@InProceedings{stewart:bia05,
  author    = {Charles V.\ Stewart},
  title     = {Computer Vision Algorithms for Retinal Image Analysis: Current
               Results and Future Directions},
  booktitle = {Proceedings of the Workshop on Biomedical Image Analysis},
  year      = 2005,
  pages     = {31--50}
}

@InProceedings{yang:icvs06,
  author =	 {Gehua Yang and C.\ V.\ Stewart and M.\ Sofka and
                  Chia-Ling Tsai},
  title =	 {Automatic robust image registration system:
                  Initialization, estimation, and decision},
  booktitle =	 {Computer Vision Systems, 2006 ICVS '06. IEEE
                  International Conference on},
  year =	 2006,
  pages =	 {23--23},
  abstract =	 {Our goal is a highly-reliable, fully-automated image
                  registration technique that takes two images and
                  correctly aligns them or decides that they can not
                  be aligned. The technique should handle image pairs
                  having low overlap, variations in scale, large
                  illumination differences (e.g. day and night),
                  substantial scene changes, and different
                  modalities. Our approach is a combination of
                  algorithms for initialization, estimation and
                  refinement, and decision-making. It starts by
                  extracting and matching keypoints. Rank-ordered
                  matches are tested individually in succession. Each
                  is used to generate a similarity transformation
                  estimate in a small region of each image surrounding
                  the matched keypoints. A generalization of the
                  recently developed Dual-Bootstrap algorithm is then
                  applied to generate an image-wide transformation
                  estimate through a combination of matching and
                  reestimation, model selection, and region growing,
                  all driven by a new multiscale feature extraction
                  technique. After convergence of the Dual-Bootstrap,
                  the transformation is accepted if it passes a
                  correctness test that combines measures of accuracy,
                  stability and non-randomness; otherwise the process
                  starts over with the next keypoint
                  match. Experimental results on a suite of
                  challenging image pairs shows the effectivenss of
                  the complete system.},
  annote =	 {}
}