Title page for ETD etd-042099-141554


Type of Document Master's Thesis
Author King, William E
Author's Email Address willking@mindspring.com
URN etd-042099-141554
Title Using an FPGA-Based Processing Platform in an Industrial Machine Vision System
Degree Master of Science
Department Electrical and Computer Engineering
Advisory Committee
Advisor Name Title
Conners, Richard W. Committee Chair
Abbott, A. Lynn Committee Member
Kline, D. Earl Committee Member
Keywords
  • color sorting
  • hardwood
  • image processing
  • machine vision
  • FPGA
  • color matching
  • reconfigurable computing
Date of Defense 1998-02-20
Availability unrestricted
Abstract
This thesis describes the development of a commercial machine vision system as a case study for utilizing the Modular Reprogrammable Real-time Processing Hardware (MORRPH) board. The commercial system described in this thesis is based on a prototype system that was developed as a test-bed for developing the necessary concepts and algorithms. The prototype system utilized color linescan cameras, custom framegrabbers, and standard PCs to color-sort red oak parts (staves). When a furniture manufacturer is building a panel, very often they come from edge-glued paneled parts. These are panels formed by gluing several smaller staves together along their edges to form a larger panel. The value of the panel is very much dependent upon the “match” of the individual staves—i.e. how well they create the illusion that the panel came from a single board as opposed to several staves.

The prototype system was able to accurately classify staves based on color into classes defined through a training process. Based on Trichromatic Color Theory, the system developed a probability density function in 3-D color space for each class based on the parts assigned to that class during training. While sorting, the probability density function was generated for each scanned piece, and compared with each of the class probability density functions. The piece was labeled the name of the class whose probability density function it most closely matched. A “best-face” algorithm was also developed to arbitrate between pieces whose top and bottom faces did not fall into the same classes. [1] describes the prototype system in much greater detail.

In developing a commercial-quality machine vision system based on the prototype, the primary goal was to improve throughput. A Field Programmable Gate Array (FPGA)-based Custom Computing Machine (FCCM) called the MORRPH was selected to assume most of the computational burden, and increase throughput in the commercial system. The MORRPH was implemented as an ISA-bus interface card, with a 3 x 2 array of Processing Elements (PE). Each PE consists of an open socket which can be populated with a Xilinx 4000 series FPGA, and an open support socket which can be populated with support chips such as external RAM, math processors, etc.

In implementing the prototype algorithms for the commercial system, a partition was created between those algorithms that would be implemented on the MORRPH board, and those that would be left as implemented on the host PC. It was decided to implement such algorithms as Field-Of-View operators, Shade Correction, Background Extraction, Gray-Scale Channel Generation, and Histogram Generation on the MORRPH board, and to leave the remainder of the classification algorithms on the host.

By utilizing the MORRPH board, an industrial machine vision system was developed that has exceeded customer expectations for both accuracy and throughput. Additionally, the color-sorter received the International Woodworking Fair’s Challengers Award for outstanding innovation.

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