Authors:
Anselm Haselhoff
and
Anton Kummert
Affiliation:
University of Wuppertal, Germany
Keyword(s):
Feature extraction, Sampling, AdaBoost.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computer Vision, Visualization and Computer Graphics
;
Data Manipulation
;
Early Vision and Image Representation
;
Feature Extraction
;
Features Extraction
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Image and Video Analysis
;
Image Filtering
;
Image Formation and Preprocessing
;
Implementation of Image and Video Processing Systems
;
Informatics in Control, Automation and Robotics
;
Methodologies and Methods
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing, Sensors, Systems Modeling and Control
;
Soft Computing
Abstract:
In this work a sampling scheme for filter-based feature extraction in the field of appearance-based object detection is analyzed. Optimized sampling radically reduces the number of features during the AdaBoost training process and better classification performance is achieved. The signal energy is used to determine an appropriate sampling resolution which then is used to determine the positions at which the features are calculated. The advantage is that these positions are distributed according to the signal properties of the training images.
The approach is verified using an AdaBoost algorithm with Haar-like features for vehicle detection. Tests of classifiers, trained with different resolutions and a sampling scheme, are performed and the results are presented.