Abstract
This final chapter provides an introduction into classification and learning, with a detailed description of basic AdaBoost and the use of random forests. These concepts are illustrated by applications for face detection, and for pedestrian detection, respectively.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
In classification theory, a descriptor is usually also called a feature. A feature in an image that, as commonly used in image analysis, combines a keypoint and a descriptor. Thus, we continue to use “descriptor” rather than “feature” for avoiding confusion.
- 2.
This is the error defined in margin-based classifiers such as support vector machines. This error is (usually) not explicitly used in AdaBoost.
- 3.
Shannon’s entropy corresponds to minus the entropy used in thermodynamics.
- 4.
The tree diagram has its root at the top (as is customary). For people who complain that it is misleading to depict a tree with its root at the top, here are two examples: Inside the large Rikoriko Cave in The Poor Knights Islands (New Zealand) some trees are growing down from the roof, and on coastal cliffs around northern New Zealand many large Pohutukawa trees are rooted at the edge of a cliff, with almost all of the tree sprawlings down the cliff, lower than its root.
- 5.
The TUD Multiview Pedestrians database is available at www.d2.mpi-inf.mpg.de/node/428 for free download.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag London
About this chapter
Cite this chapter
Klette, R. (2014). Object Detection. In: Concise Computer Vision. Undergraduate Topics in Computer Science. Springer, London. https://doi.org/10.1007/978-1-4471-6320-6_10
Download citation
DOI: https://doi.org/10.1007/978-1-4471-6320-6_10
Publisher Name: Springer, London
Print ISBN: 978-1-4471-6319-0
Online ISBN: 978-1-4471-6320-6
eBook Packages: Computer ScienceComputer Science (R0)