A Fast Multi-model Approach for Object Duplicate Extraction

This paper presents an innovative approach for localizingand segmenting duplicate objects for industrial applications.The working conditions are challenging, withcomplex heavily-occluded objects, arranged at random inthe scene. To account for high flexibility and processingspeed, this approach exploits SIFT keypoint extraction andmean shift clustering to efficiently partition the correspondencesbetween the object model and the duplicates ontothe different object instances. The re-projection (by meansof an Euclidean transform) of some delimiting points ontothe current image is used to segment the object shapes. Thisprocedure is compared in terms of accuracy with existinghomography-based solutions which make use of RANSACto eliminate outliers in the homography estimation. Moreover,in order to improve the extraction in the case of reflectiveor transparent objects, multiple object models are usedand fused together. Experimental results on different andchallenging kinds of objects are reported.