Chapter 02
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Transcript of Chapter 02
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Digital Image Processing, 3rd ed.Digital Image Processing, 3rd ed.
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19922008 R. C. Gonzalez & R. E. Woods
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Chapter 2 Digital Image FundamentalsChapter 2 Digital Image Fundamentals
Element of Visual Perception
Structure of the human eye (~20 mm)3 membranes ():
Sclera () Choroid () Retina()2 chambers ():
Anterior chamber()Posterior chamber()Six Ciliary, body, fiber and muscle (
)Control the lens and protect the
human eye ()
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Digital Image Processing, 3rd ed.Digital Image Processing, 3rd ed.
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19922008 R. C. Gonzalez & R. E. Woods
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Chapter 2 Digital Image FundamentalsChapter 2 Digital Image Fundamentals
Receiver of Retina ()Cone ( ): 6~7 million
Photopic vision, Bright-light visionRod ( ): 75~150 million :
Scotopic vision, Dim-light vision
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Digital Image Processing, 3rd ed.Digital Image Processing, 3rd ed.
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19922008 R. C. Gonzalez & R. E. Woods
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Chapter 2 Digital Image FundamentalsChapter 2 Digital Image Fundamentals
Lens shape controlled by the tension of ciliary fiberFocal length: 17 mm ~ 14mm
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Digital Image Processing, 3rd ed.Digital Image Processing, 3rd ed.
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19922008 R. C. Gonzalez & R. E. Woods
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Chapter 2 Digital Image FundamentalsChapter 2 Digital Image Fundamentals
Range of light intensity that humancan adapt
1010 Transition from scotopic to photopic
vision is gradualFrom 0.001 to 0.1 millilambert
(-3 to -1 in log) Vision system cannot operate over
such a range simultaneously Brightness adaptation level: current
sensitivity level of the visual system
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Digital Image Processing, 3rd ed.Digital Image Processing, 3rd ed.
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19922008 R. C. Gonzalez & R. E. Woods
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Chapter 2 Digital Image FundamentalsChapter 2 Digital Image Fundamentals
Uniformly illuminated background, occupying entire field of view. Add a short duration of flashI. I c: the increment of illumination discriminable 50% of the time
with background illumination I. I c/ I : Weber ratio
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Digital Image Processing, 3rd ed.Digital Image Processing, 3rd ed.
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19922008 R. C. Gonzalez & R. E. Woods
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Chapter 2 Digital Image FundamentalsChapter 2 Digital Image Fundamentals
Rod vision (photopic vision): Weber ration largerbrightness discrimination poor
Cone vision (scotopic vision): Weber ration smallerbrightness discrimination better
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Digital Image Processing, 3rd ed.Digital Image Processing, 3rd ed.
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19922008 R. C. Gonzalez & R. E. Woods
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Chapter 2 Digital Image FundamentalsChapter 2 Digital Image Fundamentals
Human Vision Phenomenon: Mach Band
Ernst Mach first describedthe phenomenon in 1865.
The visual system tends to undershootor overshoot around the boundary ofregions of different intensities.
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Digital Image Processing, 3rd ed.Digital Image Processing, 3rd ed.
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19922008 R. C. Gonzalez & R. E. Woods
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Chapter 2 Digital Image FundamentalsChapter 2 Digital Image Fundamentals
Human Vision Phenomenon: Simultaneous Contrast
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Digital Image Processing, 3rd ed.Digital Image Processing, 3rd ed.
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19922008 R. C. Gonzalez & R. E. Woods
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Chapter 2 Digital Image FundamentalsChapter 2 Digital Image Fundamentals
Human Vision Phenomenon: Optical Illusion
The outline of a squareis seen clearly.
A few lines are sufficient togive the illusion of a circle.
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Digital Image Processing, 3rd ed.Digital Image Processing, 3rd ed.
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19922008 R. C. Gonzalez & R. E. Woods
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Chapter 2 Digital Image FundamentalsChapter 2 Digital Image Fundamentals
Light and EM Spectrum
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Digital Image Processing, 3rd ed.Digital Image Processing, 3rd ed.
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19922008 R. C. Gonzalez & R. E. Woods
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Chapter 2 Digital Image FundamentalsChapter 2 Digital Image Fundamentals
Visible band: 0.43 um (violet)~0.79 um (red)
Achromatic or monochromatic lightGray level, intensity Radiance (watts): total energy from light source Luminance (lumens): energy that an observer perceives Brightness : subjective descriptor of light perception
hard to measureEx. The light emitted from a source operating in the far infrared region of
the spectrum could have significant energy, but an observer wouldhardly perceived it.
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Digital Image Processing, 3rd ed.Digital Image Processing, 3rd ed.
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19922008 R. C. Gonzalez & R. E. Woods
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Chapter 2 Digital Image FundamentalsChapter 2 Digital Image Fundamentals
Image sensing and acquisition
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Digital Image Processing, 3rd ed.Digital Image Processing, 3rd ed.
