Computer Laboratory

Past projects

Teaser figure Cluster-based point set saliency on a range scan. From left to right: normal map, adaptive fuzzy clustering (each cluster a different colour), cluster uniqueness metric, cluster spatial distribution metric, cluster saliency calculated from uniqueness and distribution metrics, point saliency derived from cluster saliency.


We propose a cluster-based approach to point set saliency detection, a challenge since point sets lack topological information. A point set is first decomposed into small clusters, using fuzzy clustering. We evaluate cluster uniqueness and spatial distribution of each cluster and combine these values into a cluster saliency function. Finally, the probabilities of points belonging to each cluster are used to assign a saliency to each point. Our approach detects fine-scale salient features and uninteresting regions consistently have lower saliency values. We evaluate the proposed saliency model by testing our saliency-based keypoint detection against a 3D interest point detection benchmark. The evaluation shows that our method achieves a good balance between false positive and false negative error rates, without using any topological information.




  • Paper (PDF, 5.7 MB)
  • Source: Soon


     author    = {Tasse, Flora P. and Kosinka, Ji{\v{r}}{\'{i}}
                  and Dodgson, Neil A.},
     title     = {Cluster-based point set saliency},
     year      = {2015},
     booktitle = {ICCV 2015},
     url       = {},