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One of the major problems in computer vision involves dealing with uncertain information. Occlusion, dissimilar views, insufficient illumination, insufficient resolution, and degradation give rise to imprecise data. At the same time, incomplete or local knowledge of the scene gives rise to imprecise interpretation rules. Uncertainty arises at different processing levels of computer vision either because of the imprecise data or because of the imprecise interpretation rules. It is natural to build computer vision systems that incorporate uncertainty reasoning. The Dempster-Shafer (D-S) theory of evidence is appealing for coping with uncertainty hierarchically. However, very little work has been done to apply D-S theory to practical vision systems because some important problems are yet to be resolved. / Ph. D.

Most current machine vision systems for industrial inspection were developed with one specific task in mind. Due to the requirement for real-time operation, these systems are typically implemented in special purpose hardware that performs very specific operations. Hence, these systems inflexible in the sense that they cannot easily be adapted to other applications. However, current trends in computer technology suggests that low-cost general-purpose computers will be available in the very near future that are fast enough to meet the speed requirements of many industrial inspection problems. If this low-cost computing power is to be effectively utilized on industrial inspection problems, more general-purpose vision systems must be developed, vision systems that can be easily adapted to a variety of applications. Unfortunately, little research has gone into creating such general-purpose industrial inspection systems. In this dissertation, a general vision system framework has been developed that can be easily adapted to a variety of industrial web inspection problems. The objective of this system is to automatically locate and identify "defects" on the surface of the material being inspected. This framework is designed to be robust, to be flexible, and to be as computationally simple as possible. To assure robustness this framework employs a combined strategy of top-down and bottom-up control, hierarchical defect models, and uncertain reasoning methods. To make this framework flexible, a modular Blackboard framework is employed. To minimize computational complexity the system incorporates a simple multi-thresholding segmentation scheme, a fuzzy logic focus of attention mechanism for scene analysis operations, and a partitioning of knowledge that allows concurrent parallel processing during recognition. Based on the proposed vision system framework, a computer vision system for automated lumber grading has been developed. The purpose of this vision system is to locate and identify grading defects on rough hardwood lumber in a species independent manner. This problem seems to represent one of the more difficult and complex web inspection problems. The system has been tested on approximately 100 boards from several different species. Three different methods for performing label verification were tested and compared. These are a rule-based approach, a k-nearest neighbor approach, and a neural network approach. The results of these tests together with other considerations suggest that the neural network approach is the better choice and hence is the one selected for use in the vision system framework. Also, a new back-propagation learning algorithm using a steep activation function was developed that is much faster and more stable than the standard back-propagation learning algorithm. This algorithm was designed to speed the learning process involved in training a neural network to do label verification. However this algorithm seems to have general applicability. / Ph. D.

Indiana University-Purdue University Indianapolis (IUPUI) / We present a novel, variational and statistical approach for model-based segmentation. Our model generalizes the Chan-Vese model, proposed for concurrent segmentation of multiple objects embedded in the same image domain. We also propose a novel shape descriptor, namely the Multi-Compartment Distance Functions or mcdf. Our proposed framework for segmentation is two-fold: first, several training samples distributed across various classes are registered onto a common frame of reference; then, we use a variational method similar to Active Shape Models (or ASMs) to generate an average shape model and hence use the latter to partition new images. The key advantages of such a framework is: (i) landmark-free automated shape training; (ii) strict shape constrained model to fit test data. Our model can naturally deal with shapes of arbitrary dimension and topology(closed/open curves). We term our model Active Geometric Model, since it focuses on segmentation of geometric shapes. We demonstrate the power of the proposed framework in two important medical applications: one for morphology estimation of 3D Motor Neuron compartments, another for thickness estimation of Henle's Fiber Layer in the retina. We also compare the qualitative and quantitative performance of our method with that of several other state-of-the-art segmentation methods.

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.