Importance

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Abstract

Object recognition by point-of-sale camera systems is aided by first removing perspective distortion. Yet pose of the object—relative to the system—depends on actions of the operator, and is usually unknown. Multiple trial counter-distortions to remove perspective distortion can be attempted, but the number of such trials is limited by the frame rate of the camera system—which limits the available processing interval. One embodiment of the present technology examines historical image data to determine counter-distortions that statistically yield best object recognition results. Similarly, the system can analyze historical data to learn what sub-parts of captured imagery most likely enable object recognition. A set-cover strategy is desirably used. In some arrangements, the system identifies different counter-distortions, and image sub-parts, that work best with different clerk- and customer-operators of the system, and processes captured imagery accordingly. A great variety of other features and arrangements are also detailed.

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First Claim