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19922008 R. C. Gonzalez & R. E. Woods
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Chapter 2 Digital Image FundamentalsChapter 2 Digital Image Fundamentals
Image acquisition using a single sensor
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Digital Image Processing, 3rd ed.Digital Image Processing, 3rd ed.
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19922008 R. C. Gonzalez & R. E. Woods
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Chapter 2 Digital Image FundamentalsChapter 2 Digital Image Fundamentals
Image acquisition using sensor strips
1. OA: scanner.2. Airborne: the imaging system is
mounted on an aircraft that flies at aconstant speed over the geographicalarea to be imaged.
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Digital Image Processing, 3rd ed.Digital Image Processing, 3rd ed.
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19922008 R. C. Gonzalez & R. E. Woods
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Chapter 2 Digital Image FundamentalsChapter 2 Digital Image Fundamentals
A simple image formation model f(x, y) = i(x, y) r(x, y) i(x, y) : the amount of illumination incident on the scene r(x, y) : the reflectivity function (or transmissivity function)
yxf ,0
yxi ,0 1,0 yxr
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Digital Image Processing, 3rd ed.Digital Image Processing, 3rd ed.
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19922008 R. C. Gonzalez & R. E. Woods
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Chapter 2 Digital Image FundamentalsChapter 2 Digital Image Fundamentals
Image Sampling and Quantization
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19922008 R. C. Gonzalez & R. E. Woods
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Chapter 2 Digital Image FundamentalsChapter 2 Digital Image Fundamentals
Sampling and Quantization with a Sensing Array
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Digital Image Processing, 3rd ed.Digital Image Processing, 3rd ed.
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19922008 R. C. Gonzalez & R. E. Woods
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Chapter 2 Digital Image FundamentalsChapter 2 Digital Image Fundamentals
Representing Digital Images
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19922008 R. C. Gonzalez & R. E. Woods
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Chapter 2 Digital Image FundamentalsChapter 2 Digital Image Fundamentals
Representing Digital Images
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Digital Image Processing, 3rd ed.Digital Image Processing, 3rd ed.
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19922008 R. C. Gonzalez & R. E. Woods
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Chapter 2 Digital Image FundamentalsChapter 2 Digital Image Fundamentals
Representing Digital Images maxmin , LLDynamic range/ Image contrast
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19922008 R. C. Gonzalez & R. E. Woods
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Chapter 2 Digital Image FundamentalsChapter 2 Digital Image Fundamentals
Image SizeThe number of gray levels: L = 2k
The number of bits required to store a digitalized image:b = M * N * kWhen M=N:
b = N2 * k
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Digital Image Processing, 3rd ed.Digital Image Processing, 3rd ed.
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19922008 R. C. Gonzalez & R. E. Woods
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Chapter 2 Digital Image FundamentalsChapter 2 Digital Image Fundamentals
Spatial and Gray-Level Resolution
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Digital Image Processing, 3rd ed.Digital Image Processing, 3rd ed.
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19922008 R. C. Gonzalez & R. E. Woods
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Chapter 2 Digital Image FundamentalsChapter 2 Digital Image Fundamentals
Spatial and Gray-Level Resolution
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Digital Image Processing, 3rd ed.Digital Image Processing, 3rd ed.
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19922008 R. C. Gonzalez & R. E. Woods
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Chapter 2 Digital Image FundamentalsChapter 2 Digital Image Fundamentals
Zoom in to show the effect of subsampling
Blocking effect
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19922008 R. C. Gonzalez & R. E. Woods
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Chapter 2 Digital Image FundamentalsChapter 2 Digital Image Fundamentals
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Digital Image Processing, 3rd ed.Digital Image Processing, 3rd ed.
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19922008 R. C. Gonzalez & R. E. Woods
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Chapter 2 Digital Image FundamentalsChapter 2 Digital Image Fundamentals
False contouring
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Digital Image Processing, 3rd ed.Digital Image Processing, 3rd ed.
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19922008 R. C. Gonzalez & R. E. Woods
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Chapter 2 Digital Image FundamentalsChapter 2 Digital Image Fundamentals
Detail increased
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19922008 R. C. Gonzalez & R. E. Woods
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Chapter 2 Digital Image FundamentalsChapter 2 Digital Image Fundamentals
Isopreference curves tend to become more vertical in theNk-plane as the detail in the image increases.For images with a large amount of detail only a few gray
levels may be needed.
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19922008 R. C. Gonzalez & R. E. Woods
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Chapter 2 Digital Image FundamentalsChapter 2 Digital Image Fundamentals
Aliasing and Moire Pattern
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Digital Image Processing, 3rd ed.Digital Image Processing, 3rd ed.
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19922008 R. C. Gonzalez & R. E. Woods
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Chapter 2 Digital Image FundamentalsChapter 2 Digital Image Fundamentals
A moir pattern formed by incorrectly downsamplingthe image left.
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Digital Image Processing, 3rd ed.Digital Image Processing, 3rd ed.
