Histogram based processing (About image processing )
Histogram based processing (About image processing )
Introduction:
Histogram equalization is a technique for the image processing or enhancement.it involves finding a grey scale function transformation that creates an output image with a uniform histogram (or nearly so). We must find a transformation T that maps grey values r in the input image F to grey values s = T(r) in the transformed image processing .
Histogram based Image processing
It is assumed that
T is single valued and monotonically increasing, and
0 <= T(r) <= 1 for 0 <= r <= 1
Comparing Two Histograms :
the ability of comparing 2 histograms in terms of some
specific criteria for the similarity.
(first introduced by Swain and Ballard [Swain91] and further
generalized by Schiele and Crowley [Schiele96])
For the correlation, the high score represents a better matching than a lower score.
A perfect match can be 1 and a maximal mismatch ican be –1; a value of 0
indicates no correlation (random association) For chi-square, a low score represents a better match than a high score. A perfect match is 0 and a total mismatch is unbounded (depending on the size of the histogram). For histogram intersection,low scores indicate bad matchesand the high scores indicate good matches If both histograms are normalized to 1, after that a total mismatch is 0 and a perfect match is 1 .
Image enhancement using Histogram based image processing:
Image enhancement is a mean as the improvement of an image appearance by increasing dominance of some features or by decreasing ambiguity between different regions of the image. Image enhancement processes consist of a collection of techniques that seek to improve the visual appearance of an image or to convert the image to a form better suited for analysis by a human or machine. Many images such as remote sensing images, medical images, electron microscopy images and even real life photographic pictures, suffer from poor contrast. Therefore it is necessary to enhance the contrast.The purpose of image enhancement methods is to increase image visibility and details. Enhanced image provide clear image processing to eyes or assist feature extraction processing in computer vision system.
Numerous enhancement methods have been proposed but the enhancement efficiency, computational requirements, noise amplification, user intervention, and application suitability are the common factors to be considered when choosing from these different methods for specific image processing application.
Histogram-based Operations:
An important class of point operations is based upon the manipulation of an image processing or image histogram or a region histogram.
Contrast stretching:
Frequently, an image is scanned in such a way that the resulting brightness values do not make full use of the available in the dynamic ranges. This may be easily observed in the histogram of the brightness values . By stretching this image processing or histogram over the available dynamic range we attempt to correct this situation.
Equalization:
When one wishes to compare two or more images on a specific basis, that are texture, it is common to first normalize their histograms to a histogram “standard”. This may be especially useful when the images have been acquired under different circumstances. The common histogram normalization technique is histogram equalization where one attempts to change the histogram through the use of a function b = (a) into a histogram that is constant for all brightness values.
Question answers:
Q1: What is actually Histogram based image processing?
Ans: Histogram equalization is a technique for the image processing or enhancement. it involves finding a grey scale function transformation that creates an output image with a uniform histogram
Q2: What are the assumption taken here?
Ans: It is assumed that
T is single valued and monotonically increasing, and
0 <= T(r) <= 1 for 0 <= r <= 1
Q3: What is Contrast stretching?
Ans: Frequently, an image is scanned in such a way that the resulting brightness values do not make full use of the available in the dynamic ranges. This may be easily observed in the histogram of the brightness values . By stretching this image processing or histogram over the available dynamic range we attempt to correct this situation.
Q4: What is Equalization?
Ans: When one wishes to compare two or more images on a specific basis, that are texture, it is common to first normalize their histograms to a histogram “standard”. This may be especially useful when the images have been acquired under different circumstances. The common histogram normalization technique is histogram equalization where one attempts to change the histogram through the use of a function b = (a) into a histogram that is constant for all brightness values.
Q5: If both histograms are
normalized to 1 waht happens to it?
Ans: If both histograms are normalized to 1 after that a total mismatch is 0 and a perfect match is 1 .
This article is about About image processing.An image histogram is a type of histogram that acts as a graphical representation of the tonal distribution in a digital image. It plots the number of pixels for each tonal value. By looking at the histogram for a specific image a viewer will be able to judge the entire tonal distribution at a glance.good article…..