Probabilistic Search for Object Segmentation and Recognition

\bibitem{Ballard81}

{\sc Ballard, D.~H.}

\newblock Generalizing the {Hough} transform to detect arbitrary shapes.

\newblock {\em Pattern Recognit. 13\/} (1981), 111--122.

\bibitem{Besl&Jain85a}

{\sc Besl, P., and Jain, R.}

\newblock Intrinsic and extrinsic surface characteristics.

\newblock In {\em Proc.\ IEEE Conf.\ Comp.\ Vision Patt.\ Recogn.\/} (1985),

pp.~226--233.

\bibitem{Besl&Jain85b}

{\sc Besl, P., and Jain, R.}

\newblock Three-dimensional object recognition.

\newblock {\em ACM Comput.\ Surveys 17\/} (1985), 75--145.

\bibitem{Faugeras&Hebert87}

{\sc Faugeras, O.~D., and Hebert, M.}

\newblock The representation, recognition, and positioning of {3-D} shapes from

range data.

\newblock In {\em Three-Dimensional Machine Vision}, T.~Kanade, Ed. Kluwer

Academic Publishers, 1987, pp.~301--353.

\bibitem{Fischler&Bolls81}

{\sc Fischler, M., and Bolles, R.}

\newblock Random sample consensus: {A} paradigm for model fitting with

application to image analysis and automated cartography.

\newblock {\em Comm.\ Assoc.\ Comp.\ Mach. 24\/} (1981), 381--395.

\bibitem{Fleuret&Geman99}

{\sc Fleuret, F., and Geman, D.}

\newblock Graded learning for object detection.

\newblock In {\em Proc.\ IEEE Workshop Stat.\ Comput.\ Theo.\ Vision\/} (1999).

\bibitem{Goad83}

{\sc Goad, C.}

\newblock Special purpose automatic programming for {3D} model-based vision.

\newblock In {\em Proc.\ ARPA Image Understanding Workshop\/} (1983).

\bibitem{Grimson90}

{\sc Grimson, W. E.~L.}

\newblock {\em Object Recognition by Computer: {The} Role of Geometric

Constraints}.

\newblock MIT Press, Cambridge, MA, 1990.

\bibitem{Huttenlocher&Ullman87}

{\sc Huttenlocher, D.~P., and Ullman, S.}

\newblock Object recognition using alignment.

\newblock In {\em Proc.\ Intern.\ Conf.\ Comput.\ Vision\/} (1987),

pp.~102--111.

\bibitem{Huttenlocher&Ullman90}

{\sc Huttenlocher, D.~P., and Ullman, S.}

\newblock Recognizing solid objects by alignment with an image.

\newblock {\em Int.\ J.\ Computer Vision 5\/} (1990), 195--212.

\bibitem{Clemens&Jacobs91}

{\sc Jacobs, D., and Clemens, D.}

\newblock Space and time bounds on indexing {3-D} models from {2-D} images.

\newblock {\em IEEE Trans.\ Patt.\ Anal.\ Mach.\ Intell. 13\/} (1991),

1007--1017.

\bibitem{Kuno_etal91}

{\sc Kuno, Y., Okamoto, Y., and Okada, S.}

\newblock Robot vision using a feature search strategy generated from a {3-D}

object model.

\newblock {\em IEEE Trans.\ Patt.\ Anal.\ Machine Intell. 13\/} (1991),

1085--1097.

\bibitem{Lamdan&Wolfson88}

{\sc Lamdan, Y., and Wolfson, H.~J.}

\newblock Geometric hashing: a general and efficient model-based recognition

scheme.

\newblock In {\em Proc.\ Intern.\ Conf.\ Comput.\ Vision\/} (1988),

pp.~238--249.

\bibitem{Lowe85}

{\sc Lowe, D.~G.}

\newblock {\em Perceptual Organization and Visual Recognition}.

\newblock Kluwer, Boston, 1985.

\bibitem{Lowe90}

{\sc Lowe, D.~G.}

\newblock Visual recognition as probabilistic inference from spatial relations.

\newblock In {\em {AI} and the Eye}, A.~Blake and T.~Troscianko, Eds. John

Wiley and Sons Ltd., New York, 1990, pp.~261--279.

\bibitem{Moss_etal99}

{\sc Moss, S., Wilson, R.~C., and Hancock, E.~R.}

\newblock A mixture model for pose clustering.

\newblock {\em Patt.\ Recogn.\ Let. 20\/} (1999), 1093--1101.

\bibitem{Stockman87}

{\sc Stockmann, G.}

\newblock Object recognition and localization via pose clustering.

\newblock {\em CVGIP 40\/} (1987), 361--387.

\bibitem{Stokely&YouWu92}

{\sc Stokely, E.~M., and Wu, S.~Y.}

\newblock Surface parameterization and curvature measurement of arbitrary {3-D}

objects: {Five} practical methods.

\newblock {\em IEEE Trans.\ Patt.\ Anal.\ Mach.\ Intell. 14\/} (1992),

833--840.

\bibitem{Torr&Zisserman00}

{\sc Torr, P. H.~S., and Zisserman, A.}

\newblock {MLESAC}: {A} new robust estimator with application to estimating

image geometry.

\newblock {\em Comput.\ Vision Image Understanding 78\/} (2000), 138--156.

\bibitem{Wolfson90}

{\sc Wolfson, H.~J.}

\newblock Model-based object recognition by geometric hashing.

\newblock In {\em Proc.\ Eur.\ Conf.\ Comput.\ Vision\/} (1990), pp.~526--536.