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19922008 R. C. Gonzalez & R. E. Woods
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Chapter 2 Digital Image FundamentalsChapter 2 Digital Image Fundamentals
Zooming and Shrinking Digital Images
Zooming: oversamplingNeed interpolationSuperresolutionShrinking: undersampling
Better to apply LPF (blurring of digital image) beforesubssampling to avoid aliasingNeed interpolation for non-interger factor
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Digital Image Processing, 3rd ed.Digital Image Processing, 3rd ed.
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19922008 R. C. Gonzalez & R. E. Woods
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Chapter 2 Digital Image FundamentalsChapter 2 Digital Image Fundamentals
Image Interpolation Nearest Neighbor Interpolation Bilinear Interpolation Bicubic Interpolation etc
Application Image Scaling/Resize Image Rotation Image Warping Image Morphing etc
Image Morphing
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19922008 R. C. Gonzalez & R. E. Woods
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Chapter 2 Digital Image FundamentalsChapter 2 Digital Image Fundamentals
Nearest-neighborInterpolation
BilinearInterpolation
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19922008 R. C. Gonzalez & R. E. Woods
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Chapter 2 Digital Image FundamentalsChapter 2 Digital Image Fundamentals
Neighbors of a pixel
A pixel p at coordinate (x, y)N4(p): 4-neighbors of p
(x+1, y), (x-1, y), (x, y+1), (x, y-1)ND(p): 4 diagonal neighbors of p
(x+1, y+1), (x+1, y-1), (x-1, y+1), (x-1, y-1)N8(p): N4(p) together with ND(p)
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Digital Image Processing, 3rd ed.Digital Image Processing, 3rd ed.
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19922008 R. C. Gonzalez & R. E. Woods
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Chapter 2 Digital Image FundamentalsChapter 2 Digital Image Fundamentals
AdjacencyLet V be the set of gray-level values used to define adjacency4- adjacency
Two pixels p and q with values from V are 4- adjacency if q is in theset N4(p).
8- adjacencyTwo pixels p and q with values from V are 8- adjacency if q is in the
set N8(p).
m- adjacency (mixed adjacency)Two pixels p and q with values from V are m- adjacency if
(i) q is in N4(p), or(ii) q is in ND(p) and the set N4(p) N4(q) has no pixels whose values
are from V.
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Digital Image Processing, 3rd ed.Digital Image Processing, 3rd ed.
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19922008 R. C. Gonzalez & R. E. Woods
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Chapter 2 Digital Image FundamentalsChapter 2 Digital Image Fundamentals
m- adjacency is a modification of 8-adjacency
V={1} Multiple 8-adjacency ambiguousAmbiguity is removed bym-adjacency
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Digital Image Processing, 3rd ed.Digital Image Processing, 3rd ed.
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19922008 R. C. Gonzalez & R. E. Woods
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Chapter 2 Digital Image FundamentalsChapter 2 Digital Image Fundamentals
A digital path from pixel p(x, y) to pixel q(s, t)
S: a subset of pixels in an imageTwo pixels p and q are said to be connected in S if there exists a path
between them consisting entirely of pixels in S.The set of pixels that are connected to it in S is called a connected
component of S.If it is only one connected component, then S is called a connected set.
(x0, y0), (x1, y1), ,(xn, yn)(x0, y0)= (x, y), (xn, yn)=(s, t)(xi, yi) and (xi+1, yi+1) are adjacentn is the length of the pathIf (x0, y0)= (xn, yn), the path is a closed path.
Connectivity
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Digital Image Processing, 3rd ed.Digital Image Processing, 3rd ed.
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19922008 R. C. Gonzalez & R. E. Woods
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Chapter 2 Digital Image FundamentalsChapter 2 Digital Image Fundamentals
Regions and BoundariesR: subset of pixels in an imageWe call R a region of image if R is a connected set. Two pixels p and
Boundary of R:The set of pixels have one or more neighbors that are not in R.
Edge : intensity discontinuities (local concept)Boundary : closed paths (global concept)
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Digital Image Processing, 3rd ed.Digital Image Processing, 3rd ed.
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19922008 R. C. Gonzalez & R. E. Woods
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Chapter 2 Digital Image FundamentalsChapter 2 Digital Image Fundamentals
For pixels p, q, and z, with coordinates (x, y), (s, t), and (v, w)D is a distance function or metric if(a) D(p, q)0(b) D(p, q)= D(q, p), and(c) D(p, z)D(p, q)+ D(q, z)
Distance Measures
Euclidean distance between p and q:
2/122, tysxqpDe
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Digital Image Processing, 3rd ed.Digital Image Processing, 3rd ed.
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19922008 R. C. Gonzalez & R. E. Woods
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Chapter 2 Digital Image FundamentalsChapter 2 Digital Image Fundamentals
D4 distance between p and q:
tysxqpD4 ,D8 distance between p and q:
tysxqpD8 ,max,
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Digital Image Processing, 3rd ed.Digital Image Processing, 3rd ed.
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19922008 R. C. Gonzalez & R. E. Woods
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Chapter 2 Digital Image FundamentalsChapter 2 Digital Image Fundamentals
Let H be an operator whose input and output are images.H is said to be a linear operator if
Linear and Nonlinear Operations
gbHfaHbgafH Where f, g are two images, a and b are two scalars