IEEE Conference on Computer Vision and Pattern Recognition 2000, Volume 1, pages, JuneAlpha Estimation in Natural Images Mark A. Ruzon Carlo Tomasi

Source URL: www.cs.duke.edu

19th Portuguese Conference on Pattern Recognition (RecPadLisbon, Portugal, 1 November 2013 Autonomous Learning of Tool Affordances Afonso Gonçalves

Source URL: www.poeticon.eu

Graph-based Kinship Recognition Yuanhao Guo∗† , Hamdi Dibeklio˘glu∗ , Laurens van der Maaten∗ ∗ Pattern Recognition & Bioinformatics Group, Delft University of Technology, The Netherlands † Section Imaging &

Source URL: lvdmaaten.github.io

RGB-D Tracking and Reconstruction for TV Broadcasts Tommi Tykk¨al¨a1 , Hannu Hartikainen2 , Andrew I. Comport3 and Joni-Kristian K¨am¨ar¨ainen4 1 Machine Vision and Pattern Recognition Laboratory, Lappeenranta Unive

Source URL: personal.lut.fi

Pattern Recognition Letters–346 www.elsevier.com/locate/patrec Graphical models for graph matching: Approximate models and optimal algorithms Terry Caelli *, Tiberio Caetano

Source URL: www.tiberiocaetano.com

Q IWA Publishing 2010 Journal of Hydroinformatics | 12.3 | Assessing water renewal of the northern coastal zone in China using a variable fuzzy pattern recognition model

Source URL: www.iwaponline.com

Identification of Occupant State Parameters using a Pattern Recognition Methodology to Reduce the Risk of Injury in a Collision Primary Author: Thomas Adam Academic Department: Mechanical and Aerospace Engineering Instit

Source URL: ibrc.osu.edu

―Original― Pattern Recognition Analysis of Proton Nuclear Magnetic Resonance Spectra of Extracts of Intestinal Epithelial Cells under Oxidative Stress Keiji Nakata1, Norio Sato2, Keiko Hirakawa3, Takayuki Asakura1,

Source URL: www.camo.com

• Antiseptics
• Disinfectants
• Hydrogen peroxide
• Otologicals
• Nuclear magnetic resonance spectroscopy
• Nuclear magnetic resonance
• Cellular differentiation
• Principal component analysis
• Chemistry
• Biology
• Statistics

Psychol Res:Research Psychological chologischeForschung

Source URL: cvc.psy.ku.dk

• Vision
• Optics
• Template matching
• Pattern recognition
• Object recognition
• Centroid
• Pattern
• Image processing
• Geometry
• Computer vision

An Instance Selection Approach to Multiple Instance Learning Zhouyu Fu∗ 1 and Antonio Robles-Kelly2,3 1 Gippsland School of IT, Monash University, Churchill, VIC 3842, Australia 2 National ICT Australia †, Locked Bag

Source URL: users.cecs.anu.edu.au

• Artificial intelligence
• Support vector machine
• Multiple-instance learning
• Multiclass classification
• Pruning
• Supervised learning
• Pattern recognition
• Boosting methods for object categorization
• Machine learning
• Statistics
• Statistical classification