Boundary-based shape normalization technique

Jia-Guu Leu

doi:10.1117/12.131534

1 November 1992 Boundary-based shape normalization technique

Jia-Guu Leu

Proceedings Volume 1825, Intelligent Robots and Computer Vision XI: Algorithms, Techniques, and Active Vision; (1992) https://doi.org/10.1117/12.131534
Event: Applications in Optical Science and Engineering, 1992, Boston, MA, United States

Abstract

When a camera's center axis is not parallel to the surface normal of a planar object, the perceived shape of the object will be skewed. Most existing shape analysis methods are sensitive to such shape distortions. That is, if a shape is skewed due to non-orthographic projection, it may not be correctly recognized. In this paper we present a shape normalization process which neutralizes such shape skewing effects. In our case, a perceived shape is represented by a list of corner points along its boundary. We first compute the six lower moments of the shape from its boundary. Then these moments are used to compute the shape's center location, orientation, and maximum and minimum moments of inertia. To normalize the shape we first translate the shape's center to the origin. Next we rotate the shape to align its major axis with the x-axis. Then we expand the shape along its minor axis to neutralize shape skewing. Lastly, we scale the size of the shape so it has a standard moment of inertia. The suggested method can be used as a preprocessing step for any planar shape analysis method which is sensitive to shape skewing, shape size change, and/or shape translation. Since the moments are computed from the shape's boundary instead of from all its interior pixels, the method is also efficient. Several experimental results are given to show the effectiveness of the approach.

Citation Download Citation

Jia-Guu Leu "Boundary-based shape normalization technique", Proc. SPIE 1825, Intelligent Robots and Computer Vision XI: Algorithms, Techniques, and Active Vision, (1 November 1992); https://doi.org/10.1117/12.131534

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KEYWORDS

Shape analysis

Cameras

Computer vision technology

Machine vision

Robot vision

Robots

Image segmentation

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