## Spatial Computing: Issues in Vision, Multimedia and Visualization TechnologiesThis book is the result of a special workshop on Spatial Computing which brought together experts in computer vision, visualization, multimedia and geographic information systems to discuss common problems and applications. The common theme of the workshop was the need to integrate human perception and domain knowledge with developing representations and solutions to problems which necessarily involve the interpretation of sensed data. The overwhelming conclusion was that these different areas of spatial computing should be communicating more than is done at present and that such workshops and publications would help this process. |

### What people are saying - Write a review

We haven't found any reviews in the usual places.

### Contents

Foreword | 1 |

Inspiration From Insects | 35 |

Assessing Feature Importance in the Context of Object Recognition | 49 |

Analysis Optimization and Control | 79 |

Using Aspect Graphs to Control the Recovery and Tracking | 115 |

The Role of Machine Learning in Building Image Interpretation | 143 |

Recent Advances in Graph Matching | 169 |

Cooperative Spatial Reasoning for Image Understanding | 205 |

Human Understanding Limits in Visualization | 229 |

A Strategy and Architecture for the Visualization of Complex | 239 |

The Problem of Paradigms | 263 |

A Simple Video Reuse Application | 275 |

Conceptual Representation for Multimedia Information | 303 |

R W Smith D Kieronska S Venkatesh | 312 |

### Other editions - View all

### Common terms and phrases

adjacency matrices agent algorithm application approach aspect assigned Bayesian bees binary features boundary channels classification clips cluster complex Computer Vision concept Conf constraints contour database decision tree defined deformable models described distance Distance transform edge detection environment error estimation evidence vectors example feature extraction frame function Gaussian geometric global graph isomorphism graph matching HLPC hypothesis IEEE IEEE Trans image processing input graph interaction iteration Kalman filter knowledge labels layers LLCCs machine learning Markov random fields matrix method metric model graphs motion nodes noise number of model number of vertices object recognition optimization paradigm parameters Pattern Recognition permutation permutation matrix pixels pose problem Proc query relations relationships representation represented robot rules scale scene segmentation sequence spatial reasoning string structure subgraph isomorphism Table task techniques temporary region tunnel unary variables vertex visual attributes