Robust color contour object detection invariant to shadows

Authors:
Jorge Scandaliaris;Michael Villamizar;Juan Andrade-Cetto;Alberto Sanfeliu
Affiliations:
Institut de Robòtica i Informàtica Industrial, UPC, CSIC;Institut de Robòtica i Informàtica Industrial, UPC, CSIC;Institut de Robòtica i Informàtica Industrial, UPC, CSIC;Institut de Robòtica i Informàtica Industrial, UPC, CSIC and Dept. System Engineering and Automation, Universitat Politècnica de Catalunya, Barcelona, Spain
Venue:
CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications
Year:
2007

Citing 5
Cited 1

Cast shadow segmentation using invariant color features

Computer Vision and Image Understanding
Contour-Based Learning for Object Detection

ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Computation of Rotation Local Invariant Features using the Integral Image for Real Time Object Detection

ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Classifying color edges in video into shadow-geometry, highlight, or material transitions

IEEE Transactions on Multimedia
Physical models for moving shadow and object detection in video

IEEE Transactions on Pattern Analysis and Machine Intelligence

Combining color-based invariant gradient detector with hog descriptors for robust image detection in scenes under cast shadows

ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this work a new robust color and contour based object detection method in images with varying shadows is presented. The method relies on a physics-based contour detector that emphasizes material changes and a contour-based boosted classifier. The method has been tested in a sequence of outdoor color images presenting varying shadows using two classifiers, one that learnt contour object features from a simple gradient detector, and another that learnt from the photometric invariant contour detector. It is shown that the detection performance of the classifier trained with the photometric invariant detector is significantly higher than that of the classifier trained with gradient detector.