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Why the feature should be RST invarient..when extracting a feature from an image..

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A good feature should have high varinace and it should be RST invariant for image. Why is it so?

Respuestas (2)

Walter Roberson
Walter Roberson el 4 de Abr. de 2013
If "RST" here stands for "Rotation, Scaling, Translation", then under what circumstances would it be beneficial to have a feature that was varient under one of those operations?
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Image Analyst
Image Analyst el 4 de Abr. de 2013
One example, robots to "pick and place" parts from a bin onto an assembly they're building.
Walter Roberson
Walter Roberson el 4 de Abr. de 2013
True, size (scale) could be important in some contexts that involved calibrated sensors.

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Image Analyst
Image Analyst el 4 de Abr. de 2013
That's only true for certain situations. I'm sure you can imaging situations were you'd like them to vary and situations where you'd like them not to vary. Though I can't think of any situations where you'd want the feature measurements to have a high variance instead of a low variance. Invariant and variance are different concepts, but not antonymns (opposites) - consult a dictionary.

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