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              <c val="#800080">Convex Hull</c>
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        <phrase>convex hull</phrase>
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        <p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="left" list-style-type="none" tabs="inherit">This function computes the convex hull of an image. The convex hull is a way of characterizing the shape of an image by determining which pixels are adjacent to other pixels of the same intensity. This is a good way to find holes and convex features in an image.</p>
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        <p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="left" list-style-type="none" tabs="inherit">The function returns a binary image matrix that contains the convex hull of <b>M</b>, with foreground pixels set to value 1 and background to 0. </p>
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        <p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="left" list-style-type="none" tabs="inherit">This function finds the convex hull of the pixels with foreground intensity value fg in matrix <b>M</b>. The output is binarized with values of 1 inside the convex hull and 0 outside. The algorithm used is described in <i>Digital Picture Processing</i> by A. Rosenfeld and A. C. Kak, page 269, 1982. </p>
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        <p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="left" list-style-type="none" tabs="inherit">The hull is found by choosing P1 as the leftmost and topmost point of the set of pixels in <b>M</b> and L1 as the horizontal line through P1. Then it rotates L1 about P1 until it hits the value fg in the set of pixels. Calling the resulting rotated line L2 and letting P2 be the point farthest from P1 along L2, the algorithm then repeats, until Pn = P1. The union of {P1,P2, ... , Pn-1} are the vertices of the convex hull. </p>
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        <p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="left" list-style-type="none" tabs="inherit">For example:</p>
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        <p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="left" list-style-type="none" tabs="inherit">Compute the convex hull of the set with foreground pixel value 1:</p>
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        <p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="left" list-style-type="none" tabs="inherit">Notice that the convex hull contains all the pixels of 1 in the original matrix, and it is the smallest convex set.</p>
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        <p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="left" list-style-type="none" tabs="inherit">If we select the foreground pixel value as 2, we have a different convex hull:</p>
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        <p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="left" list-style-type="none" tabs="inherit">In the next example, a binary image of a kernel of corn is superimposed with its convex hull, demonstrating the usefulness of the hull in describing the contour and holes in an image:</p>
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        <p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="left" list-style-type="none" tabs="inherit">Find the convex hull of the white (1) pixels in the binarized image, and then subtract the image from the hull to find the convex deficiency. Instead of subtracting directly, the binarized image and convex hull are scaled so the area outside the hull is gray, the image pixels black, and the convex deficiency white:</p>
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            <ml:mult style="default" />
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            <ml:real>255</ml:real>
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      <component hide-arguments="false" clsid-buddy="8442da2d-08d7-45ad-a4a6-f28bee6ae06a" item-idref="19" disable-calc="false">
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