Quality Control Assurance Applications

The Kernel Extension Rapid New Evolution Level (KERNEL) was developed to grade morphological features and color defects in turkey carcasses (15). The turkey carcasses suspended on a chain are appraised for pin feathers, visceras, and filets/cuts yield. Different appearance (color) characteristics were extracted using color segmentation methods. A CMVS was developed to identify the reddish comb of each chicken and thus be able to count the chickens before placing them in the crate automatically. Another system was developed to measure count, uniformity ratio, color, and melanosis and to detect foreign objects in processing white shrimp (16). Shrimp area was used to estimate weight and calculate count and uniformity ratio. Color and melanosis were quantified by correlating with a trained inspector.

A CMVS was developed to inspect frozen dinners for proper food amount and ratio in each compartment (17). The image captured is analyzed by color-connectivity analysis where it is compared against predetermined tolerance limits and proximity of one food to another to check for

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Figure 5. Color measurement using an MVS.

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Figure 6. Product cutting/portioning using an MVS, a scale, and a cutting tool.

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Figure 6. Product cutting/portioning using an MVS, a scale, and a cutting tool.

spills. A method to characterize the color of fruits and vegetables at different ripening states, describe color changes during storage of mushrooms, distinguish varieties by shape, and recognize shape defects was developed calculating the average red, green, and blue (RGB) color components of the pixels belonging to each product (18). Utilizing hue, saturation, and intensity, an (HSI) MVS was trained to distinguish between good and greened potatoes and yellow and green apples (19). The CMVS used multivariate discriminate techniques to hue histograms of representing features and increased accuracy by reducing the number of hue bins by selecting significant features or summing groups of hue bins. X-ray technology has been developed to detect motals, glass, minerals, stone, polyvinyl chloride (PVC), dense rubber, and other materials within a package or in a conveyor belt (20). The system consists of an X-ray generator, a detector, and a processor. Differences in density are detected, and the sensor converts the X-ray signal into light, then into an electrical signal before it is converted into a video signal. It can also be used for morphology processing and for detection of underfilled or broken packages.

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