Kurt Skifstad’s research while affiliated with University of Michigan and other places

Most research in computational stereo has followed the approach described in Barnard and Fischler’s “Computational Stereo” (in Computing Surveys‚ vol. 14, no. 4, 1982). This approach, although conceptually appealing and theoretically elegant, suffers from several limitations. Among these are the difficulties in the matching process, problems with feature localization, restrictive camera geometries, and, perhaps most importantly, the extensive computational effort required to produce depth estimates. By approaching the problem from more of an engineering perspective, a new paradigm for computational stereo had been developed that avoids the problems inherent in the conventional “extract and match” paradigm. The Intensity Gradient Analysis (IGA) technique determines depth values by analyzing temporal intensity gradients arising from the optic flow field induced by known camera motion. IGA assumes nothing about the nature of the environment and places no restrictions on camera orientation (IGA is applicable to sequences obtained using arbitrary translational motion).

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

Change detection plays a very important role in many vision applications. Most change detection algorithms assume that the illumination on a scene will remain constant. Unfortunately, this assumption is not necessarily valid outside a well-controlled laboratory setting. The accuracy of existing algorithms diminishes significantly when confronted with image sequences in which the illumination is allowed to vary. In this note, we present two techniques for change detection that have been developed to deal with the more general scenario where illuination is not assumed to be constant. A detailed description of both new methods, the derivative model method and the shading model method, is provided. Results are presented for applying each of the techniques discussed to various image pairs.

Conventional approaches to recovering depth from gray-level imagery have involved obtaining two or more images, applying an “interest” operator, and solving the correspondence problem. Unfortunately, the computational complexity involved in feature extraction and solving the correspondence problem makes existing techniques unattractive for many real-world robotic applications. By approaching the problem from more of an engineering perspective, we have developed a new depth recovery technique that completely avoids the computationally intensive steps of feature selection and correspondence required by conventional approaches. The Intensity Gradient Analysis technique (IGA) is a depth recovery algorithm that exploits 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. Peer Reviewed http://deepblue.lib.umich.edu/bitstream/2027.42/46054/1/138_2005_Article_BF01212370.pdf

If an autonomous vehilce is to operate in an environment of arbitrary complexity, it must be able to perceive the locations of the obstacles in its environment and store this information in a world model. It is important that the world model be structured in such a manner that the information may be easily utilized. An object-oriented data structure called Hyper-Pyramids is presented for representing the environment in the navigation problem. A summary of preliminary research efforts on the navigation problem is also presented. This includes work on the implementation and manipulation of octtree-like data structures, and the introduction of a new technique for the recovery of depth information from grey-scale images.

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