ntrast Modification in Image Data togram Modification Rule























































































































- Slides: 119
































ﻫﻴﺴﺘﻮگﺮﺍﻡ : پﺮﺩﺍﺯﺵ ﺗﺼﺎﻭﻳﺮ ﺭﻗﻮﻣﻲ ntrast Modification in Image Data togram Modification Rule x : the old brightness value of a particular bar in the histogram y : the corresponding new brightness value


ﻫﻴﺴﺘﻮگﺮﺍﻡ : پﺮﺩﺍﺯﺵ ﺗﺼﺎﻭﻳﺮ ﺭﻗﻮﻣﻲ Piecewise Linear Contrast Modification Saturating Linear Contrast Enhancement Logarithmic and Exponential Contrast Enhancement









پﺮﺩﺍﺯﺵ ﺗﺼﺎﻭﻳﺮ ﺭﻗﻮﻣﻲ Neighbourhood Operations (Template Operators)



ﺗﻤپﻠﺖ : پﺮﺩﺍﺯﺵ ﺗﺼﺎﻭﻳﺮ ﺭﻗﻮﻣﻲ Image Smoothing (Low Pass Filtering) Linear Edge Mean Value Smoothing Detecting Templates Image Smoothing Point Detection Filtering Edge Detection and Enhancement Line Detection General Convolution Filtering Detecting Geometric Properties The Roberts Median Operator Edge Detection and Enhancement The Sobel Operator Spatial Derivative Techniques The Prewitt Operator Thinning, Linking and Border Responses Edge Enhancement by Subtractive Smoothing (Sharpening)

ﺗﻤپﻠﺖ : پﺮﺩﺍﺯﺵ ﺗﺼﺎﻭﻳﺮ ﺭﻗﻮﻣﻲ Image Smoothing (Low Pass Filtering) 1 - Mean Value Smoothing Modified Mean Value Smoothing Edges and Lines be saved Thresho ld



ﺗﻤپﻠﺖ : پﺮﺩﺍﺯﺵ ﺗﺼﺎﻭﻳﺮ ﺭﻗﻮﻣﻲ Illustration of the effect of median filtering on an image which contains impulsive noise. a Original image; b Image with noise; c Filtered image



ﺗﻤپﻠﺖ : پﺮﺩﺍﺯﺵ ﺗﺼﺎﻭﻳﺮ ﺭﻗﻮﻣﻲ patial Derivative Techniques Roberts Operator Sobel Operator Response of a the Robert’s operator and b the Sobel












ﻫﻨﺪﺳﻲ : ﺧﻄﺎﻫﺎﻱ ﺗﺼﻮﻳﺮ Sources of Geometric Distortion:









ﻫﻨﺪﺳﻲ : ﺧﻄﺎﻫﺎﻱ ﺗﺼﻮﻳﺮ Image Geo. Referencing, Geo. Coding, Registration ﺗﺼﺤﻴﺢ ﺧﻄﺎﻱ ﻫﻨﺪﺳﻲ ﻓﺘﻮگﺮﺍﻣﺘﺮﻱ. 1 Colinearity • Rational Functions • ﺳﻨﺠﺶ ﺍﺯ ﺩﻭﺭ. 2 Polynomial • Special Functions • Rational o Functions Modified Colinearity o


Resampling Interpolation


Bicubic

ﻫﻨﺪﺳﻲ : ﺧﻄﺎﻫﺎﻱ ﺗﺼﻮﻳﺮ Image Registration 1. Georeferencing and Geocoding 2. Image to Image Registration

ﻫﻨﺪﺳﻲ : ﺧﻄﺎﻫﺎﻱ ﺗﺼﻮﻳﺮ Geocoding • ﺍﻣﻜﺎﻥ ﺑﻴﺎﻥ آﺪﺭﺱ ﻫﺮ پﻴﻜﺴﻞ ﺑﺎ ﻣﺨﺘﺼﺎﺕ ﺟﻐﺮﺍﻓﻴﺎﻳﻲ Georeferencing • ﺩﻳﺪﻥ ﺗﺼﻮﻳﺮ ﺑﺎﺯﻧﻤﻮﻧﻪﺑﺮﺩﺍﺭﻱ ﺷﺪﻩ ﺩﺭ گﺮﻳﺪ ﻣﺨﺘﺼﺎﺕ ﻧﻘﺸﻪ Geocoding: Expressing image pixel addresses in terms of a map coordinate Georeferencing: Geometrical error calculation/modeling followed by image resampling

