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This thesis describes the development of a software system for color sorting hardwood edge-glued panel parts. Conceptually, this system can be broken down into three separate processing steps. The first step is to segment color images of each of the two part faces into background and part. The second step involves extracting color information from each region labeled part and using this information to classify each part face as one of a pre-selected number of color classes plus an out class. The third step involves using the two face labels and some distance information to determine which part face is the better to use in the face of an edge-glued panel. Since a part face is illuminated while the background is not, the segmentation into background and part can be done using very simple computational methods. The color classification component of this system is based on the Trichromatic Color Theory. It uses an estimate of a part's 3-dimension (3-D) color probability function, P, to characterize the surface color of the part. Each color class is also represented by an estimate of the 3-D color probability function that describes the permissible distribution of colors within this color class. Let P_omega_i denote the estimated probability function for color class omega_i. Classification is accomplished by finding the color difference between the estimated color probability function for the part and each of the estimated 3-D color probability functions that represent the color classes. The distance function used is the sum of the absolute values of the differences between the elements of the estimated probability function for a class and the estimated probability function of the part. The sample is given the label of the color class to which it is closest if this distance is less than some class specific threshold for that class. If the distance to the class to which the part is closest is larger than the threshold for that class, the part is called an out. This supervised classification procedure first requires one to select training samples from each of the color classes to be considered. These training samples are used to generate P_omega_i for each color class omega_i and to establish the value of the threshold T_i that is used to determine when a part is an out. To aid in determining which part face is better to use in making a panel, the system allows one to prioritize the various color classes so that one or more color classes can have the same priority. Using these priorities, labels for each of the part faces, and the distance from each of the part faces' estimated probability functions to the estimated probability function of the class to which each face was assigned, the decision logic selects which is the ``better'' face. If the two part faces are assigned to color classes that have different priorities, the part face assigned to the color class with higher priority is chosen as the better face. If the two part faces have been assigned to the same color class or to two different classes having the same priority, the part face that is closest to the estimated probability function of the color class to which it has been assigned is chosen to be the better face. Finally, if both faces are labeled out, the part becomes an out part. This software system has been implemented on a prototype machine vision system that has undergone several months of in-plant testing. To date the system has only been tested on one type of material, southern red oak, with which it has proven itself capable of significantly out performing humans in creating high-quality edge-glued panels. Since southern red oak has significantly more color variation than any other hardwood type or species, it is believed that this system will work very well on any hardwood material. / Master of Science

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. / Master of Science