Accurate 3D measurement using a structured light system

A method and system for probe-based pattern matching including an apparatus for synthetic training of a model of a pattern. The apparatus comprises a sensor for obtaining an image of the pattern and a processor for receiving the image of the pattern from the sensor and running a program. In the steps performed by the program a boundary of the pattern in the image is identified. A plurality of positive probes are placed at selected points along the boundary of the pattern and at least one straight segment of the boundary of the pattern is identified. The at least one straight segment of the boundary is extended to provide an imaginary straight segment and a plurality of negative probes are placed at selected points along the imaginary straight segment, where each negative probe has a negative weight.

Method for fast, robust, multi-dimensional pattern recognition

We have implemented a tabletop setup to explore augmented reality (AR) visualization. We call this setup an "augmented workspace". The user sits at the table and performs a manual task, guided by computer graphics overlaid on to his view. The setup serves as a testbed for developing the technology and for studying visual perception issues. The user wears a custom video see-through head mounted display (HMD). Two color video cameras attached to the HMD provide a stereo view of the scene, and a third video camera is added for tracking. The system runs at the full 30-Hz video frame rate with a latency of about 0.1 s, generating a stable augmentation with no apparent jitter visible in the composite images. Two SGI Visual Workstations provide the computing power for the system. In this paper, we describe the augmented workspace system in more detail and discuss several design issues.

Augmented workspace: designing an AR testbed

While laparoscopes are used for numerous minimally invasive (MI) procedures, MI liver resection and ablative surgery is infrequently performed. The paucity of cases is due to the restriction of the field of view by the laparoscope and the difficulty in determining tumor location and margins under video guidance. By merging MI surgery with interactive, image-guided surgery (IIGS), the authors hope to overcome localization difficulties present in laparoscopic liver procedures. One key component of any IIGS system is the development of accurate registration techniques to map image space to physical or patient space. This manuscript focuses on the accuracy and analysis of the direct linear transformation (DLT) method to register physical space with laparoscopic image space on both distorted and distortion-corrected video images. Experiments were conducted on a liver-sized plastic phantom affixed with 20 markers at various depths, After localizing the points in both physical and laparoscopic image space, registration accuracy was assessed for different combinations and numbers of control points (n) to determine the quantity necessary to develop a robust registration matrix. For n=11, average target registration error (TRE) was 0.70/spl plusmn/0.20 mm. The authors also studied the effects of distortion correction on registration accuracy For the particular distortion correction method and laparoscope used in the authors' experiments, there was no statistical significance between physical to image registration error for distorted and corrected images. In cases where a minimum number of control points (n=6) are acquired, the DLT is often not stable and the mathematical process can lead to high TRE values. Mathematical filters developed through the analysis of the DLT were used to prospectively eliminate outlier cases where the TRE was high. For n=6, prefilter average TRE was 17.4/spl plusmn/153 mm for all trials; when the filters were applied, average TRE decreased to 1.64/spl plusmn/1.10 mm for the remaining trials.

Registration of physical space to laparoscopic image space for use in minimally invasive hepatic surgery

A method is disclosed for finding a deformed pattern in an image using a plurality of sub-patterns. By advantageously restricting sub-pattern search ranges, search speed is improved, and the incidence of spurious matches is reduced. The method also quickly decides which sub-pattern result, of several potential candidates, is most likely to be the correct match for a deformed sub-pattern. Also, a method is provided for characterizing a deformed pattern in an image by using results from feature-based search tools to create a mapping that models the deformation of the pattern. A transform, selectable by a user, is fit to the results from the search tools to create a global deformation mapping. This transformation is fit only to feature points derived from matches resulting from successful sub-pattern search, without including data from areas of the pattern that were blank, not matched, or otherwise didn't contain information about the pattern's distorted location.

Methods for finding and characterizing a deformed pattern in an image

This paper provides a review of a number of subpixel estimators classified as moment based, local modelling and reconstruction. Three algorithms are described in detail, one from each class. In the first, the basic centroid method is generalized so that it is applicable to a wider class of problems and the general formulation is applied to develop a subpixel ridge estimator. The second algorithm is a restricted polynomial model and is developed based on the assumption that an edge profile remains invariant in a local neighborhood. The third algorithm uses Gaussian interpolation to perform local image reconstruction. Simulations are performed to measure the performance of these three algorithms under ideal and noisy conditions.© (1994) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

P. Wayne Power

Papers

Evaluation of subpixel feature localization methods for precision measurement