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Table 2 presents the results of automation of backfat depth and loin eye muscle thickness measurements on swine carcasses. The images were collected using the Sensoray frame grabber and Aloka SSD 500V ultrasound scanner for 749 carcasses at a line speed of 1200 carcasses per hour. Auto depth measurements were compared with the manual measurements done by a certified and experienced technician. The accuracy statistic for fat depth comparison is defined as an absolute difference of less than 3 mm between auto and manual measurements. The accuracy statistic for loin depth is defined as an absolute difference of less than 6 mm between auto and manual measurements.
and loin depth for swine carcasses.
Aspects of the present invention lend themselves to implementations in a variety of devices including, but not limited to, hardware circuitry, programmable logic devices, firmware, software, and combinations thereof. A specific example includes computer readable medium storing computer executable instructions that when executed by a processor perform one or more of the process steps. The implementations of the various algorithms and methods described herein effectively transforms what would otherwise be a general purpose processor into a specially-programmed processor that is configured and arranged to implement the specialized algorithms.
While the present invention has been described in the above description and in the claims that follow, those skilled in the art will recognize that changes may be made thereto without departing from the spirit and scope of the present invention. Such changes may include, for example, the implementation of one or more approaches including those approaches as described in the contemplated claims below which further characterize aspects of the present invention.
calculating the fat depth and muscle depth from the determined boundaries.
using un-discarded images from the plurality of images in the steps of determining and calculating.
3. A method of claim 1, wherein the food animal portion is a longissimus dorsi muscle of a hot carcass.
4. A method of claim 1, further including the step of using the fat depth and muscle depth to perform at least one of the following: select livestock for breeding, and rate the portion based on quality criteria, to sort livestock, and wherein the aforementioned steps are used to calibrate equipment used in performing the method.
5. A method of claim 1, further including repeating the steps of presenting, determining and calculating on subsequently processed food animal portions, wherein the steps of repeating, presenting and measuring are implemented automatically and without human intervention.
6. A method of claim 1, wherein the step of determining the skin-fat boundary, the fat-muscle boundary, and the rib-muscle boundary includes capturing at least one image representing responses of the response-provoking signal as a function of depth in the animal portion, and analyzing the captured image.
correlating the identified top interface to the skin-fat boundary.
based upon the comparison, identifying interfaces within the captured image that correspond to fat layers.
based upon the comparison, identifying rib shadow column locations.
based upon the comparison of the calculated intensity average values for small horizontal stripes, identifying rib top boundaries.
11. A method of claim 10, wherein the step of determining the rib-muscle boundary further includes the step of comparing intensity average differences for a pair of consecutive horizontal stripes between a pair of identified rib column locations.
to calculate the fat depth and muscle depth from the determined boundaries.
13. The systems of claim 12, further comprising a pressure sensor for sensing pressure being exerted between the probe and the food animal.
a user interface configured and arranged to allow human interaction with the device.
15. The device of claim 14, wherein the image sensor is an ultrasound imaging probe having indicators that alert an operator of the current amount of pressure being detected.
17. The method of claim 1, wherein the step of determining includes identifying rib shadow column locations.
18. The method of claim 1, further including a step of identifying a region of interest by identifying rib shadow column locations and wherein the region of interest is used in the step of determining.
19. The method of claim 1, further including a step of identifying rib top boundaries for intensity average values of horizontal stripes.
20. The method of claim 1, wherein the step of determining the rib-muscle boundary further includes a step of comparing intensity average differences for a pair of consecutive horizontal stripes between a pair of rib column locations.
21. The method of claim 1, wherein the automated method is carried out at line speeds of at least 1,000 carcasses per hour.
22. The method of claim 1, further including the step of acquiring, automatically, without human intervention, and in response to the response-provoking signal and the sensed pressure, a set of acceptable images.
23. The method of claim 11, wherein the data processor is further configured and arranged to automatically determine the skin-fat boundary, the fat-muscle boundary, and the rib-muscle boundary using upon statistical filtering.
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Fully automated and self-cleaning, the Colossus® Series provides continuous throughput while maintaining constant process pressure. Backflush capabilities significantly reduce operator intervention.
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Bin Dividers were used in LP and CD tracks to separate releases from each artist. They were usually simple plain black or white pieces of plastic with the artist's name printed on a tab at the top.
Virgin America issued this custom bin divider for US record shops. This single sided divider could either be used for the LP bin or CD bin - perforations were made across the card so it could be trimmed to size.
New US reissues of Life In A Day and Real To Real Cacophony were also advertised.
The company printed up a large number of these promotional items and mint, untorn copies to turn up occasionally.
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We represent individual and corporate clients, aiding them in resolving problems running the full gamut of business and personal disputes. We understand that litigation can be very expensive, and, therefore, we approach each case from the perspective that success is often achieved through negotiation rather than racing to the courthouse. However, whether court action is required or when a decision is made to try an alternative method of dispute resolution, we're here to help.
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Ronald J. Saffa has been a Certified Public Accountant, since 1974. He received his Bachelor of Science, and Master of Science, in Accounting, from Oklahoma State University. After his certification, he worked 9 years for the Internal Revenue Service. In 1981, he received his Juris Doctor of Law, from the University of Tulsa. In 1982, he was admitted to the Oklahoma Bar Association. In 1983, he was, also, admitted to the U.S. District Court, Northern and Eastern Districts of Oklahoma, the U.S. Court of Appeals, Tenth and Federal Circuits, the U.S. Tax Court and the U.S. Court of Federal Claims. Ronald was then admitted to the Supreme Court, in 1985. In 2011, he was named one of the Super Lawyers of Oklahoma. His expertise lies in the areas of Federal Taxation and State Taxation.
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