Gradient vector griding: an approach to shape-based object detection in RoboCup scenarios

Authors:
Hamid Moballegh;Naja Von Schmude;Raúl Rojas
Affiliations:
Institut für Informatik, Arbeitsgruppe Künstliche Intelligenz, Freie Universität Berlin, Berlin, Germany;Institut für Informatik, Arbeitsgruppe Künstliche Intelligenz, Freie Universität Berlin, Berlin, Germany;Institut für Informatik, Arbeitsgruppe Künstliche Intelligenz, Freie Universität Berlin, Berlin, Germany
Venue:
Robot Soccer World Cup XV
Year:
2012

Citing 6
Cited 0

Fast Image Segmentation, Object Recognition and Localization in a RoboCup Scenario

RoboCup-99: Robot Soccer World Cup III
Histograms of Oriented Gradients for Human Detection

CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Multi-cue Localization for Soccer Playing Humanoid Robots

RoboCup 2006: Robot Soccer World Cup X
Towards a Calibration-Free Robot: The ACT Algorithm for Automatic Online Color Training

RoboCup 2006: Robot Soccer World Cup X
Utilizing the structure of field lines for efficient soccer robot localization

RoboCup 2010
The color and the shape: automatic on-line color calibration for autonomous robots

RoboCup 2005

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper describes a new method of extraction and clustering of edges in images. The proposed method results a graph of detected edges instead of a binary mask of the edge pixels. The developed algorithm contains a sequential pixel-level scan, and a much smaller second and third pass on the results to determine the connectivities. It is therefore significantly faster than Canny edge detector, performing both edge detection and grouping tasks. The method is developed for a RoboCup scenario, however it can also be applied to any other image as long as the prerequisites are met. The paper explains the idea, discusses the prerequisites and finally presents the implementation results and issues.