The authors have developed a depth-recovery technique that completely avoids the computationally intensive steps of feature selection and correspondence required by conventional approaches. The intensity gradient analysis (IGA) technique is a depth-recovery algorithm that utilizes the properties of the MCSO (moving camera, stationary objects) scenario. Depth values are obtained by analyzing temporal intensity gradients arising from the optic flow field induced by known camera motion. In doing so, IGA avoids the feature extraction and correspondence steps of conventional approaches and is therefore very fast. A detailed description of the algorithm is provided along with experimental results from complex laboratory scenes. It is suggested that the most appealing property of this approach is that IGA places little burden on computational resources, and therefore seems ideally suited for real-world robotic applications.>

The task of automating the visual inspection of pin-in-hole solder joints is addressed. Two approaches are explored: statistical pattern recognition and expert systems. An objective dimensionality-reduction method is used to enhance the performance of traditional statistical pattern recognition approaches by decorrelating feature data, generating feature weights, and reducing run-time computations. The expert system uses features in a manner more analogous to the visual clues that a human inspector would rely on for classification. Rules using these cues are developed, and a voting scheme is implemented to accumulate classification evidence incrementally. Both methods compared favorably with human inspector performance.>