Implicit reconstruction by zooming

This paper presents a new method to infer 3D information using a static camera equipped with a zoom-lens. The modelling algorithm does not required any explicit calibration model and the computations involved are straightforward. This approach uses several images of accurate regular grids placed on a micrometric table, as calibration process . The basic idea is to compute a local transformation that allows to establish a relationship between a distorted grid detected on the CCD matrix and the real one located in front of the camera . This relationship takes automatically into account all distortion phenomena and allows to obtain reconstruction results much more accurate than previous works in the same field . A complete experiment on real data is provided and shows that it is possible to compute 3D information from a zooming image set even if data are close to the optical axis .Cet article présente une nouvelle méthode permettant d'inférer des informations tridimensionnelles à l'aide d'une caméra statique munie d'un zoom. L'algorithme de modélisation ne nécessite aucun modèle explicite de calibrage et met en oeuvre plusieurs images de grilles régulières et précises formant un espace métrique particulier. Une transformation locale permet d'établir une relation entre l'image distordue d'une grille détectée sur la matrice CCD et une grille réelle située devant la caméra. Cette relation prend automatiquement en compte les phénomènes de distorsion optique et permet d'obtenir des résultats de reconstruction bien meilleurs que ceux obtenus jusqu'à présent en reconstruction axiale par zoom. De plus, la méthode présentée permet de calibrer l'objectif sur une gamme importante de distances focales sans changer d'objet de calibrage. Une expérimentation complète sur des données réelles est présentée et montre qu'il est possible de reconstruire des objets 3D à partir d'une séquence d'images de zoom même si ces données sont proches de l'axe optique

Dual Arbitrary Scale Super-Resolution for Multi-Contrast MRI

Limited by imaging systems, the reconstruction of Magnetic Resonance Imaging (MRI) images from partial measurement is essential to medical imaging research. Benefiting from the diverse and complementary information of multi-contrast MR images in different imaging modalities, multi-contrast Super-Resolution (SR) reconstruction is promising to yield SR images with higher quality. In the medical scenario, to fully visualize the lesion, radiologists are accustomed to zooming the MR images at arbitrary scales rather than using a fixed scale, as used by most MRI SR methods. In addition, existing multi-contrast MRI SR methods often require a fixed resolution for the reference image, which makes acquiring reference images difficult and imposes limitations on arbitrary scale SR tasks. To address these issues, we proposed an implicit neural representations based dual-arbitrary multi-contrast MRI super-resolution method, called Dual-ArbNet. First, we decouple the resolution of the target and reference images by a feature encoder, enabling the network to input target and reference images at arbitrary scales. Then, an implicit fusion decoder fuses the multi-contrast features and uses an Implicit Decoding Function~(IDF) to obtain the final MRI SR results. Furthermore, we introduce a curriculum learning strategy to train our network, which improves the generalization and performance of our Dual-ArbNet. Extensive experiments in two public MRI datasets demonstrate that our method outperforms state-of-the-art approaches under different scale factors and has great potential in clinical practice.Comment: Accepted by MICCAI202

Simultaneous reconstruction of evolutionary history and epidemiological dynamics from viral sequences with the birth-death SIR model

The evolution of RNA viruses such as HIV, Hepatitis C and Influenza virus occurs so rapidly that the viruses' genomes contain information on past ecological dynamics. Hence, we develop a phylodynamic method that enables the joint estimation of epidemiological parameters and phylogenetic history. Based on a compartmental susceptible-infected-removed (SIR) model, this method provides separate information on incidence and prevalence of infections. Detailed information on the interaction of host population dynamics and evolutionary history can inform decisions on how to contain or entirely avoid disease outbreaks. We apply our Birth-Death SIR method (BDSIR) to two viral data sets. First, five human immunodeficiency virus type 1 clusters sampled in the United Kingdom between 1999 and 2003 are analyzed. The estimated basic reproduction ratios range from 1.9 to 3.2 among the clusters. All clusters show a decline in the growth rate of the local epidemic in the middle or end of the 90's. The analysis of a hepatitis C virus (HCV) genotype 2c data set shows that the local epidemic in the C\'ordoban city Cruz del Eje originated around 1906 (median), coinciding with an immigration wave from Europe to central Argentina that dates from 1880--1920. The estimated time of epidemic peak is around 1970.Comment: Journal link: http://rsif.royalsocietypublishing.org/content/11/94/20131106.ful

Stevin numbers and reality

We explore the potential of Simon Stevin's numbers, obscured by shifting foundational biases and by 19th century developments in the arithmetisation of analysis.Comment: 22 pages, 4 figures. arXiv admin note: text overlap with arXiv:1104.0375, arXiv:1108.2885, arXiv:1108.420