To fully optimize the value of material produced from a log requires information about the log's internal defects prior to log breakdown. Studies have shown that a 7 to 21 percent improvement in log value recovery can be achieved if the location and identity of internal defects are known. Recent developments in advanced nondestructive testing methods such as CT and MRI offer, for the first time, the possibility of finding internal defects in logs prior to breakdown. Our ability to detect and recognize defects using this data depends Critically on our understanding of wood structure and our ability to devise reliable method for automated image interpretation. While a lot of work has gone into demonstrating that certain types of defects manifest themselves in such sensor imagery, there has not been a systematic approach toward making the automatic inspection of logs a practical reality. This dissertation describes work aimed at creating a viable automated technology for locating and identifying log defects. The imaging modality used in this dissertation is CT. An important first step is to establish a data base of imagery and the ground truth information to determine how the various defects manifest themselves in this imagery. The second step is to study defect characterization and determine exactly which defects are detectable. The final step is to develop a basic method of approach to automated image analysis. A data base has been created from two hardwood species. It is representative of hardwood logs in the sense that it contains almost all the major defects. Visual inspection and analysis of these CT images have shown that most defects manifest themselves in CT imagery. These defects can be detected by features such as intensity, 3-d shape, and texture. As a means of automated image analysis, a knowledge-based vision system has been developed. It consists of three components: a data acquisition unit, an image segmentation module, and scene analysis module. A 3-d adaptive LS filter has been developed in the segmentation module that is efficient in removing annual rings while preserving other needed high frequency detail. Images are segmented using a multiple threshold scheme and regions are grouped using a 3-d connected volume growing algorithm. To represent the 3-d nature of wood defects, a set of basic features have been defined and used to design a set of hypothesis tests. These features seem to be adequate for defect recognition. To cope with imprecision and ambiguity the Dempster-Shaffer model for knowledge representation is used in the vision system. As a viable alternative to Bayesian-based theory, the Dempster's method of evidential reasoning is employed that uses previously unavailable information such as the amount of ignorance and ambiguity a hypothesis exhibits. As such, the proposed vision system seems to be able to recognize a number of hardwood defects. This dissertation also explores wood texture as an additional feature in defect recognition, and contributes the first application of robust Spatial AutoRegressive modeling to wood texture analysis. Based on a correlation measure, two simple but efficient texture discrimination schemes are proposed. Incorporating a texture test in the scene analysis should improve the vision system's recognition power. As a pilot research, this dissertation has explored a number of important issues in creating a vision system for automated log inspection. Clearly, more work is needed to make the system more robust with additional species. Nevertheless, preliminary results seem to indicate that a machine vision system for automated hardwood log inspection can be developed.Ph. D.