Mean Value of Pixels in Neighborhood Solution

STEP 0: Pre-Calculation Summary
Formula Used
Global Mean Pixel Intensity level of Subimage = sum(x,0,Number of Intensity Levels-1,Intensity Level of Ith Pixel*Probability of Occurrence of Rith in Subimage)
mSxy = sum(x,0,L-1,ri*pSxy_ri)
This formula uses 1 Functions, 4 Variables
Functions Used
sum - Summation or sigma (∑) notation is a method used to write out a long sum in a concise way., sum(i, from, to, expr)
Variables Used
Global Mean Pixel Intensity level of Subimage - Global Mean Pixel Intensity level of Subimage is the global mean computed over an entire image and are useful for gross adjustments in overall intensity and contrast.
Number of Intensity Levels - Number of Intensity Levels is the total number of distinct intensity values an image can represent, determined by its bit depth.
Intensity Level of Ith Pixel - (Measured in Watt per Square Meter) - Intensity Level of Ith Pixel represents the i-th possible intensity level in the image.
Probability of Occurrence of Rith in Subimage - Probability of Occurrence of Rith in Subimage represents the probability of occurrence of the intensity level r_i within the subimage S_xy.
STEP 1: Convert Input(s) to Base Unit
Number of Intensity Levels: 4 --> No Conversion Required
Intensity Level of Ith Pixel: 15 Watt per Square Meter --> 15 Watt per Square Meter No Conversion Required
Probability of Occurrence of Rith in Subimage: 0.25 --> No Conversion Required
STEP 2: Evaluate Formula
Substituting Input Values in Formula
mSxy = sum(x,0,L-1,ri*pSxy_ri) --> sum(x,0,4-1,15*0.25)
Evaluating ... ...
mSxy = 15
STEP 3: Convert Result to Output's Unit
15 --> No Conversion Required
FINAL ANSWER
15 <-- Global Mean Pixel Intensity level of Subimage
(Calculation completed in 00.004 seconds)

Credits

Created by Zaheer Sheik
Seshadri Rao Gudlavalleru Engineering College (SRGEC), Gudlavalleru
Zaheer Sheik has created this Calculator and 10+ more calculators!
Verified by Dipanjona Mallick
Heritage Insitute of technology (HITK), Kolkata
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14 Intensity Transformation Calculators

Histogram Linearization
Go Discrete Form of Transformation = ((Number of Intensity Levels-1)/(Digital Image Row*Digital Image Column)*sum(x,0,Number of Intensity Levels-1,Number of Pixels with Intensity Ri))
Nth Moment of Discrete Random Variable
Go Nth Moment of Discrete Random Variable = sum(x,0,Number of Intensity Levels-1,Probability of Intensity Ri*(Intensity Level of Ith Pixel-Mean of Intensity Level)^Order of Moment)
Variance of Pixels in Subimage
Go Variance of Pixels in Subimage = sum(x,0,Number of Intensity Levels-1,Probability of Occurrence of Rith in Subimage*(Intensity Level of Ith Pixel-Subimage Pixel Mean Intensity Level)^2)
Mean Value of Pixels in Neighborhood
Go Global Mean Pixel Intensity level of Subimage = sum(x,0,Number of Intensity Levels-1,Intensity Level of Ith Pixel*Probability of Occurrence of Rith in Subimage)
Mean Value of Pixels in Subimage
Go Mean Value of Pixels in Subimage = sum(x,0,Number of Intensity Levels-1,Intensity Level of ith Pixel in Subimage*Probability of Zi in Subimage)
Histogram Equalization Transformation
Go Transformation of Continuous intensities = (Number of Intensity Levels-1)*int(Probability Density Function*x,x,0,Continuous Intensity)
Transformation Function
Go Transformation Function = (Number of Intensity Levels-1)*sum(x,0,(Number of Intensity Levels-1),Probability of Intensity Ri)
Average Intensity of Pixels in Image
Go Average Intensity of Image = sum(x,0,(Intensity Value-1),(Intensity Level*Normalized Histogram Component))
Characteristic Response of Linear Filtering
Go Characteristic Response of Linear Filtering = sum(x,1,9,Filter Coefficients*Corresponding Image Intensities of Filter)
Bits Required to Store Digitized Image
Go Bits in Digitized Image = Digital Image Row*Digital Image Column*Number of Bits
Bits Required to Store Square Image
Go Bits in Digitized Square Image = (Digital Image Column)^2*Number of Bits
Energy of Components of EM Spectrum
Go Energy of Component = [hP]/Frequency of Light
Wavelength of Light
Go Wavelength of Light = [c]/Frequency of Light
Number of Intensity Levels
Go Number of Intensity Level = 2^Number of Bits

