Active Contours is about the computer analysis of moving video images. It develops geometric and probabilistic models for shapes and their dynamics. The models are applied to the real-time analysis of shapes in motion, and addresses issues of learning, temporal filtering and the problems of visual clutter. Numerous applications are illustrated from computer graphics animation, user-interface design, medical imaging, automated surveillance and robotics.
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Preface, contents, glossary, index ps (0.3Mb) and pdf (0.2 Mb)
Introductory section ps (6.3 Mb) and pdf (1.1 Mb)
Section 1 - Geometric fundamentals ps (4.3Mb) and pdf (1.1 Mb)
Section 2 - Probabilistic modelling ps (4.7 Mb) and pdf (1.7 Mb)
Appendix ps (0.3 Mb) and
pdf (0.2 Mb)
Professor Demetri Terzopoulos, University of Toronto.
1. Introduction: organisation of the book, applications.
2. Active shape models: snakes, deformable templates, dynamic contours.
3. Spline curves: B-spline functions, finite bases, multiple knots, norm and inner product for spline functions, B-spline parametric curves, curves with vertices, control vector, norm for curves, areas and moments.
4. Shape-space models: representing transformations in shape-space, the space of Euclidean similarities, planar affine shape-space, norms and moments in a shape-space, perspective and weak perspective, three-dimensional affine shape-space, key-frames, articulated motion.
5. Image processing techniques for feature location: linear scanning, image filtering, using colour, correlation matching, background subtraction.
6. Fitting spline templates: regularised matching, normal displacement in curve fitting, recursive solution of curve-fitting problems, examples.
7. Pose recovery: calculating the pose of a planar object, pose recovery for three-dimensional objects, separation of rigid and non-rigid motion.
8: Probabilistic models of shape: probability distributions over curves, posterior distribution, probabilistic modelling of image features, validation gate, learning the prior, Principal Components Analysis (PCA).
9. Dynamical models: some simple dynamical prior distributions, first-order auto-regressive processes, limitations of first-order dynamical models, second-order dynamical models, second-order AR processes in shape-space, setting dynamical parameters.
10. Dynamic contour tracking: temporal fusion by Kalman filter tracking performance, choosing dynamical parameters, case study.
11. Learning motion: learning one-dimensional dynamics, learning AR process dynamics in shape-space, dynamical modes, performance of trained trackers.
12. Non-Gaussian models and random sampling algorithms: factored sampling, the Condensation algorithm, an observation model for Condensation, applications of the Condensation algorithm.
A. Mathematical background: vectors and matrices, B-spline basis functions, probability.
B. Stochastic dynamical systems: continuous-time first-order dynamics, second-order dynamics in continuous time, accuracy of learning.
C. Further shape-space models: recursive synthesis of shape-spaces.
Glossary of notation, bibliography, author index, index.