Authors:
Christopher Pramerdorfer
and
Martin Kampel
Affiliation:
Vienna University of Technology, Austria
Keyword(s):
Interest Points, Descriptors, Local Features, Instance Recognition, PCB Recognition, Evaluation.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Camera Networks and Vision
;
Computer Vision, Visualization and Computer Graphics
;
Features Extraction
;
Image and Video Analysis
Abstract:
We present a method for detecting and classifying Printed Circuit Boards (PCBs) in waste streams for recycling
purposes. Our method employs local feature matching and geometric verification to achieve a high open-set
recognition performance under practical conditions. In order to assess the suitability of different local features
in this context, we perform a comprehensive evaluation of established (SIFT, SURF) and recent (ORB, BRISK,
FREAK, AKAZE) keypoint detectors and descriptors in terms of established performance measures. The
results show that SIFT and SURF are outperformed by recent alternatives, and that most descriptors benefit
from color information in the form of opponent color space. The presented method achieves a recognition rate
of up to 100% and is robust with respect to PCB damage, as verified using a comprehensive public dataset.