Mean Value of Pixels in Neighborhood Formula

Global Mean Pixel Intensity level of Subimage = sum(x,0,Number of Intensity Levels-1,Intensity Level of Ith Pixel*Probability of Occurrence of Rith in Subimage)
mSxy = sum(x,0,L-1,ri*pSxy_ri)

What is the Mean Value of Pixels in Neighborhood?

The global mean and variance are computed over an entire image and are useful for gross adjustments in overall intensity and contrast. A more powerful use of these parameters is in local enhancement, where the local mean and variance are used as the basis for making changes that depend on image characteristics in a neighborhood about each pixel in an image.

How to Calculate Mean Value of Pixels in Neighborhood?

Mean Value of Pixels in Neighborhood calculator uses Global Mean Pixel Intensity level of Subimage = sum(x,0,Number of Intensity Levels-1,Intensity Level of Ith Pixel*Probability of Occurrence of Rith in Subimage) to calculate the Global Mean Pixel Intensity level of Subimage, The Mean Value of Pixels in Neighborhood formula is used to find global mean which is computed over an entire image and are useful for gross adjustments in overall intensity and contrast. Global Mean Pixel Intensity level of Subimage is denoted by mSxy symbol.

How to calculate Mean Value of Pixels in Neighborhood using this online calculator? To use this online calculator for Mean Value of Pixels in Neighborhood, enter Number of Intensity Levels (L), Intensity Level of Ith Pixel (ri) & Probability of Occurrence of Rith in Subimage (pSxy_ri) and hit the calculate button. Here is how the Mean Value of Pixels in Neighborhood calculation can be explained with given input values -> 15 = sum(x,0,4-1,15*0.25).

FAQ

What is Mean Value of Pixels in Neighborhood?
The Mean Value of Pixels in Neighborhood formula is used to find global mean which is computed over an entire image and are useful for gross adjustments in overall intensity and contrast and is represented as mSxy = sum(x,0,L-1,ri*pSxy_ri) or Global Mean Pixel Intensity level of Subimage = sum(x,0,Number of Intensity Levels-1,Intensity Level of Ith Pixel*Probability of Occurrence of Rith in Subimage). Number of Intensity Levels is the total number of distinct intensity values an image can represent, determined by its bit depth, Intensity Level of Ith Pixel represents the i-th possible intensity level in the image & Probability of Occurrence of Rith in Subimage represents the probability of occurrence of the intensity level r_i within the subimage S_xy.
How to calculate Mean Value of Pixels in Neighborhood?
The Mean Value of Pixels in Neighborhood formula is used to find global mean which is computed over an entire image and are useful for gross adjustments in overall intensity and contrast is calculated using Global Mean Pixel Intensity level of Subimage = sum(x,0,Number of Intensity Levels-1,Intensity Level of Ith Pixel*Probability of Occurrence of Rith in Subimage). To calculate Mean Value of Pixels in Neighborhood, you need Number of Intensity Levels (L), Intensity Level of Ith Pixel (ri) & Probability of Occurrence of Rith in Subimage (pSxy_ri). With our tool, you need to enter the respective value for Number of Intensity Levels, Intensity Level of Ith Pixel & Probability of Occurrence of Rith in Subimage and hit the calculate button. You can also select the units (if any) for Input(s) and the Output as well.
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