What do we do when the value does not fall at an Empirical Rule subdivision? By using z-scores, we have the ability to locate a percentage (or area) under a standard normal distribution at any location. These subdivisions are fine for determining percentages as long as we are dealing with values that fall at these exact subdivision locations. We have seen that the Empirical Rule (68% - 95% - 99.7%) subdivides the area under a normal distribution into sections with widths of one standard deviation. The fact stated above is the reason we can find an area over an interval for any normal curve by finding the corresponding area under a standard normal curve (with a mean of 0 and a standard deviation of 1). (The term "area" will refer to "area percentage".) For example, the area percentage to the right of 1.5 standard deviations above the mean is identical for all normal curves. Areas under all normal curves are related.
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I = ind2gray(X, map) % Convert indexed to grayscale Here is how I would accomplish what you are doing in your example: load trees % Load the image data So the thing is, camera will be mounted on the base of robotic arm. I suggest the function graythresh, which will compute a threshold to plug into im2bw (or the newer imbinarize). How to convert 2D (x,y) coordinates to 3D (x,y,z) coordinates using MATLAB Ask Question Asked 4 years, 11 months ago Modified 4 years, 10 months ago Viewed 2k times 0 I am trying to automate an robotic arm using MATLAB. Next, you need to determine a threshold to use to convert the grayscale image to a binary image. For RGB images the function rgb2gray would do the same. The sample built-in image trees.mat that you load in your example is an indexed image, and you should therefore use the function ind2gray to first convert it to a grayscale intensity image. 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