Digitized by Kansas Correctional Industries

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In order for an industrial inspection system to be of utility in manufacturing it must be fast, accurate, and flexible [Chin 1986]. Current machine vision systems are very specialized and inflexible in nature. A reason for the inflexibility of current machine vision systems is the need for real-time processing of image data. Such a need has forced both the use of very specialized image processing hardware as well as the use of rather simple, very specialized computer vision algorithms to do the analysis. On the other hand, most, if not all, of today’s computer vision methods are not general purpose in nature. In the absence of truly robust general purpose methods, developing satisfactory machine vision solutions will continue to involve experimenting with machine vision hardware and software components. Given the current state of machine vision technology, it would seem that the best method for creating flexible machine vision systems is, perhaps, to define a subclass of inspection problems where all the problems within the subclass have a number of common features about them. Such a subclass must be of interest to a number of manufacturers. It must also be “reasonable” to solve, given the current state of the art. Once the subclass has been selected, the next logical step would seem to be to create a device that makes performing all the needed experiments on the various problems within the class easy to perform. Based on the above line of reasoning, this work has four major objectives. The first objective is to define a meaningful subclass of inspection problems that are a) of interest to a number of manufacturers, and b) represent inspection tasks that seem “reasonable” within the current state-of-the-art of computer vision. The subclass of inspection problems selected for this work is the longitudinal planar web inspection problem under the two-dimensional imaging restriction. The second objective of this work is to create a vehicle that will allow the types of experimentation usually associated with the development of machine vision systems to be facilitated. This vehicle created is called a “machine vision prototyper.” The third objective of this work is to use the machine vision prototyper system to attack a particular planar web applications problem. The application considered is the problem of locating and identifying surface defects in surfaced hardwood lumber in a species independent manner. The fourth objective of this research is to indicate how the prototyper system can be used to attack a second planar web application problem. This application problem is the inspection of hardwood parts coming out of a molder. The utility of the machine vision prototyper system as an experimental tool is demonstrated on two of the three possible types of longitudinal planar web inspection problems. The results include the development of a machine vision system for a hardwood surfaced lumber surface feature detection problem, and a discussion of how the prototyper can be used to attack the problem of inspecting hardwood parts coming out of a molder. / Ph. D.

The biggest obstacle to robust color image processing of wood is in developing a color model that represents all possible defect colors. When the color model is too general or too specific, defect recognition fails because too many or too few non-defect pixels match the model, respectively. Because a color image of wood contains far more clear and clear-grain colored pixels than grain-knot and knot colored pixels, it is beneficial to first statistically identify and remove the clear and clear-grain colors and to use the accumulated data to simultaneously enhance and normalize the remaining grainknot and knot colored pixels. This process is here called adaptive color correlation. The normal image processing strategy is to search and test for defect features directly. The strategy proposed and developed here is to instead classify all wood pixels containing non-defect colors first, and then identify defect features. Once non-defect features are removed from an image, the task of finding candidate defects becomes easier and faster. This improvement is realized in a sigmoid-shaped color correlation implemented as an adaptive look-up table. As wood has become more expensive relative to manufacturing costs, more efficient methods of maximizing the recovery of clear wood in every board are sought. Optimization, in the present context, is a broad term for selecting products that are made from wood boards so the value of products produced is maximized for a given production requirement. Wood contains random defects which prohibit the production of some products. The normal optimization strategy is to mathematically change the value of under/over-produced products directly. The strategy proposed and developed here is to instead separate optimization into two steps: 1) determine all possible product solutions for a board; and 2) select the single best solution that satisfies value and production goals. Maximum utilization of clear wood is achieved because the solution is "frozen" before mathematically changing the value of products. Recovering long-lengths of clear wood is achieved because various length-based valuation strategies may be implemented as postsolution processes. Separating the product selection process from the solution generation process is shown by this work (simulation) to maximize value recovery.

Indiana University-Purdue University Indianapolis (IUPUI) / In crime scene investigations it is necessary to capture images of impression evidence such as tire track or shoe impressions. Currently, such evidence is captured by taking two-dimensional (2D) color photographs or making a physical cast of the impression in order to capture the three-dimensional (3D) structure of the information. This project aims to build a digitizing device that scans the impression evidence and generates (i) a high resolution three-dimensional (3D) surface image, and (ii) a co-registered two-dimensional (2D) color image. The method is based on active structured lighting methods in order to extract 3D shape information of a surface. A prototype device was built that uses an assembly of two line laser lights and a high-definition video camera that is moved at a precisely controlled and constant speed along a mechanical actuator rail in order to scan the evidence. A prototype software was also developed which implements the image processing, calibration, and surface depth calculations. The methods developed in this project for extracting the digitized 3D surface shape and 2D color images include (i) a self-contained calibration method that eliminates the need for pre-calibration of the device; (ii) the use of two colored line laser lights projected from two different angles to eliminate problems due to occlusions; and (iii) the extraction of high resolution color image of the impression evidence with minimal distortion.The system results in sub-millimeter accuracy in the depth image and a high resolution color image that is registered with the depth image. The system is particularly suitable for high quality images of long tire track impressions without the need for stitching multiple images.