Name
Abstract | ||
|---|---|---|
The dramatic increase of electronic waste requires automatic recycling, including technologies from machine vision. A framework for segmentation and classification of THC (through-hole components) mounted on PCBAs is presented, using both RGB and depth frames from the Kinect sensor by Microsoft. A segmentation approach, combining local and global features in a flexible manner, is shown to optimize a freely definable cost function globally. We interleave segmentation and classification as we form the final components using a simple, yet robust shape model. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/INDIN.2013.6622858 | 2013 11TH IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL INFORMATICS (INDIN) |
Keywords | Field | DocType |
shape,printed circuits,sensors,cost function,merging,recycling,machine vision,image segmentation | Computer vision,Engineering drawing,Machine vision,Segmentation,Printed circuit board,RGB color model,Artificial intelligence,Engineering | Conference |
ISSN | Citations | PageRank |
1935-4576 | 1 | 0.43 |
References | Authors | |
5 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daniel Herchenbach | 1 | 1 | 0.43 |
| Wei Li | 2 | 25 | 5.04 |
| Matthias Breier | 3 | 8 | 3.46 |