Unsupervised clustering in Hough space for recognition of multiple instances of the same object in a cluttered scene
We describe an active binocular vision system that is capable of localising multiple instances of objects of the same-class in different settings within a covert, pre-attentive, visual search strategy. By clustering SIFT-feature matches that have been projected into a non-quantised (i.e. continuous) Hough space we are able to detect up to 6 same-class object instances simultaneously while tolerating up to ?66% of each objects surface being occluded by another object instance of the same-class. Our findings are based on using a database of ?2300 images of synthetically composited and real-world images.
unsupervised clustering generalised hough transform active vision robot head multiple localisation