Image Histogram: Unveiling Visual Insights, Exploring the Depths of Image Histograms in Computer Vision

By Fouad Sabry

What is Image Histogram

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.

How you will benefit

(I) Insights, and validations about the following topics:

Chapter 1: Image histogram

Chapter 2: Histogram

Chapter 3: Color histogram

Chapter 4: Thresholding (image processing)

Chapter 5: Histogram equalization

Chapter 6: Adaptive histogram equalization

Chapter 7: Histogram matching

Chapter 8: Tone mapping

Chapter 9: Error diffusion

Chapter 10: Graph cuts in computer vision

(II) Answering the public top questions about image histogram.

(III) Real world examples for the usage of image histogram in many fields.

Who this book is for

Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of Image Histogram.

• Language: English
• Publisher: One Billion Knowledgeable
• Release date: Apr 28, 2024

Book preview

Chapter 1: Image histogram

One kind of histogram is the picture histogram, which provides a visual depiction of the image's tonal distribution. It displays the pixel count for each intensity level. A viewer can quickly assess the image's overall tone distribution by inspecting its histogram.

Histograms for images can be found on a wide variety of websites today. Photographers might use them to demonstrate the range of tones obtained and the extent to which highlight and shadow detail have been lost.

The darkest values are found on the left side of the horizontal axis, the intermediate values in the middle, and the brightest values on the right. The vertical axis shows the total captured area in each of these zones as a function of the number of pixels.

Therefore, most of the information for a really dark image will be concentrated on the left and center of the histogram.

On the other hand, if the image is predominantly bright with very little in the way of shadows, the histogram will have the majority of its data points on the right and center of the graph.

Histograms of the image being modified are commonly generated by image editors. The histogram displays, along the vertical axis, the fraction of image pixels that have a given brightness or tonal value (horizontal axis). The algorithms in the digital editor make it possible for the user to change the brightness value of each pixel in real time. One well-known instance of such an algorithm is histogram equalization. Image enhancements in terms of luminance and contrast are thus possible.

Histograms of images are often employed as a means of thresholding in the field of computer vision. Image histograms can be inspected for spikes and valleys due to the graph's portrayal of pixel distribution as a function of tonal variation. Edge detection, picture segmentation, and co-occurrence matrices are just few of the applications where this threshold value comes in handy.

{End Chapter 1}

Chapter 2: Histogram

A histogram is a graphical tool for approximating the spread of numerical data. Karl Pearson is credited with coining the word.

The number of cases in each bin determines the height of the bar drawn over the bin, assuming the bins are all the same size. To represent the proportion of examples that fall into each of multiple categories, a histogram can be normalized to display relative frequencies, with the total of the heights equaling 1.

Bins can be of varying widths, in which case the size of the resulting rectangle is determined to be proportional to the bin's frequency of occurrence. Instead of frequency, the vertical axis represents frequency density, or the number of occurrences per unit of the horizontal axis variable. The following Census Bureau data exhibits examples of varying bin widths.

If the original variable is continuous, then the histogram's bins will leave no spaces between them, and the resulting rectangles will touch.

Histograms are commonly used for density estimation, or estimating the probability density function of the underlying variable, as they provide a rough idea of the density of the underlying distribution of the data. Histograms of probability densities always have their total areas normalized to 1. Histograms look like relative frequency plots if the x-intervals are all 1 unit long.

One of the seven fundamental instruments of quality control is the histogram.

Common data visualizations include bar charts and histograms. Despite their superficial similarities, there are significant distinctions between the two.

A bar graph is a type of chart in which bars are used to show the relative amount or frequency of various groups of data. The bars might be vertical or horizontal, and they are often laid out in a horizontal or vertical fashion, respectively, to facilitate comparisons between the various groups. The number of students in each grade level at a school is a good example of the kind of data that benefits from being displayed in a bar graph.

In contrast, numerical data can be visualized using a graph called a histogram. Binned bar charts display the number of observations or their frequency throughout a range of numbers. The bins are often specified as a series of discrete, non-overlapping time intervals. The histogram displays the distribution of the data graphically, with the number of observations in each bin being displayed. This can be helpful for seeing trends and patterns in the data, as well as drawing parallels between various data sets.

The information for the histogram on the right was derived from 500 unique