Image Sub-Pixel Feature Location Algorithm on Vision Measurement System

Tian, Junwei; Yuan, Shu

doi:10.3901/jme.2006.11.075

Cited by 2 publications

(2 citation statements)

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“…Through calculating the second-ordered directional derivative, edge point is defined as the zero-crossing point of facet model along the gray gradient direction [5]. Third-ordered facet model of the pixel (x, y) in 5×5 regions can be expressed as follows:…”

Section: Edge Detecting Algorithm Based On Facet Modelmentioning

confidence: 99%

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Study on Algorithms of Graphic Element Recognition for Precise Vectorization of Industrial Computed Tomographic Image

Liu¹

2011

TOMEJ

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Circle, line and circular arc are the common basic graphic elements in industrial computed tomography (ICT) image. The algorithm of recognizing such elements is the key to industrial CT image precise vectorization. An industrial CT image vectorization system has been studied, including different recognition methods for these elements. Firstly, based on facet model, the sub-pixel edge of an industrial CT image is extracted. Then, the circles are recognized by an improved algorithm based on probability of existence map, while the lines are recognized with the set intersection algorithm of fitting a straight line, and the circular arcs are recognized by the combination of the perpendicular bisector tracing algorithm and least squares function. Finally, the graphic element parameters are measured according to recognition results, and the drawing exchange file (DXF) is produced and transmitted into the computer aided design (CAD) system to be edited and consummated. The experimental results show that these methods are capable of recognizing graphic elements in industrial CT image with an excellent accuracy, besides, the absolute errors of circles are less than 0.1 mm, and the relative errors are less than 0.5%. It can satisfy the industrial CT vectorization requirements of higher precision, rapid speed and non-contact.

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Section: Edge Detecting Algorithm Based On Facet Modelmentioning

confidence: 99%

“…(r, c) is the edge point, if we can obtain ρ 0 (ρ 0 is within the 5×5 sub-region whose center is (r, c)) which satisfies (4), and the third-ordered partial derivative negative, as in (5).…”

Section: Edge Detecting Algorithm Based On Facet Modelmentioning

confidence: 99%

Study on Algorithms of Graphic Element Recognition for Precise Vectorization of Industrial Computed Tomographic Image

Liu¹

2011

TOMEJ

View full text Add to dashboard Cite

show abstract

Precise Vectorization of Industrial Computed Tomographic Image

Liu

2010

AMR

View full text Add to dashboard Cite

Circle, line and circular arc are the common basic elements in industrial computed tomography (ICT) image. The algorithm of recognizing such elements is the key to industrial CT image precise vectorization. An industrial CT image vectorization system has been studied, including different recognition methods for these elements. Firstly, based on facet model, the sub-pixel edge of an industrial CT image is extracted. Then, the circles are recognized by an improved algorithm based on probability of existence map, while the lines are recognized with the set intersection algorithm of fitting a straight line, and the circular arcs are recognized by the combination of the perpendicular bisector tracing algorithm and least squares function. Finally, the element parameters are measured according to recognition results, and the drawing exchange file (DXF) is produced and transmitted into the computer aided design (CAD) system to be edited and consummated. The experimental results show that these methods are capable of recognizing graphic elements in industrial CT image with an excellent accuracy, besides, the absolute errors of circles are less than 0.1 mm, the relative errors are less than 0.5%. It can satisfy the industrial CT vectorization requirements of higher precision, rapid speed and non-contact.

show abstract

Image Sub-Pixel Feature Location Algorithm on Vision Measurement System

Cited by 2 publications

References 0 publications

Study on Algorithms of Graphic Element Recognition for Precise Vectorization of Industrial Computed Tomographic Image

Study on Algorithms of Graphic Element Recognition for Precise Vectorization of Industrial Computed Tomographic Image

Precise Vectorization of Industrial Computed Tomographic Image

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