EP0204516A2
Expired / Lapsed
EP
⏱️ 0.0 years remaining
Vision system for distinguishing touching parts
Assignee: Omron Robotic and Safety Technologies Inc
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Filing: 1986-05-29
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Granted: —
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Expires: 2006-05-29
Abstract
Abstract A practical vision system for controlling the positioning of a robot arm recognises and locates objects. The vision system (20, 30) processes binary images, but recognises objects based on boundary features such as lines, arcs, corners and holes instead of "blob features" such as area and best-fit ellipse. Consequently, the vision system can process two common situations not handled by blob analysis: merged blobs due to touching or overlapping parts and incomplete blobs due to low image contrast. The microprocessor-based system is interfaced to the robot system and can recognize up to five parts per second.
Inventors
- 👤 Scot D Roth
Bibliographic Details
Application Number
—
Jurisdiction
EP
Data Source
google_patents
Last Synchronized
2026-10-29 22:19
CPC Classifications
B; B25; B25J; B25J9/00; B25J9/16; B25J9/1694; B25J9/1697; G; G05; G05B; G05B19/00; G05B19/02; G05B19/418; G05B19/41815; G05B19/4182; G06; G06V; G06V10/00; G06V10/40; G06V10/46; G06V20/00; G06V20/60; G06V20/66; G06V30/00; G06V30/10; G06V30/24; G06V30/248; Y; Y02; Y02P; Y02P90/00; Y02P90/02
Claims Analysis
Total Claims: 2
Claim 1
Independent Claim
96 words
A method for identifying parts comprising a) developing sensory data for an object on a line by line basis; b) separating closed regions from background by a connectivity analysis of the data; c) developing a chain encoded representation of the region's perimeter; d) fitting edge segments to the chain encoded data; e) fitting line and arc segments to the edge segments; f) classifying all segments by feature class; g) proposing a match of image of features present in an object; and h) verifying the presence of features in the object image to positively identify the object.
Claim 2
Independent Claim
96 words
A method for identifying parts comprising a) developing sensory data for an object on a line by line basis; b) separating closed regions from background by a connectivity analysis of the data; c) developing a chain encoded representation of the region's perimeter; d) fitting edge segments to the chain encoded data; e) fitting line and arc segments to the edge segments; f) classifying all segments by feature class; g) proposing a match of image of features present in an object; and h) verifying the presence of features in the object image to positively identify the object.
References Cited (Backward Citations)
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