Stochastic Algorithms for Visual Tracking by John Maccormick
A central problem in computer vision is to track objects as they move and deform in a video sequence. Stochastic algorithms -- in particular, particle filters and the Condensation algorithm -- have dramatically enhanced the state of the art for such visual tracking problems in recent years. This book presents a unified framework for visual tracking using particle filters, including the new technique of partitioned sampling which can alleviate the curse of dimensionality suffered by standard particle filters. The book also introduces the notion of contour likelihood: a collection of models for assessing object shape, colour and motion, which are derived from the statistical properties of image features. Because of their statistical nature, contour likelihoods are ideal for use in stochastic algorithms. A unifying theme of the book is the use of statistics and probability, which enable the final output of the algorithms presented to be interpreted as the computer's belief about the state of the world. The book will be of use and interest to students, researchers and practitioners in computer vision, and assumes only an elementary knowledge of probability theory.-
Accurate Visual Metrology from Single and Multiple Uncalibrated Images
-
Image Mosaicing and Super-resolution
-
Asynchronous System-on-Chip Interconnect
-
User-Developer Cooperation in Software Development
-
Strength or Accuracy: Credit Assignment in Learning Classifier Systems
-
Theorem Proving with the Real Numbers
-
Hardware Evolution
-
Extensional Constructs in Intensional Type Theory
-
Models of Sharing Graphs
-
Randomized Algorithms: Approximation, Generation, and Counting
-
Architecture-Independent Loop Parallelisation
-
Large Scale Collaborative Virtual Environments
-
Games and Full Abstraction for a Functional Metalanguage with Recursive Types
-
Automated Theory Formation in Pure Mathematics
-
Inheritance Relationships for Disciplined Software Construction
-
Dynamic Flexible Constraint Satisfaction and its Application to AI Planning
-
A Combination of Geometry Theorem Proving and Nonstandard Analysis with Application to Newton's Principia
John MacCormick is a well-known computer science researcher and educator. He holds a PhD in computer vision from Oxford University, has worked in Hewlett-Packard and Microsoft research labs, and is currently a professor of computer science at Dickinson College.
| SKU | Unavailable |
| ISBN 13 | 9781447111764 |
| ISBN 10 | 1447111761 |
| Title | Stochastic Algorithms for Visual Tracking |
| Author | John Maccormick |
| Series | Distinguished Dissertations |
| Condition | Unavailable |
| Binding Type | Paperback |
| Publisher | Springer London Ltd |
| Year published | 2011-09-16 |
| Number of pages | 174 |
| Cover note | Book picture is for illustrative purposes only, actual binding, cover or edition may vary. |
| Note | Unavailable |