    is the scene element that images to a single
    pixel on the image plan
  • 10 resolution refer to the sensor in
    making measurements subpixel resolution
    Precision of measurement is a fraction of the
    nominal resolution
  • 11 def field of view of a sensor (FOV) is the
    size of the scene can sense (10 inches X 10
    inches),Angular field of view (55 degrees by 40
    degrees)

22
2.5.11 MPEG format for video
  • Use appropriate resolution
  • Too little produce poor recognition
  • Too much slow down algorithm and waste memory

23
2.4.2 Image Quantization and Spatial Measurement
  • Spatial quantization effects impose limits on
    measurement accuracy and detectability

24
2.4.2 Image Quantization and Spatial Measurement
  • Expect error as bad as 0.5 pixels in the
    placement of a boundary due to rounding of a
    mixed pixel when a binary image
  • If we expect to detect certain features in a
    binary image, then we must make sure that their
    image size is at list two pixels in diameter
    this include gaps between objects
  • 12 mixed pixel image pixel whose
    intensity represents a sample from a mixture of
    material types in the real world

25
Contents
  • 2.6 Richness and Problems of Real Imagery
  • 2.7 3D Structure form 2D Image
  • 2.8 Five Frames of Reference
  • 2.9 Other Types of Sensors
  • 2.1 Sensing Light
  • 2.2 Image Device
  • 2.3 Problems in Digital Images
  • 2.4 Picture Function And Digital Image
  • 2.5 Digital Image Formats

26
2.5 Digital Image Formats
  • Dozen of different formats still in use
  • Row data encode image pixels in row-by-row
    (raster order)
  • Most recently developed standard formats contain
    a header with mom-image information necessary to
    label the data to decode it

27
2.5.1/2 Image File header Data
  • Image File header
  • Need to make an image file self-describing so
    that image-processing tools can work with them
  • Should contain
  • image dimention, type, data , title
  • Color table, coding table
  • Nice feature not often available
  • History section
  • Image Data
  • Handle only limited types if images binary,
    monochrome, today grow include more image type
    and features
  • Pixel size, image size limits between file
    formats
  • Multimedia format evolving image data along with
    text, graphics, music, etc.

28
2.5.3 Data Compression
  • Reduce the size of an image (30 percent or even
    3 percent of raw size)
  • Copression can be lossless and lossy
  • Lossless compression original image recovered
    exactly
  • Lossy compression loss of quality is perceived
    (but, not always)
  • To implement compression
  • Include overhead (compression method and
    parameter)
  • Loss or Change of a few bit little or no affect
    on
  • ( exciting area from signal processing to object
    recognition)
  • 13 Lossless decompression methods exists
    to original image, otherwise Lossy

29
2.5.4 Commonly Used Formats
  • Colleague or Image data base GIF, JPG, PS
  • Scanned and Original Data format GIF, TIFF
  • Image/Graphics file formats are still evolving

30
2.5.5 Run-Coded Binary Images
  • Efficient for binary or labeled images
  • Reduce memory space
  • Speed up image operations

31
2.5.6 PGM Portable Gray Map
  • Simplest file format
  • PBM or PBM (Portable Bit Map)
  • family format PBM/PGM,PPM
  • Image header encoded ASCII
  • Magic Value P2
  • ( P2 - gray level,
  • P3 - (R,G,B)
  • P4 -P4 binary )
  • Rows 8
  • Cols 16
  • Maximum gray value 192

32
2.5.7-8 GIF/TIFF Image File Format
  • Tag Image File Format (TIFF/TIF)
  • Originated by Aldus Corp.
  • Very general and complex
  • Used all popular platforms and scanner
  • Support multiple images
  • ( 1 24 bit / pixel)
  • Option available
  • (lossy / lossless)
  • Graphics Interchange Format(GIF)
  • oriented from CompuServ, Inc
  • Used on WWW or current DBs
  • Only 256 color value available
  • Typically sufficient
  • Cannot be used high precision color
  • More compact 16-color option
  • (LZW nonlossy compression)

