The group extended the model to Dynamic Hidden-State Shape Models (DHSSMs) to track and recognize the non-rigid motion of objects with structural shape variation, in particular, human hands. Betke's student Zheng Wu conducted experiments that showed that the proposed method can recognize the digits of a hand while the fingers are being moved and curled to various degrees. The method was also shown to be robust to various illumination conditions, the presence of clutter, occlusions, and some types of self-occlusions.

J. Wang, V. Athitsos, S. Sclaroff, and M. Betke. "Detecting Objects of Variable Shape Structure with Hidden State Shape Models."IEEE Transactions on Pattern Analysis and Machine Intelligence.30(3):477-492, March 2008. Abstract, pdf. Videos.Z. Wu, M. Betke, J. Wang, V. Athitsos, and S. Sclaroff, "Tracking with Dynamic Hidden-State Shape Models,"

Computer Vision - ECCV 2008, 10th European Conference on Computer Vision, Marseille, France, October 12-18, 2008, Proceedings, Part I, LNCS 5302,D. Forsyth, P. Torr, and A. Zisserman (editors), pages 643-656, Springer-Verlag. pdf.

J. Wang, E. Gu, and M. Betke. "MosaicShape: Stochastic Region Grouping with Shape Prior." Proceedings of theIEEE Computer Society International Conference on Computer Vision and Pattern Recognition (CVPR 2005), pp. 902-908, San Diego, CA, USA, June 2005. Abstract. Pdf file.

S. Epstein and M. Betke. Active Hidden Models for Tracking with Kernel Projections, Department of Computer Science Technical Report BUCS-2009-006, Boston University. March 10, 2009. Abstract. pdf. ps.

S. Epstein and M. Betke. The Kernel Semi-least Squares Method for Distance Approximation. Manuscript under review. September 2010.

S. Epstein, E. Missimer, and M. Betke. Improving the Camera Mouse with the Kernel-Subset-Tracker. Unpublished manuscript. September 2010.

M. Betke, E. Naftali and N. C. Makris, "Necessary Conditions to Attain Performance Bounds on Structure and Motion Estimates of Rigid Objects,"

Proceedings of the IEEE Computer Vision and Pattern Recognition Conference (CVPR 2001)Vol. 1, pp. 448-455, Kauai, Hawaii, December 2001. pdf.

The problem of recognizing objects subject to affine transformation in images is examined from a physical perspective using the theory of statistical estimation. Focusing first on objects that occlude zero-mean scenes with additive noise, we derive the Cramer-Rao lower bound on the mean-square error in an estimate of the six-dimensional parameter vector that describes an object subject to affine transformation and so generalize the bound on one-dimensional position error previously obtained in radar and sonar pattern recognition. We then derive two useful descriptors from the object?s Fisher information that are independent of noise level. The first is a generalized coherence scale that has great practical value because it corresponds to the width of the object?s autocorrelation peak under affine transformation and so provides a physical measure of the extent to which an object can be resolved under affine parameterization. The second is a scalar measure of an object?s complexity that is invariant under affine transformation and can be used to quantitatively describe the ambiguity level of a general 6-dimensional affine recognition problem. This measure of complexity has a strong inverse relationship to the level of recognition ambiguity. We then develop a method for recognizing objects subject to affine transformation imaged in thousands of complex real-world scenes. Our method exploits the resolution gain made available by the brightness contrast between the object perimeter and the scene it partially occludes. The level of recognition ambiguity is shown to decrease exponentially with increasing object and scene complexity. Ambiguity is then avoided by conditioning the permissible range of template complexity above a priori thresholds. Our method is statistically optimal for recognizing objects that occlude scenes with zero-mean background.

M. Betke and N. Makris, "Recognition, Resolution and Complexity of Objects Subject to Affine Transformation."

International Journal of Computer Vision, 44:1, pp. 5-40, August 2001. pdf.M. Betke and N. Makris, "Information-Conserving Object Recognition." Proceedings of the Sixth International Conference on Computer Vision, pp. 145-152, Bombay, India, January 1998. pdf.

M. Betke and N. Makris, "Fast Object Recognition in Noisy Images Using Simulated Annealing." Proceedings of the Fifth International Conference on Computer Vision, pp. 523-530, June 1995. pdf.