cs443: digital imaging and multimedia point operations on digital images spring 2008 ahmed elgammal...
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![Page 1: CS443: Digital Imaging and Multimedia Point Operations on Digital Images Spring 2008 Ahmed Elgammal Dept. of Computer Science Rutgers University Spring](https://reader038.vdocuments.site/reader038/viewer/2022110207/56649d365503460f94a0ef92/html5/thumbnails/1.jpg)
CS443: Digital Imaging and MultimediaPoint Operations on Digital Images
CS443: Digital Imaging and MultimediaPoint Operations on Digital Images
Spring 2008
Ahmed Elgammal
Dept. of Computer Science
Rutgers University
Spring 2008
Ahmed Elgammal
Dept. of Computer Science
Rutgers University
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OutlinesOutlines
Point Operations Brightness and contrast adjustment Auto contrast Histogram equalization Histogram specifiation
Point Operations Brightness and contrast adjustment Auto contrast Histogram equalization Histogram specifiation
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Point OperationsPoint Operations
Point Operations perform a mapping of the pixel values without changing the size, geometry, or local structure of the image
Each new pixel value I’(u,v) depends on the previous value I(u,v) at the same position and on a mapping function f()
The function f() is independent of the coordinates Such operation is called “homogeneous”
Point Operations perform a mapping of the pixel values without changing the size, geometry, or local structure of the image
Each new pixel value I’(u,v) depends on the previous value I(u,v) at the same position and on a mapping function f()
The function f() is independent of the coordinates Such operation is called “homogeneous”
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Example of homogeneous point operations: Modifying image brightness or contrast Applying arbitrary intensity transformation
(curves) Quantizing (posterizing) images Global thresholding Gamma correction Color transformations
Example of homogeneous point operations: Modifying image brightness or contrast Applying arbitrary intensity transformation
(curves) Quantizing (posterizing) images Global thresholding Gamma correction Color transformations
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A nonhomogeneous point operation g() would also take into account the current image coordinate (u,v)
A nonhomogeneous point operation g() would also take into account the current image coordinate (u,v)
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Changing contrast and brightness
Limiting Results by Clamping
Changing contrast and brightness
Limiting Results by Clamping
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Inverting Images Inverting Images
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Threshold OperationThreshold Operation
Thresholding an image is a special type of quantization that separates the pixel values in two classes, depending on a given threshold value ath
The threshold function maps all the pixels to one of two fixed intensity values ao,a1
Example: binarization: ao=0,a1=1
Thresholding an image is a special type of quantization that separates the pixel values in two classes, depending on a given threshold value ath
The threshold function maps all the pixels to one of two fixed intensity values ao,a1
Example: binarization: ao=0,a1=1
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Point Operations and HistogramsPoint Operations and Histograms
The effect of some point operations on histograms are easy to predict: ex: increasing the brightness, raising the contrast, inverting an image
Point operations can only shift and merge histogram entries
Operations that result in merging histogram bins are irreversible
The effect of some point operations on histograms are easy to predict: ex: increasing the brightness, raising the contrast, inverting an image
Point operations can only shift and merge histogram entries
Operations that result in merging histogram bins are irreversible
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Automatic Contrast AdjustmentAutomatic Contrast Adjustment
Auto-contrast: a point operation that modifies the pixels such that the available range of values is fully covered.
Linear stretching of the intensity range - can result in gaps in the new histogram
Auto-contrast: a point operation that modifies the pixels such that the available range of values is fully covered.
Linear stretching of the intensity range - can result in gaps in the new histogram
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Better Auto-contrastBetter Auto-contrast
It’s better to map only a certain range of the values and get rid of the tails (usually noise) based on predefined percentiles (slow, shigh)
It’s better to map only a certain range of the values and get rid of the tails (usually noise) based on predefined percentiles (slow, shigh)
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Histogram EqualizationHistogram Equalization
Adjust two different images in such a way that their resulting intensity distribution are similar
Useful when comparing images to get rid of illumination variations
The goal is to find and apply a point operation such that the histogram of the modified image approximates a uniform distribution.
Adjust two different images in such a way that their resulting intensity distribution are similar
Useful when comparing images to get rid of illumination variations
The goal is to find and apply a point operation such that the histogram of the modified image approximates a uniform distribution.
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Linear Histogram equalization
Linear Histogram equalization
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Histogram SpecificationHistogram Specification
Real images never show uniform distribution In most real images the distribution of pixel values
is more similar to a Gaussian Distribution Histogram specification modifies the image to
match an arbitrary intensity distribution, including the histogram of a given image.
Also depends on the alignment of the cumulative histograms by applying a homogeneous point operation.
Real images never show uniform distribution In most real images the distribution of pixel values
is more similar to a Gaussian Distribution Histogram specification modifies the image to
match an arbitrary intensity distribution, including the histogram of a given image.
Also depends on the alignment of the cumulative histograms by applying a homogeneous point operation.
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Histogram SpecificationHistogram Specification
Find a mapping such that Find a mapping such that
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Adjusting piecewise linear distributionAdjusting piecewise linear distribution
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Adjusting to a given histogramAdjusting to a given histogram
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Gamma CorrectionGamma Correction
What is the relation between the amount of light falling onto a sensor and the “intensity” or “brightness” measured at the corresponding pixel.
What is the relation between the intensity of a pixel and the actual light emanating from that pixel on the display?
The relation between a pixel value and the corresponding physical quantity is usually complex and nonlinear.
Approximation ?
What is the relation between the amount of light falling onto a sensor and the “intensity” or “brightness” measured at the corresponding pixel.
What is the relation between the intensity of a pixel and the actual light emanating from that pixel on the display?
The relation between a pixel value and the corresponding physical quantity is usually complex and nonlinear.
Approximation ?
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What is Gamma?What is Gamma?
Originates from analog photography
Exposure function: the relationship between the logarithmic light intensity and the resulting film density.
Gamma is the slope of the linear range of the curve.
The same in TV broadcasting
Originates from analog photography
Exposure function: the relationship between the logarithmic light intensity and the resulting film density.
Gamma is the slope of the linear range of the curve.
The same in TV broadcasting
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The Gamma functionThe Gamma function
Gamma function is a good approximation for the exposure curve.
The inverse of a Gamma function is another gamma function with
Gamma of CRT and LCD monitors: 1.8-2.8 (typically 2.4)
Gamma function is a good approximation for the exposure curve.
The inverse of a Gamma function is another gamma function with
Gamma of CRT and LCD monitors: 1.8-2.8 (typically 2.4)
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Gamma CorrectionGamma Correction
Obtain a measurement b proportional to the original light intensity B by applying the inverse gamma function
This is important to achieve a device independent representation
Obtain a measurement b proportional to the original light intensity B by applying the inverse gamma function
This is important to achieve a device independent representation
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Gamma CorrectionGamma Correction