Recognition of 2-D shapes using set erosion

S. John Rees; Bryan F. Jones

doi:10.1117/12.57058

1 February 1992 Recognition of 2-D shapes using set erosion

S. John Rees, Bryan F. Jones

Proceedings Volume 1607, Intelligent Robots and Computer Vision X: Algorithms and Techniques; (1992) https://doi.org/10.1117/12.57058
Event: Robotics '91, 1991, Boston, MA, United States

Abstract

Set erosion is an efficient algorithm which has been used to recognize shapes irrespective of orientation, translation, and scaling. The technique has successfully recognized complex shapes, even when two shapes overlap. The uncertainty in the measured estimate of scaling rose to 8% from the 2% figure obtained for separate shapes. The image picture is segmented between shape and background. The orientation and length of each side or arc on the perimeter of the shape is extracted using a chain code based technique and a set composed of the orientation and angle information formed. This set of data is then morphologically eroded with the orientation/angle spectra of each of the shapes in a predefined library of reference shapes, the reference shapes being scaled to the acquired image data. If the set of angle/weight reference data is contained within the acquired set, the reference shape is recognized as being part of the solution. The required shift of the reference spectrum to match the acquired spectrum yields the rotation of the shape relative to the reference data. Scale information is generated as part of the preconditioning of the reference data prior to the erosion process. Location data are generated by tagging extracted vertices within the chain code extraction of side data.

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S. John Rees and Bryan F. Jones "Recognition of 2-D shapes using set erosion", Proc. SPIE 1607, Intelligent Robots and Computer Vision X: Algorithms and Techniques, (1 February 1992); https://doi.org/10.1117/12.57058

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KEYWORDS

Data acquisition

Detection and tracking algorithms

Image processing

Computer vision technology

Machine vision

Robot vision

Robots

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