ﺭﻭﺵﻫﺎﻱ ﺗﻔﺴﻴﺮ ﻋﻜﺲ Approaches to Image Interpretation ﺗﻔﺴﻴﺮ Image interpretation ﻋﻜﺴﻲ ﺗﺤﻠﻴﻞ ﻋﺪﺩﻱ Quantitative analysis













ﻛﻼﺱﺑﻨﺪﻱ : ﺭﻭﺵﻫﺎﻱ ﺗﻔﺴﻴﺮ ﻋﻜﺲ ﻣﻔﺎﻫﻴﻢ پﺎﻳﺔ ﻛﻼﺱﺑﻨﺪﻱ 1 ﺩﺭﺟﻪ ﺧﺎﻛﺴﺘﺮﻱ ﺑﺎﻧﺪ 2 ﺩﺭﺟﻪ ﺧﺎﻛﺴﺘﺮﻱ ﺑﺎﻧﺪ pixel vectors ﻳﺎ Feature vector N ﺩﺭﺟﻪ ﺧﺎﻛﺴﺘﺮﻱ ﺑﺎﻧﺪ Before that classification can be performed however m and Σ are estimated for each class from a representative set of pixels, commonly called a training set.


پﻴﺶپﺮﺩﺍﺯﺵﻫﺎ : ﻛﻼﺱﺑﻨﺪﻱ pixel vectors ﻳﺎ Feature vector ﺩﺭﺟﻪ ﺧﺎﻛﺴﺘﺮﻱ ﺑﺎﻧﺪ 1 ﺩﺭﺟﻪ ﺧﺎﻛﺴﺘﺮﻱ ﺑﺎﻧﺪ 2 ﺩﺭﺟﻪ ﺧﺎﻛﺴﺘﺮﻱ ﺑﺎﻧﺪ N Chapter 6: Multispectral Transformations of Image Data Principal Components Transformation Kauth-Thomas Tasseled Cap Transformation Image Arithmetic, Band Ratios and Vegetation Indices

پﻴﺶپﺮﺩﺍﺯﺵﻫﺎ : ﻛﻼﺱﺑﻨﺪﻱ Principal Components Transformation



Colour composites TM bands 4, 3 and 2 PC 3, PC 2 TM ﺗﺼﻮﻳﺮ ﺍﺻﻠﻲ PC 3, PC 2 and PC 1 PC 4, ﺗﺼﻮﻳﺮ ﻣﺆﻠﻔﻪﻫﺎﻱ ﺍﺻﻠﻲ

پﻴﺶپﺮﺩﺍﺯﺵﻫﺎ : ﻛﻼﺱﺑﻨﺪﻱ Kauth-Thomas Tasseled Cap Transformation Or Tasseled Cap Transformation pixel vectors



پﺮﺩﺍﺯﺵﻫﺎ : ﻛﻼﺱﺑﻨﺪﻱ Steps in Supervised Classification 1. Decide the set of ground cover types (for example: water, urban regions, croplands, rangelands, etc. ) 2. Choose prototype pixels (training data) from each of the desired classes using site visits, maps, air photographs or even photointerpretation. 3. Use the training data to estimate the parameters of the particular classifier. The set of parameters for a given class is sometimes called the signature of that class. 4. Using the trained classifier, label or classify every pixel in the image into one of the desired ground cover types (information classes). 5. Produce tabular summaries or thematic (class) maps. 6. Assess the accuracy of the final product using a labelled testing data set.

پﺮﺩﺍﺯﺵﻫﺎ : ﻛﻼﺱﺑﻨﺪﻱ Maximum Likelihood Classification Bayes’ Classification




پﺮﺩﺍﺯﺵﻫﺎ : ﻛﻼﺱﺑﻨﺪﻱ Number of Training Pixels Required for Each Class For an N dimensional multispectral space the covariance matrix is symmetric of size N × N. It has, therefore, 1/2 N(N + 1) distinct elements that need to be estimated from the training data. To avoid the matrix being singular at least N(N + 1) independent samples is needed. Fortunately, each N dimensional pixel vector in fact contains N samples (one in each waveband); thus the minimum number of independent training pixels required is (N+1). Because of the difficulty in assuring independence of the pixels, usually many more than this minimum number is selected. Swain and Davis (1978) recommend as a practical minimum that 10 N training pixels per spectral class be used, with as many as 100 N per class if possible.


