Publications | Perceiving Systems - Max Planck Institute for Intelligent Systems

8 results (View BibTeX file of all listed publications)

2009

ISocRob-MSL 2009 Team Description Paper for Middle Sized League

Lima, P., Santos, J., Estilita, J., Barbosa, M., Ahmad, A., Carreira, J.

13th Annual RoboCup International Symposium 2009, July 2009 (techreport)

Abstract

This paper describes the status of the ISocRob MSL roboticsoccer team as required by the RoboCup 2009 qualiﬁcation procedures.Since its previous participation in RoboCup, the ISocRob team has car-ried out signiﬁcant developments in various topics, the most relevantof which are presented here. These include self-localization, 3D objecttracking and cooperative object localization, motion control and rela-tional behaviors. A brief description of the hardware of the ISocRobrobots and of the software architecture adopted by the team is also in-cluded.

[BibTex]

2009

Lima, P., Santos, J., Estilita, J., Barbosa, M., Ahmad, A., Carreira, J. ISocRob-MSL 2009 Team Description Paper for Middle Sized League 13th Annual RoboCup International Symposium 2009, July 2009 (techreport)

[BibTex]

An introduction to Kernel Learning Algorithms

Gehler, P., Schölkopf, B.

In Kernel Methods for Remote Sensing Data Analysis, pages: 25-48, 2, (Editors: Gustavo Camps-Valls and Lorenzo Bruzzone), Wiley, New York, NY, USA, 2009 (inbook)

Abstract

Kernel learning algorithms are currently becoming a standard tool in the area of machine learning and pattern recognition. In this chapter we review the fundamental theory of kernel learning. As the basic building block we introduce the kernel function, which provides an elegant and general way to compare possibly very complex objects. We then review the concept of a reproducing kernel Hilbert space and state the representer theorem. Finally we give an overview of the most prominent algorithms, which are support vector classification and regression, Gaussian Processes and kernel principal analysis. With multiple kernel learning and structured output prediction we also introduce some more recent advancements in the field.

link (url) DOI [BibTex]

Gehler, P., Schölkopf, B. An introduction to Kernel Learning Algorithms In Kernel Methods for Remote Sensing Data Analysis, pages: 25-48, 2, (Editors: Gustavo Camps-Valls and Lorenzo Bruzzone), Wiley, New York, NY, USA, 2009 (inbook)

link (url) DOI [BibTex]

Visual Object Discovery

Sinha, P., Balas, B., Ostrovsky, Y., Wulff, J.

In Object Categorization: Computer and Human Vision Perspectives, pages: 301-323, (Editors: S. J. Dickinson, A. Leonardis, B. Schiele, M.J. Tarr), Cambridge University Press, 2009 (inbook)

link (url) [BibTex]

Sinha, P., Balas, B., Ostrovsky, Y., Wulff, J. Visual Object Discovery In Object Categorization: Computer and Human Vision Perspectives, pages: 301-323, (Editors: S. J. Dickinson, A. Leonardis, B. Schiele, M.J. Tarr), Cambridge University Press, 2009 (inbook)

link (url) [BibTex]

Automatic recognition of rodent behavior: A tool for systematic phenotypic analysis

Serre, T.*, Jhuang, H*., Garrote, E., Poggio, T., Steele, A.

CBCL paper #283/MIT-CSAIL-TR #2009-052., MIT, 2009 (techreport)

pdf [BibTex]

Serre, T.*, Jhuang, H*., Garrote, E., Poggio, T., Steele, A. Automatic recognition of rodent behavior: A tool for systematic phenotypic analysis CBCL paper #283/MIT-CSAIL-TR #2009-052., MIT, 2009 (techreport)

pdf [BibTex]