33
2.5.9 JPEG Image File Format
  • JPEG (Joint Photographic Experts Group)
  • Provide practical compression of high-quality
    color
  • Stream oriented and allow realtime hardware for
    encoding and decoding
  • Up to 64K X 64K pixels of 24 bits
  • Header contain thumbnail image (up to 64k)
  • Achieve high compression, flexible
  • but lossy coding scheme Unnoticeable
    degration(1/20)
  • Compression work well when has large constant
    regions
  • High frequency variation not important
  • Compression scheme DCT(Discrete Cosine
    Transform) followed by Huffman coding

34
2.5.10 PostScript
  • Family of formats BDC/PDL/EPS
  • Using Printable ASCII
  • Commonly used to contain graphics or images
    inserted into large document
  • PDL page description language
  • EPS encapsulated postscript
  • Pixel value encoded via 7 bit ASCII ( changed by
    Text Editor)
  • 753000 dots / inch grayscale or color
  • PDL header contain boundary box of image

35
2.5.11 MPEG format for video
  • Stream-oriented encoding scheme for video, text,
    and graphics
  • MPEG stands for Motion Picture Experts Group
  • MPEG-1
  • Primary design for multimedia systems
  • Data rate
  • Compression audio 0.25 Mbits/s
  • Compression video 1.25 Mbits/s
  • MPEG-2
  • Data rate up to 15Mbits/s
  • Handle high definition TV rates
  • Compression scheme takes advantage of both
    spatial redundancy (used in JPEG) and temporal
    redundancy (general 1/25, 1/200 possible)
  • Motion JPEG compression is not good

36
2.5.12 Comparison of Formats
  • Cars TIF file output from the scanner 509,253
    bytes
  • Final TIF file had fewer bits in color code
    171,430
  • Lossy JPEG clear winner in terms of space (but,
    cost of decoding complexity)

37
Contents
  • 2.6 Richness and Problems of Real Imagery
  • 2.7 3D Structure form 2D Image
  • 2.8 Five Frames of Reference
  • 2.9 Other Types of Sensors
  • 2.1 Sensing Light
  • 2.2 Image Device
  • 2.3 Problems in Digital Images
  • 2.4 Picture Function And Digital Image
  • 2.5 Digital Image Formats

38
2.6 Richness and Problems of real imagery
  • Applications of automation solving problem
  • Bruises in dark red cherries Irrelevant
    bandwidths of light filtered out
  • Moving Object Strobe light for very short
    period time
  • Turbine blades Structured light (red and green
    stripe) can make suface mesurement and inspection
    much easier
  • The richness enhances human experience but causes
    problems for machine vision
  • Intensity and color depends on complex way
  • For example
  • shiny surfaces
  • Shadows
  • mutual reflection
  • transparent materials
  • For recognition of many surfaces or objects,
    color little important relative to shape or
    texture
  • Reflection controlled monochrome laser light, but
    be dominated by secondary reflections

39
Contents
  • 2.6 Richness and Problems of Real Imagery
  • 2.7 3D Structure form 2D Image
  • 2.8 Five Frames of Reference
  • 2.9 Other Types of Sensors
  • 2.1 Sensing Light
  • 2.2 Image Device
  • 2.3 Problems in Digital Images
  • 2.4 Picture Function And Digital Image
  • 2.5 Digital Image Formats

40
2.7 3D Structure from 2D Images
  • Images processing records complex relationships
    between 3D and 2D structure of the image
  • Interposition
  • Most important in depth cues
  • Relative size
  • Vanish point rail road
  • Foreshortening Opened door image as trapeziodal
  • Texture gradient
  • Close can see detail of blade of grass
  • Far away only green color

