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In image processing, normalization is a process that changes the range of pixel intensity values. Applications include photographs with poor contrast due to glare, for example. Normalization is sometimes called contrast stretching or histogram stretching.
The terms normalization and standardization are sometimes used interchangeably, but they usually refer to different things. Normalization usually means to scale a variable to have a values between 0 and 1, while standardization transforms data to have a mean of zero and a standard deviation of 1.
Standardization: Standardizing the features around the center and 0 with a standard deviation of 1 is important when we compare measurements that have different units. Variables that are measured at different scales do not contribute equally to the analysis and might end up creating a bais.
In statistics, Standardization is the subtraction of the mean and then dividing by its standard deviation. In Algebra, Normalization is the process of dividing of a vector by its length and it transforms your data into a range between 0 and 1. Originally published at
Data standardization is the process of rescaling one or more attributes so that they have a mean value of 0 and a standard deviation of 1. Standardization assumes that your data has a Gaussian (bell curve) distribution.
Standardization is the process of developing, promoting and possibly mandating standards-based and compatible technologies and processes within a given industry. Standards for technologies can mandate the quality and consistency of technologies and ensure their compatibility, interoperability and safety.
Mean image is an image where i,j,c pixel is an average of i,j,c pixels from all images. So you take a mean separately for each position and each color channel.
'mean' value gives the contribution of individual pixel intensity for the entire image & variance is normally used to find how each pixel varies from the neighbouring pixel (or centre pixel) and is used in classify into different regions.
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