From the Publisher: 
The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

Computer vision : a modern approach = 计算机视觉 : 一种现代的方法

This purpose of this introductory paper is threefold. First, it introduces the Monte Carlo method with emphasis on probabilistic machine learning. Second, it reviews the main building blocks of modern Markov chain Monte Carlo simulation, thereby providing and introduction to the remaining papers of this special issue. Lastly, it discusses new interesting research horizons.

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An introduction to MCMC for machine learning

We present an example-based crowd simulation technique. Most crowd simulation techniques assume that the behavior exhibited by each person in the crowd can be defined by a restricted set of rules. This assumption limits the behavioral complexity of the simulated agents. By learning from real-world examples, our autonomous agents display complex natural behaviors that are often missing in crowd simulations. Examples are created from tracked video segments of real pedestrian crowds. During a simulation, autonomous agents search for examples that closely match the situation that they are facing. Trajectories taken by real people in similar situations, are copied to the simulated agents, resulting in seemingly natural behaviors.

Crowds by Example

We present a real-time crowd model based on continuum dynamics. In our model, a dynamic potential field simultaneously integrates global navigation with moving obstacles such as other people, efficiently solving for the motion of large crowds without the need for explicit collision avoidance. Simulations created with our system run at interactive rates, demonstrate smooth flow under a variety of conditions, and naturally exhibit emergent phenomena that have been observed in real crowds.

Continuum crowds

We present an algorithm to efficiently and robustly process collisions, contact and friction in cloth simulation. It works with any technique for simulating the internal dynamics of the cloth, and allows true modeling of cloth thickness. We also show how our simulation data can be post-processed with a collision-aware subdivision scheme to produce smooth and interference free data for rendering.

/pdf/robust-treatment-of-collisions-contact-and-friction-for-2ngobdlzjz.pdf

Robust treatment of collisions, contact and friction for cloth animation

Crowd simulation for virtual environments offers many challenges centered on the trade-offs between rich behavior, control and computational cost. In this paper we present a new approach to controlling the behavior of agents in a crowd. Our method is scalable in the sense that increasingly complex crowd behaviors can be created without a corresponding increase in the complexity of the agents. Our approach is also more authorable; users can dynamically specify which crowd behaviors happen in various parts of an environment. Finally, the character motion produced by our system is visually convincing. We achieve our aims with a situation-based control structure. Basic agents have very limited behaviors. As they enter new situations, additional, situation-specic behaviors are composed on the y to enable agents to respond appropriately. The composition is done using a probabilistic mechanism. We demonstrate our system with three environments including a city street and a theater.

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Scalable behaviors for crowd simulation

We present flow tiles, a novel technique for representing and designing velocity fields. Unlike existing procedural flow generators, tiling offers a natural user interface for field design. Tilings can be constructed to meet a wide variety of external and internal boundary conditions, making them suitable for inclusion in larger environments. Tiles offer memory savings through the re-use of prototypical elements. Each flow tile contains a small field and many tiles can be combined to produce large flows. The corners and edges of tiles are constructed to ensure continuity across boundaries between tiles. In addition, all our tiles and the resulting titing are divergence-free and hence suitable for representing a range of effects. We discuss issues that arise in designing flow tiles, algorithms for creating tilings, and three applications: a crowd on city streets, a river flowing between banks, and swirling fog. The first two applications use stationary fields, while the latter demonstrates a dynamic field.

Flow tiles

Traditional collision intensive multi-body simulations are difficult to control due to extreme sensitivity to initial conditions or model parameters Furthermore, there may be multiple ways to achieve any one goal, and it may be difficult to codify a user's preferences before they have seen the available solutions In this paper we extend simulation models to include plausible sources of uncertainty, and then use a Markov chain Monte Carlo algorithm to sample multiple animations that satisfy constraints A user can choose the animation they prefer, or applications can take direct advantage of the multiple solutions Our technique is applicable when a probability can be attached to each animation, with “good” animations having high probability, and for such cases we provide a definition of physical plausibility for animations We demonstrate our approach with examples of multi-body rigid-body simulations that satisfy constraints of various kinds, for each case presenting animations that are true to a physical model, are significantly different from each other, and yet still satisfy the constraints

/pdf/sampling-plausible-solutions-to-multi-body-constraint-3srjbqtrad.pdf

Sampling plausible solutions to multi-body constraint problems

We introduce Group Motion Graphs, a data-driven animation technique for groups of discrete agents, such as flocks, herds, or small crowds. Group Motion Graphs are conceptually similar to motion graphs constructed from motion-capture data, but have some important differences: we assume simulated motion; transition nodes are found by clustering group configurations from the input simulations: and clips to join transitions are explicitly constructed via constrained simulation. Graphs built this way offer known bounds on the trajectories that they generate, making it easier to search for particular output motions. The resulting animations show realistic motion at significantly reduced computational cost compared to simulation, and improved control.

Group motion graphs

Group behaviors are widely used in animation, yet it is difficult to impose hard constraints on their behavior. We describe a new technique for the generation of constrained group animations that improves on existing approaches in two ways: the agents in our simulations meet exact constraints at specific times, and our simulations retain the global properties present in unconstrained motion. Users can position constraints on agents' positions at any time in the animation, or constrain the entire group to meet center of mass or shape constraints. Animations are generated in a two stage process. The first step finds an initial set of trajectories that exactly meet the constraints, but which may violate the behavior rules. The second stage samples new animations that maintain the constraints while improving the motion with respect to the underlying behavioral model. We present a range of animations created with our system.

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Stephen Chenney

Papers

Scalable behaviors for crowd simulation

Flow tiles

Sampling plausible solutions to multi-body constraint problems

Group motion graphs

Constrained animation of flocks