By Topic

Perception for a roadheader in automatic selective cutting operation

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Orteu, J.-J. ; LAAA-CNRS, Toulouse, France ; Catalina, J.-C. ; Devy, M.

The authors show how color and texture image segmentation, automatic image classification, camera calibration, and 3D scene representation can cooperate to solve a complex problem such as selective cutting. Attention is focused on the field of ore recognition, where significant improvement has been obtained by texture information. The research project considered involves the automation of the cutting operation of a roadheader for selective cutting in an underground potash mine near Barcelona in Spain. The system described is based on the use of computer vision to discriminate the different ore types found in the face (sylvinite, carnalite, and salt). Using the information about the ore distribution, paths are then planned for the computer-controlled cutting boom. It was shown that color information was important for minerals identification but texture information was absolutely necessary to get a good identification in all cases. The two kinds of information cooperate in an automatic image classification algorithm, which has been validated on many images of the mine face

Published in:

Robotics and Automation, 1992. Proceedings., 1992 IEEE International Conference on

Date of Conference:

12-14 May 1992