Precision Work-piece Detection and Measurement Combining Top-down and Bottom-up Saliency

In this paper, a fast and accurate work-piece detection and measurement algorithm is proposed based on top-down feature extraction and bottom-up saliency estimation. Firstly, a top-down feature extraction method based on the prior knowledge of workpieces is presented, in which the contour of a work-piece is chosen as the major feature and the corresponding template of the edges is created. Secondly, a bottom-up salient region estimation algorithm is proposed, where the image boundaries are labelled as background queries, and the salient region can be detected by computing contrast against image boundary. Finally, the calibration method for vision system with telecentric lens is discussed, and the dimensions of the work-pieces are measured. In addition, strategies such as image pyramids and a stopping criterion are adopted to speed-up the algorithm. An automatic system embedded with the proposed detection and measurement algorithm combining top-down and bottom-up saliency (DM-TBS) is designed to pick out defective work-pieces without any manual auxiliary. Experiments and results demonstrate the effectiveness of the proposed method. © 2018, Institute of Automation, Chinese Academy of Sciences and Springer-Verlag GmbH Germany, part of Springer Nature.