The volume spans the whole range of algorithms for geometric vision. The authors and volume editors succeeded in providing added value beyond a mere collection of papers and made the volume a state-of-the-art survey of their field.

Explains the success of Nearest Neighbor Methods in Prediction, both in theory and in practice.

Practitioners in these and related fields will find this book perfect for self-study as well.

This book constitutes the thoroughly refereed post-proceedings of the Second European Workshop on 3D Structure from Multiple Images of Large-Scale Environments, SMILE 2000, held in Dublin, Ireland in July 2000.

This book explores and introduces the latter elements through an incremental complexity approach at the same time where CVPR problems are formulated and the most representative algorithms are presented.

Comprehensive background material is provided, so readers familiar with linear algebra and basic numerical methods can understand the projective geometry and estimation algorithms presented, and implement the algorithms directly from the ...

This book provides uninitiated readers with a feeling for the principles, applications, and analysis of kernel smoothers. This is facilita

The book describes a complete method that starts from a query image and an image database and yields a list of the images in the database containing shapes present in the query image. A false alarm number is associated to each detection.

This two-volume handbook presents a collection of novel methodologies with applications and illustrative examples in the areas of data-driven computational social sciences.

And, generalizing beyond training data, models thus learned may be used for preference prediction. This is the first book dedicated to this topic, and the treatment is comprehensive.