Three-level broad-edge matching based real-time robot vision

Abstract

This paper presents our real-time vision system for robots. The system employs a new internal image representation, in which the scene is encoded into three intensity level images. This representation is generated by Laplacian-Gaussian filtering followed by dual-thresholding. We refer to this image as three-level broad-edge representation. It reduces the computational cost of the normalized cross correlation. Thus the system performs the searching visual task as well as the object tracking task, which is not reliably achieved by the mean absolute error based correlators. Our prototype system achieves 32/spl times/32 pixel normalized cross correlation based matching from the 128/spl times/128 pixel search area in 2 ms under 9 MHz pixel clock image processor. This speed is fast enough for searching and tracking a single object at video frame rate.