Want this question answered?
A histogram
All that histogram equalization does is remap histogram components on the intensity scale. To obtain a uniform (­at) histogram would require in general that pixel intensities be actually redistributed so that there are L groups of n=L pixels with the same intensity, where L is the number of allowed discrete intensity levels and n is the total number of pixels in the input image. The histogram equalization method has no provisions for this type of (arti®cial) redistribution process.
Assuming that if one were to list the sides of a geometric shape he would do so following the shape's perimeter, my guess is that consecutive sides is a synonym for adjacent sides.
You add the scores together and then divide by the number of scores. So if you rolled a dice and your scores were, 3, 5, 2, and 6 You would add these together, getting 16, and then you would divide this by 4 (because you have 4 scores) and so your mean would be 4 (16/4=4) sum of scores / number of scores = mean. Hope this helps.
When putting the scores in, you use the normal distribution graph, which is the best start.
As a visual representation of data, then a histogram is a way of analysing data.
NO where!
It would help if the "following" did actually follow!
You draw a series of line segments joining the points which would be the middle of the top of each bar of the histogram.
A histogram
bimodal histogram is a histogram where there are two clear high points on the graph. ex.) age of people at a preschool play group. There would be preschool age and adult age. Not many teenagers or elderly. Bimodal...the ages representing preschool and adult (parents?) would stand above the rest
All that histogram equalization does is remap histogram components on the intensity scale. To obtain a uniform (­at) histogram would require in general that pixel intensities be actually redistributed so that there are L groups of n=L pixels with the same intensity, where L is the number of allowed discrete intensity levels and n is the total number of pixels in the input image. The histogram equalization method has no provisions for this type of (arti®cial) redistribution process.
Bad test scores or periods of missed school.
No. That would be a histogram.
Choosing wider class boundaries would cause a histogram of the data to present the appearance of a uniform distribution. This is because the data points within each wider class would be spread out more evenly, giving the histogram a more uniform look.
A histogram is used to analyze a distribution of data. It look like a graph and can be used in many formats - the most popular may be in Photography, showing the distribution of shadows and light in a visual representation.
You would add all the numbers or scores out and then divide by how many scores you added.