Title page for ETD etd-11798-223049


Type of Document Master's Thesis
Author Lu, Qiang
Author's Email Address qianglu@vt.edu
URN etd-11798-223049
Title A Real-Time System for Color Sorting Edge-Glued Panel Parts
Degree Master of Science
Department Electrical and Computer Engineering
Advisory Committee
Advisor Name Title
Conners, Richard W. Committee Chair
Abbott, A. Lynn Committee Member
Brown, Ezra A. Committee Member
Kline, D. Earl Committee Member
Keywords
  • Color Sorting
  • Image Processing
  • Vision System
Date of Defense 1994-10-10
Availability unrestricted
Abstract
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.

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