Feature recognition and fingerprint sensing for guiding a wood patching robot
Year: 2012
Type: conference-paper
Abstract: This paper includes a summary of a few commonly used object recognition techniques, as well as a sensitivity analysis of two feature point recognition methods. The robustness was analyzed by automatically trying to recognize 886 images of pine floorboards after applying different levels of distortions. Recognition was also tested on a subset of 5% of the boards which were both re-scanned using a line scan camera and photographed using a digital camera. Experiments showed that both the Block matching method and the SURF method are valid options for recognizing wood products covered with distinct features. The Block matching method outperformed the SURF method for small geometric distortions and moderate radiometric distortions. The SURF method, in its turn, performed better compared to the other method when faced with low resolution digital images.
Authors: Tobias Pahlberg, Olle Hagman
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2914552879