A study of observation scales based on Felzenswalb-Huttenlocher dissimilarity measure for hierarchical segmentation

International audienceHierarchical image segmentation provides a region-oriented scale-space, i.e., a set of image segmentations at different detail levels in which the segmentations at finer levels are nested with respect to those at coarser levels. Guimarães et al. proposed a hierarchical graph based image segmentation (HGB) method based on the Felzenszwalb-Huttenlocher dissimilarity. This HGB method computes, for each edge of a graph, the minimum scale in a hierarchy at which two regions linked by this edge should merge according to the dissimilarity. In order to generalize this method, we first propose an algorithm to compute the intervals which contain all the observation scales at which the associated regions should merge. Then, following the current trend in mathematical morphology to study criteria which are not increasing on a hierarchy, we present various strategies to select a significant observation scale in these intervals. We use the BSDS dataset to assess our observation scale selection methods. The experiments show that some of these strategies lead to better segmentation results than the ones obtained with the original HGB method

Hierarchical colour image segmentation by leveraging RGB channels independently

In this paper, we introduce a hierarchical colour image segmentation based on cuboid partitioning using simple statistical features of the pixel intensities in the RGB channels. Estimating the difference between any two colours is a challenging task. As most of the colour models are not perceptually uniform, investigation of an alternative strategy is highly demanding. To address this issue, for our proposed technique, we present a new concept for colour distance measure based on the inconsistency of pixel intensities of an image which is more compliant to human perception. Constructing a reliable set of superpixels from an image is fundamental for further merging. As cuboid partitioning is a superior candidate to produce superpixels, we use the agglomerative merging to yield the final segmentation results exploiting the outcome of our proposed cuboid partitioning. The proposed cuboid segmentation based algorithm significantly outperforms not only the quadtree-based segmentation but also existing state-of-the-art segmentation algorithms in terms of quality of segmentation for the benchmark datasets used in image segmentation. © 2019, Springer Nature Switzerland AG

Search for a Very Light CP-Odd Higgs Boson in Top Quark Decays from p(p)over-bar Collisions at root s=1.96 TeV

We present the results of a search for a very light CP-odd Higgs boson a(1)(0) originating from top quark decays t -> H(+/-)b -> W-+/-(*) a(1)(0)b, and subsequently decaying into tau(+)tau(-). Using a data sample corresponding to an integrated luminosity of 2.7 fb(-1) collected by the CDF II detector in p (p) over bar collisions at 1.96 TeV, we perform a search for events containing a lepton, three or more jets, and an additional isolated track with transverse momentum in the range 3 to 20 GeV/c. Observed events are consistent with background sources, and 95% C.L. limits are set on the branching ratio of t -> H(+/-)b for various masses of H-+/- and a(1)(0)