Part-to-whole Registration of Histology and MRI using Shape Elements

Image registration between histology and magnetic resonance imaging (MRI) is a challenging task due to differences in structural content and contrast. Too thick and wide specimens cannot be processed all at once and must be cut into smaller pieces. This dramatically increases the complexity of the problem, since each piece should be individually and manually pre-aligned. To the best of our knowledge, no automatic method can reliably locate such piece of tissue within its respective whole in the MRI slice, and align it without any prior information. We propose here a novel automatic approach to the joint problem of multimodal registration between histology and MRI, when only a fraction of tissue is available from histology. The approach relies on the representation of images using their level lines so as to reach contrast invariance. Shape elements obtained via the extraction of bitangents are encoded in a projective-invariant manner, which permits the identification of common pieces of curves between two images. We evaluated the approach on human brain histology and compared resulting alignments against manually annotated ground truths. Considering the complexity of the brain folding patterns, preliminary results are promising and suggest the use of characteristic and meaningful shape elements for improved robustness and efficiency.Comment: Paper accepted at ICCV Workshop (Bio-Image Computing

UV Completions of Magnetic Inelastic Dark Matter and RayDM for the Fermi Line(s)

Models that seek to produce a line at ~130 GeV as possibly present in the Fermi data face a number of phenomenological hurdles, not the least of which is achieving the high cross section into gamma gamma required. A simple explanation is a fermionic dark matter particle that couples to photons through loops of charged messengers. We study the size of the dimension 5 dipole (for a pseudo-Dirac state) and dimension 7 Rayleigh operators in such a model, including all higher order corrections in 1/M_{mess}. Such corrections tend to enhance the annihilation rates beyond the naive effective operators. We find that while freezeout is generally dominated by the dipole, the present day gamma-ray signatures are dominated by the Rayleigh operator, except at the most strongly coupled points, motivating a hybrid approach. With this, the Magnetic inelastic Dark Matter scenario provides a successful explanation of the lines at only moderately strong coupling. We also consider the pure Majorana WIMP, where both freezeout and the Fermi lines can be explained, but only at very strong coupling with light (~200 - 300 GeV) messengers. In both cases there is no conflict with non-observation of continuum photons.Comment: 11 pages, 6 figure

Particular object retrieval with integral max-pooling of CNN activations

Recently, image representation built upon Convolutional Neural Network (CNN) has been shown to provide effective descriptors for image search, outperforming pre-CNN features as short-vector representations. Yet such models are not compatible with geometry-aware re-ranking methods and still outperformed, on some particular object retrieval benchmarks, by traditional image search systems relying on precise descriptor matching, geometric re-ranking, or query expansion. This work revisits both retrieval stages, namely initial search and re-ranking, by employing the same primitive information derived from the CNN. We build compact feature vectors that encode several image regions without the need to feed multiple inputs to the network. Furthermore, we extend integral images to handle max-pooling on convolutional layer activations, allowing us to efficiently localize matching objects. The resulting bounding box is finally used for image re-ranking. As a result, this paper significantly improves existing CNN-based recognition pipeline: We report for the first time results competing with traditional methods on the challenging Oxford5k and Paris6k datasets

Angularities and other shapes

I discuss soft-gluon resummation and power corrections for event shape distributions, mostly in e+ e- annihilation. I consider specifically the thrust, the C parameter, and the class of angularities, and show how factorization techniques and dressed gluon exponentiation lead to predictive models of power corrections that are firmly grounded in perturbative QCD. The scaling rule for the shape function for angularities is derived as an example. Finally, I make a few remarks on possible generalizations to hadron collisions, and on their relevance to LHC studies.Comment: 10 pages, contribution to the FRIF workshop on first principles non-perturbative QCD of hadron jets, LPTHE, Paris, France, 12-14 Jan 200