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            <c val="#008080">IMAGE ENHANCEMENT</c>
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            <b>
              <c val="#800080">Machine Quality Control of Corn</c>
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            <i>Contributed by:</i>
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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">Dr. John Reid</p>
        <p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="left" list-style-type="none" tabs="inherit">Agricultural Engineering Department</p>
        <p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="left" list-style-type="none" tabs="inherit">University of Illinois</p>
        <p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="left" list-style-type="none" tabs="inherit">Urbana, Illinois</p>
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          </f> is the science of quantifying visual information electronically so that it can be used without human monitoring. Machine vision technology applies image processing for the purpose of extracting visual information for a variety of control and quantification applications. For example, automated visual inspection of dried corn is useful for monitoring the quality of various lots and for monitoring the storage and transportation of the lots. </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">Before corn reaches commercial markets it is usually transported from farms to grain elevators. Handling can result in damage and breakage to the kernels which makes them susceptible to invasion from microorganisms. Corn that has been improperly dried has a higher risk of becoming damaged. Kernels of corn that have been improperly dried exhibit "stress cracks." </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">Stress cracks are fissures that occur under the pericarp, or outer surface, of the corn kernel. By detecting the incidence of stress cracks in a sample or lot of grain, it is possible to evaluate if the corn has been improperly dried. Image analysis is a tool for automatically identifying the stress cracks in a grain sample.</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 stress cracks are visualized by backlighting the kernels. The amount of light transmitted through the kernels is dependent on the existence of a crack. Sufficient illumination is also provided on the surface (frontlighting), to visualize the kernel shape, but not mask out the stress crack effects. </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">Here is an image of a normal kernel of corn under the lighting conditions described above.</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 image <b>cracked</b> shows an image of a kernel with several internal stress cracks:</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">Using machine vision, stress crack detection can take place as an automated process. A sample of corn to be tested is placed in a machine that separates the kernels and delivers them individually to a viewing stage. Image analysis takes place on the viewing stage to determine the existence of stress cracks in a single kernel. The analysis of an entire sample provides statistical information that can be used in marketing decisions on when, how, and whether to sell the corn.</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">Stress cracks appear visually as abrupt changes in intensity. Edge enhancement functions can be used to highlight these intensity changes. </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 edge-enhancement operation enhances both internal edges and the more predominant edges on the boundary of the kernel. These boundary edges will need to be removed before the machine can adequately check for the existence of cracks. To remove these edges, it's necessary to make a "mask" out of the silhouette of the corn kernel.</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 binary image forms a mask that represents the original kernel shape. The boundary of that shape occurs where the edges of the corn kernel occur. Image erosion can be used to reduce the size of the image mask. Then, a mask operation can be used to remove boundary edges from stress crack detection.</p>
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        <p style="Normal" margin-left="inherit" margin-right="inherit" text-indent="inherit" text-align="inherit" list-style-type="inherit" tabs="inherit">Now we apply erosion with the same structuring element twice using the</p>
        <p style="Normal" margin-left="inherit" margin-right="inherit" text-indent="inherit" text-align="inherit" list-style-type="inherit" tabs="inherit">center of the structuring element as the origin and 0.2 as a foreground value.</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">Now use the mask image to extract the relevant part of the edge enhanced image to isolate edges that are within the kernel:</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">Perform a histogram on the edges. In a real image, the edge-enhancement operation also enhances noise. The histogram is used to determine a distribution of edges so that a gradient threshold can be used to isolate the "true" edges representing the kernel damage. The low-frequency edges (close to gray level 0) are not of interest. </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">Skeletonize the remaining connected pixels:</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">Project the remaining image signal onto the horizontal and vertical axes: sum all the rows, and then sum all the columns. If there is a significant distribution of pixels, then a stress crack has been detected within the kernel.</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">Statistical properties of these profile signatures can be used to make a decision on whether or not stress cracks exist in this kernel.</p>
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