Find the histogram of the 4 ×48-bit image and derive its histogram-based features. [ 108

step 1 we are tasked with finding a histogram of this dataset. And the histogram is very much like a gr

Find the histogram of the 4 ×48-bit image and derive its...

Find the histogram of the $4 \times 48$-bit image and derive its histogram-based features.
$$
\left[\begin{array}{rrrr}
108 & 81 & 69 & 92 \\
114 & 105 & 72 & 101 \\
117 & 73 & 67 & 92 \\
105 & 0 & 65 & 101
\end{array}\right]
$$

Key Concepts

Image Histogram

An image histogram is a graphical representation that shows the frequency distribution of pixel intensities in an image. It counts how many pixels have each possible intensity value, which helps in understanding the overall brightness and contrast of the image. This concept is fundamental in image processing as it provides insights into the image’s tonal distribution and is used in various applications such as contrast enhancement and thresholding.

Histogram-Based Features

Histogram-based features are quantitative measures derived from the image histogram that summarize the statistical properties of the pixel intensity distribution. Common features include measures of central tendency (like the mean) and dispersion (like variance), as well as higher order statistics such as skewness and kurtosis, which describe the asymmetry and peakedness of the distribution. These features are used for image analysis, segmentation, and texture classification.

Statistical Moments

Statistical moments are numerical values that capture important characteristics of the histogram’s shape. The first moment (mean) indicates the average intensity, the second moment (variance) measures the spread of intensities, while the third (skewness) and fourth moments (kurtosis) provide information about the asymmetry and flatness or peakedness of the distribution. These moments are crucial in characterizing and comparing different images based on their intensity distributions.

Transcript

00:01 We are tasked with finding a histogram of this dataset.

00:05 And the histogram is very much like a graph, except it's a more discrete version.

00:10 So basically what we're doing is associating an interval with a value.

00:16 And we do that in the following way.

00:19 We draw this thing that looks something like a graph, except we would map this interval, for example, 41 to 50.

00:27 Let's call that 41 to 50 over here.

00:29 And we would associate this interval with the value two, right, according to this table.

00:40 So we have, let's say, a two over here, and we would, oops, two over here, and we would draw this bar.

00:46 And that means that this interval, 41 to 50, corresponds to this two.

00:52 And we would repeat the same process for all of my other corresponding data sets.

00:57 So over here, we have 51 through 60, and that corresponds.

01:01 To 2.

01:02 So let's say we have 51 over here through 60, 21 through 60, and that's going to correspond to 2 as well.

01:10 So we have another bar over here.

01:13 And we're just going to keep incrementing up.

01:14 So we have 61 through 70.

01:16 That's going to look something like this.

01:18 We'll just do dot, dot, dot.

01:20 So that's 61 through 70...

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