پﺮﺩﺍﺯﺵﻫﺎ : ﻛﻼﺱﺑﻨﺪﻱ nimum Distance Classification


پﺮﺩﺍﺯﺵﻫﺎ : ﻛﻼﺱﺑﻨﺪﻱ rallelepiped Classification



پﺮﺩﺍﺯﺵﻫﺎ : ﻛﻼﺱﺑﻨﺪﻱ Multilayer perceptron neural network



پﺮﺩﺍﺯﺵﻫﺎ : ﻛﻼﺱﺑﻨﺪﻱ ergings and Deletions tting Elongated Clusters
Ntrast
Magic trig triangles
Sine rule and cosine rule formula
Soh cah toa rules
Kirchhoffs junction rule
Sum rule product rule
Home rule vs dillon's rule
Kirchhoff's loop law equation
With the rule astigmatism
With the-rule astigmatism example
Astigmatism classification
Product rule vs chain rule
General power rule vs power rule
Sine and cosine rule
Chain rule power rule
Leptokurtotic
Does the mirror image rule apply to ucc
Real image vs virtual image
Real image vs virtual
Translate
What is image restoration in digital image processing
Spatial and temporal redundancy in digital image processing
Image segmentation in digital image processing
Analog image and digital image
Lossless image compression matlab source code
Image sharpening in digital image processing
Static image vs dynamic image
Image geometry in digital image processing
Area of convergence
Image formation model in digital image processing ppt
Ce n'est pas une image juste c'est juste une image
Difference between logical file and physical file
Perturbação
Contra harmonic mean filter
Image transforms in digital image processing
Image geometry in digital image processing
Noise
Xuite blog
Generative adversarial networks
Tense lax vowel chart
Modification of instinct
Stuttering modification techniques
Effect modification vs confounding
Stately motility seen in
Functional chew in technique
Interruption in network security
Foodafactoflife costing a recipe
Pre and post modification
Stem and root modifications
Bioluminescent genetic modification
Research on the pros and cons of genetic engineering.
Effect modification vs confounding
Confounding vs effect modification
Effect modification vs confounding
Confounding vs effect modification
Class 2 modification 1 rpd design
Class 6 cavity
Effect modification epidemiology
Modification anomalies
Descent with modification: a darwinian view of life
Chapter 22: descent with modification
Ch 22 descent with modification
Chapter 22 descent with modification
Is selective breeding biotechnology
Amos報表解讀
Modification of air masses
Effect modifier vs confounder
C clasp wire gauge
Accommodations vs modifications chart
Accommodation vs modification
Accommodation vs modification
Structure of modification
Genome modification ustaz auni
Ameoba
Forward caries and backward caries
Hand over mouth exercise
Areas of application of behavior modification
Dances that show imagery combat
Modification in grammar
Self management dalam modifikasi perilaku
Supportive techniques in social case work
Descent with modification definition
La county fuel modification
Classe 5 kennedy applegate
Carolus linnaeus
Namei algorithm
External parts of a leaf
Deferred database modification example
Class 2 mod 1 rpd design maxillary
Is this true
Histone modification epigenetics
Bonwill clasp
Modification of leaves
Morphology of inflorescence
Behavior modification therapy
Grass stem modification
Sistema pat
Interception attack
Aerial stem modification examples
Behaviour modification
Prosthetic dentistry meaning
Trikle down
Behaviour modification
Committed step meaning
Descent with modification
Bill gates weather modification
Stuttering modification
Post translation modification
Evolution descent with modification
Ontogeny recapitulates phylogeny
Cpu modification
Chapter 22 descent with modification
Decent with modification
Big data image processing
Image data representation
Jpeg: still image data compression standard
Jpeg still image data compression standard
Usable data definition
What is subjective data
Spatial data and attribute data