41
Contents
  • 2.6 Richness and Problems of Real Imagery
  • 2.7 3D Structure form 2D Image
  • 2.8 Five Frames of Reference
  • 2.8.1 Pixel Coordinate Frame
  • 2.8.2 Object Coordinate Frame
  • 2.8.3 Camera Coordinate Frame
  • 2.8.4 Real Image Coordinate Frame
  • 2.8.5 World Coordinate Frame
  • 2.9 Other Types of Sensors
  • 2.1 Sensing Light
  • 2.2 Image Device
  • 2.3 Problems in Digital Images
  • 2.4 Picture Function And Digital Image
  • 2.5 Digital Image Formats

42
2.8 Five frames of reference
  • Needed in order to either qualitative or
    quantitative analysis of 3D scenes

43
2.8 Five frames of reference
  • Pixel Coordinate Frame
  • Each point has integer pixel coordinates
  • A falls within pixel aar,ac
  • ar and ac are ingefer row and column
  • Using only image I, cannot determine which object
    is actually larger in 3D whether or not objects
    are on a collision course

44
2.8 Five frames of reference
  • Object Coordinate Frame O
  • Used to model ideal objects in both computer
    praphics and computer vision
  • Two object frame
  • Block Ob
  • Pyramid Op
  • Coodinate 3D corner point B relative to the
    object coordinate frame xb, 0, zb
  • Regardless of how block is posed related to world
  • Camera Coordinate Frame C
  • Often needed for egocentric (camera centric) view
  • Represent just in front of the sensor

45
2.8 Five frames of reference
  • Real Image Cordinate Frame F
  • Coordinate xf, yf, f
  • F focal length
  • xf, yf not description of pixels in the image
    array but related to the pixel size and pixel
    position of optical axis in the image
  • Frame F contians the picture function digital
    image in the pixel array I
  • World Coordinate Frame W
  • Needed to relate objects in 3D

46
Contents
  • 2.6 Richness and Problems of Real Imagery
  • 2.7 3D Structure form 2D Image
  • 2.8 Five Frames of Reference
  • 2.9 Other Types of Sensors
  • 2.1 Sensing Light
  • 2.2 Image Device
  • 2.3 Problems in Digital Images
  • 2.4 Picture Function And Digital Image
  • 2.5 Digital Image Formats

47
2.9.1 Microdensitometer
  • Slides or film can he scanned by passing a single
    beam of light through the material
  • Pros
  • Less variation in intensity value
  • Many more rows and cols can be obtained
  • Cons
  • Slow

48
2.9.2 Color and Multispectral images
  • The refract film disperse a single beam into 4
    beam falling
  • Gain traded for a loss in spatial resolution
  • In rotation wheel design, sensing speed traded
    for color sensitivity
  • Multispectral Satelite Scanner
  • View earth 1 pixel at a time (through a straw)
  • Prism produces multispectral pixel
  • Image row by scanning boresight
  • All rows by motion of satellite in orbit
  • Spectrum of intensity value possible classify the
    ground type

49
2.9.3 X-ray
  • Record transmit energy at image points in the far
    side of the emitter in same manner as
    mircodensitometer
  • CT scanner 3D sensing accomplished

50
2.9.4 Magnetic Resonance Imaging(MRI)
  • Produce 3D images if materials
  • Is , r ,c
  • s slice through the body
  • r, c row, col
  • Volume element (voxel) about 2mm per side
  • Intensity measure chemistry of material
  • Magnetic resonance angiography (MRA) produce
    intensity related speed of material at the voxel
  • Digital image extracted from 3D MRA data
  • Maximum intensity projection, or MIPr,c
    choosing the brightest voxel Is,r,c

51
2.9.5 Range Scanners and Range Images
  • LIDAR
  • Measure distance by comparing the change of phase
    (delay)
  • Estimate the reflectivity of the surface spot
  • Produce two registered images
  • Range image
  • Intensity image
  • Slow and expensive

52
2.9.5 Range Scanners and Range Images
  • Triangulation
  • Bright image point xc, yc corresponding 3D
    point xw, yw, zw
  • 3 form of light sheet and 2 equations in 3
    unknowns from the imaging ray
  • Solving linear equations yields the location of
    3D surface point
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