A pure learning approach to background-invariant object recognition using pedagogical support vector learning

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn 2, Issue , 2001, Pages

  Roobaert, Danny ^a   Zillich, Michael ^a   Eklundh, Jan Olof ^a  

^a ROYAL INSTITUTE OF TECHNOLOGY   (Sweden)

Author keywords

[No Author keywords available]

Indexed keywords

DATABASE SYSTEMS; IMAGE SEGMENTATION; LEARNING SYSTEMS; PROBLEM SOLVING; REAL TIME SYSTEMS; TEACHING;

SUPPORT VECTOR LEARNING;

OBJECT RECOGNITION;

EID: 0035684730     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (22)

1. Comparison of view-based object recognition algorithms using realistic 3D models
2. A training algorithm for optimal margin classifiers
3. Finding waldo, or focus of attention using local color information
4. Visual learning and recognition of 3-D objects from appearance
5. Training support vector machines: An application to face detection
6. A general framework for object detection
7. Support vector machines for 3D object recognition
8. Improving the generalisation of linear support vector machines: An application to 3D object recognition with cluttered background
9. DirectSVM: A fast and simple support vector machine perceptron
10. Learning to recognize objects
11. Neural network-based face detection
12. Support vector machine - Reference manual
13. Recognition without correspondence using multidimensional receptive field histograms
14. A statistical method for 3D object detection applied to faces and cars
15. Incorporating invariances in support vector learning machines
16. Example-based learning for view-based human face detection
17. Color indexing
18. Eigenfaces for recognition
19. Statistical learning theory