Abstract: "The three dimensional Shape Attractor tries to match a 3D 'template' model to a 2D image of a part. The 3D template is projected onto the image plane and the outline polygon found. This is compared with the image and a least-squares technique is used to estimate the 'b...

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Bibliographic Details

Main Author:	Balch, Peter (Author)
Format:	Book
Language:	English
Published:	Edinburgh 1992
Series:	University <Edinburgh> / Department of Artificial Intelligence: DAI research paper 573
Subjects:	Pattern recognition, image processing and remote sensing Robotics and its application Robot vision
Summary:	Abstract: "The three dimensional Shape Attractor tries to match a 3D 'template' model to a 2D image of a part. The 3D template is projected onto the image plane and the outline polygon found. This is compared with the image and a least-squares technique is used to estimate the 'best' way to rotate, translate, and scale the template so that it fits the image. The template is rotated, scaled, and translated and its new projected image is compared with the part outline once again. Although the estimate of the 'best' rotation' is crude, the errors are self-limiting and iteration ensures that an exact solution is quickly found. The 3D Shape Attractor is robust and its output is a transformation, not a similarity measure. This makes it particularly suitable for the control of robot motion A robot programming example demonstrates how the algorithm can be used in part acquisition. The computer model of the part carries 'handles' which are used to indicate where the image of the robot hand should be so the hand can pick up the part.
Physical Description:	15 S.