12,272 research outputs found
Cluster-based point set saliency
© 2015 IEEE. 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
Quantitative Analysis of Saliency Models
Previous saliency detection research required the reader to evaluate
performance qualitatively, based on renderings of saliency maps on a few
shapes. This qualitative approach meant it was unclear which saliency models
were better, or how well they compared to human perception. This paper provides
a quantitative evaluation framework that addresses this issue. In the first
quantitative analysis of 3D computational saliency models, we evaluate four
computational saliency models and two baseline models against ground-truth
saliency collected in previous work.Comment: 10 page
Saliency-guided Adaptive Seeding for Supervoxel Segmentation
We propose a new saliency-guided method for generating supervoxels in 3D
space. Rather than using an evenly distributed spatial seeding procedure, our
method uses visual saliency to guide the process of supervoxel generation. This
results in densely distributed, small, and precise supervoxels in salient
regions which often contain objects, and larger supervoxels in less salient
regions that often correspond to background. Our approach largely improves the
quality of the resulting supervoxel segmentation in terms of boundary recall
and under-segmentation error on publicly available benchmarks.Comment: 6 pages, accepted to IROS201
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