In the drive towards miniaturization in manufacturing, accuracy in positioning minute objects by camera is vital. For visual servoing, the rapid and robust detections of features in camera images is also essential to production line efficiency. Template matching provides flexibility in achieving this, often lacked by other methods, because it avoids the need to set object-specific parameters. Unfortunately, standard methods of template matching require much calculation, especially for detecting feature rotation. The delay this causes means that for many applications template matching provides too slow a source of visual feedback. As an alternative, we propose a new method of detecting the translation and rotation of a feature from coarse optical flow, we and apply it to visual servoing. Coarse optical flow is derived from the difference in intensity between a region of the initial and current image and their pixel-by-pixel intensity gradients. Unlike template matching, our method can detect large rotations with relatively little calculation. Image resolution is then adjusted from coarse to fine. Subpixel accuracy results in a 100 fold improvement in precision (by area). We show experimental results for precise planar positioning.
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