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This paper presents an innovative approach for localizing and segmenting duplicate objects for industrial applications. The working conditions are challenging, with complex heavily-occluded objects, arranged at random in the scene. To account for high flexibility and processing speed, this approach exploits SIFT keypoint extraction and mean shift clustering to efficiently partition the correspondences between the object model and the duplicates onto the different object instances. The re-projection (by means of an Euclidean transform) of some delimiting points onto the current image is used to segment the object shapes. This procedure is compared in terms of accuracy with existing homography-based solutions which make use of RANSAC to eliminate outliers in the homography estimation. Moreover, in order to improve the extraction in the case of reflective or transparent objects, multiple object models are used and fused together. Experimental results on different and challenging kinds of objects are reported.