# 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