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  1. IEEE Symposium on Computational Intelligence for Multimedia Signal and Vision Processing, CIMSVP.
  2. 2009 IEEE Symposium on Computational Intelligence for Multimedia Signal and Vision Processing
  3. A car detection system based on hierarchical visual features
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2009 IEEE Symposium on Computational Intelligence for Multimedia Signal and Vision Processing
Cover
Copyright
Table of Contents
Tutorial CIMSVP-T advanced image processing
A model of angle selectivity in area V2 with local divisive normalization
Tactile or visual?: Stimulus characteristics determine receptive field type in a self-organizing map model of cortical development
Maximizing neural responses leads to sensori-motor coordination of binocular vergence
Cortical columns: Building blocks for intelligent systems
Character recognition with two spiking neural network models on multicore architectures
A car detection system based on hierarchical visual features
Recurrent network-based face recognition using image sequences
3D volume extraction of densely packed cells in EM data stack by forward and backward graph cuts
A hybrid method for facial recognition systems
Binary image registration using cellular simultaneous recurrent networks
Quality-aware selection of quality factor and scaling parameters in JPEG image transcoding
Relevance tuning in content-based retrieval of structurally-modeled images using Particle Swarm Optimization
Vision-inertial tracking system for robust fiducials registration in augmented reality
Image registration for sequence of visual images captured by UAV
Author index

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A car detection system based on hierarchical visual features

Content Provider IEEE Xplore Digital Library
Author Tivive, F.H.C. Bouzerdoum, A.
Copyright Year 2009
Description Author affiliation: School of Electrical, Computer and Telecommunications Engineering, University of Wollongong, Northfields Ave, NSW, 2522, Australia (Tivive, F.H.C.; Bouzerdoum, A.)
Abstract In this paper, we address the problem of detecting and localizing cars in still images. The proposed car detection system is based on a hierarchical feature detector in which the processing units are shunting inhibitory neurons. To reduce the training time and complexity of the network, the shunting inhibitory neurons in the first layer are implemented as directional nonlinear filters, whereas the neurons in the second layer have trainable parameters. A multi-resolution processing scheme is implemented so as to detect cars of different sizes, and to reduce the number of false positives during the detection stage, an adaptive thresholding strategy is developed. Tested on the UIUC car database, the proposed method achieves better classification results than some of the existing car detection approaches.
Starting Page 35
Ending Page 40
File Size 961733
Page Count 6
File Format PDF
ISBN 9781424427710
DOI 10.1109/CIMSVP.2009.4925645
Language English
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date 2009-03-30
Publisher Place USA
Access Restriction Subscribed
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword Support vector machines Computer vision Neurons Support vector machine classification Detectors Feature extraction Brain modeling Pattern recognition Gabor filters Data mining
Content Type Text
Resource Type Article
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