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About this product
- DescriptionDescribing some of the probabilistic machine learning methods, this work uses Maximum Likelihood framework, Bayesian Networks, and Hidden Markov models. Designed for both experts and novices in computer vision, it focuses on three aspects: features, similarity metric, and models.
- Author BiographyNicu Sebe received his PhD degree from Leiden University in 2001. Currently, he is an Assistant Professor at Leiden University in the Netherlands. His main interest is in the fields of computer vision and pattern recognition, in particular content-based retrieval and robust techniques in computer vision. He was co-editing the proceedings of the International Conference on Image and Video Retrieval 2002. He is also acting as the technical program co-chair for the International Conference on Image and Video Retrieval 2003. Michael S. Lew received his PhD degree in Electrical Engineering from the University of Illinois at Urbana-Champaign. He is currently an Associate Professor at Leiden University in the Netherlands. He has published over 100 scientific papers and helped organize several large conferences including IEEE Multimedia, ACM Multimedia, and the International Conference on Image and Video Retrieval.
- Author(s)Michael S. Lew,Nicu Sebe
- PublisherKluwer Academic Publishers
- Date of Publication01/04/2003
- GenreComputing: Professional & Programming
- Series TitleComputational Imaging and Vision
- Series Part/Volume Numberv.26
- Country of PublicationUnited States
- ImprintKluwer Academic Publishers
- Content Notebiography
- Weight504 g
- Width210 mm
- Height297 mm
- Spine14 mm
- Format DetailsLaminated cover
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