Two new shape measures for quantifying the degree of convexity are described. When applied to assessment of skin lesions they are shown to be an effective indicator of malignancy, outperforming Lee et al.’s OII scale-space based irregularity measure. In addition, the new measures were applied to the classification of mammographic masses and lung field boundaries and were shown to perform well r...

We investigate the effect of a space-dependent random mass density field on small amplitude acoustic modes that are settled in a semi-infinite medium of a temperature growing linearly with depth. Using a perturbation method, the dispersion relation is derived in the form of Hill’s determinant. Numerical solutions of this equation lead to the following conclusions: (a) a weak random field (with ...

We prove a large deviation principle for Minkowski sums of i.i.d. random compact sets in a Banach space, that is, the analog of Cramér theorem for random compact sets. Several works have been devoted to deriving limit theorems for random sets. For i.i.d. random compact sets in R, the law of large numbers was initially proved by Artstein and Vitale [1] and the central limit theorem by Cressie [3...

Scale-space theory is a framework for multiscale image representation, which has been developed by the computer vision community with complementary motivations from physics and biologic vision. The idea is to handle the multiscale nature of real-world objects, which implies that objects may be perceived in different ways depending on the scale of observation. If one aims to develop automatic al...

Neuroimaging produces data that are continuous in one or more dimensions. This calls for an inference framework that can handle data that approximate functions of space, for example, anatomical images, time–frequency maps and distributed source reconstructions of electromagnetic recordings over time. Statistical parametric mapping (SPM) is the standard framework for whole-brain inference in neu...

The goal of this work is to investigate the performance of classical methods for feature description and classification, and to identify the difficulties of the ImageCLEF 2010 modality classification subtask. In this paper, we describe different approaches based on visual information for classifying medical images into 8 different modality classes. Since within the same class, images depict ver...

This paper approaches the problem of finding correspondences between images in which there are large changes in viewpoint, scale and illumination. Recent work has shown that scale-space ‘interest points’ may be found with good repeatability in spite of such changes. Furthermore, the high entropy of the surrounding image regions means that local descriptors are highly discriminative for matching...

We revisit here the notion of discrete scale invariance. Initially defined for signal indexed by the positive reals, we present a generalized version of discrete scale invariant signals relying on a renormalization group approach. In this view, the signals are seen as fixed point of a renormalization operator acting on a space of signal. We recall how to show that these fixed point present disc...