Spatially-Variant Directional Mathematical Morphology Operators Based on a Diffused Average Squared Gradient Field

International audienceThis paper proposes an approach for mathematical morphology operators whose structuring element can locally adapt its orientation across the pixels of the image. The orientation at each pixel is extracted by means of a diffusion process of the average squared gradient field. The resulting vector field, the average squared gradient vector flow, extends the orientation information from the edges of the objects to the homogeneous areas of the image. The provided orientation field is then used to perform a spatially variant filtering with a linear structuring element. Results of erosion, dilation, opening and closing spatially-variant on binary images prove the validity of this theoretical sound and novel approach

General Adaptive Neighborhood Image Restoration, Enhancement and Segmentation

12 pagesInternational audienceThis paper aims to outline the General Adaptive Neighborhood Image Processing (GANIP) approach [1–3], which has been recently introduced. An intensity image is represented with a set of local neighborhoods defined for each point of the image to be studied. These so-called General Adaptive Neighborhoods (GANs) are simultaneously adaptive with the spatial structures, the analyzing scales and the physical settings of the image to be addressed and/or the human visual system. After a brief theoretical introductory survey, the GANIP approach will be successfully applied on real application examples in image restoration, enhancement and segmentation

General Adaptive Neighborhood Image Processing. Part I: Introduction and Theoretical Aspects

30 pagesInternational audienceThe so-called General Adaptive Neighborhood Image Processing (GANIP) approach is presented in a two parts paper dealing respectively with its theoretical and practical aspects. The Adaptive Neighborhood (AN) paradigm allows the building of new image processing transformations using context-dependent analysis. Such operators are no longer spatially invariant, but vary over the whole image with ANs as adaptive operational windows, taking intrinsically into account the local image features. This AN concept is here largely extended, using well-defined mathematical concepts, to that General Adaptive Neighborhood (GAN) in two main ways. Firstly, an analyzing criterion is added within the definition of the ANs in order to consider the radiometric, morphological or geometrical characteristics of the image, allowing a more significant spatial analysis to be addressed. Secondly, general linear image processing frameworks are introduced in the GAN approach, using concepts of abstract linear algebra, so as to develop operators that are consistent with the physical and/or physiological settings of the image to be processed. In this paper, the GANIP approach is more particularly studied in the context of Mathematical Morphology (MM). The structuring elements, required for MM, are substituted by GAN-based structuring elements, fitting to the local contextual details of the studied image. The resulting transforms perform a relevant spatially-adaptive image processing, in an intrinsic manner, that is to say without a priori knowledge needed about the image structures. Moreover, in several important and practical cases, the adaptive morphological operators are connected, which is an overwhelming advantage compared to the usual ones that fail to this property

Morphological tools for spatial and multiscale analysis of passive microwave remote sensing data

International audienceEarth Observation through microwave radiometry is particularly useful for various applications, e.g., soil moisture, ocean salinity, or sea ice cover. However, most of the image processing/data analysis techniques aiming to provide automatic measurement from remote sensing data do not rely on any spatial information, similarly to the early years of opti-cal/hyperspectral remote sensing. After more than a decade of research, it has been observed that spatial information can very significantly improve the accuracy of land use/land cover maps. In this context, the goal of this paper is to propose a few insights on how spatial information can benefit to (passive) microwave remote sensing. To do so, we focus here on mathematical morphology and provide some illustrative examples where morphological operators can improve the processing and analysis of microwave radiometric information. Such tools had great influence on multispectral/hyperspectral remote sensing in the past, and are expected to have a similar impact in the microwave field in the future, with the launch of upcoming missions with improved spatial resolution, e.g. SMOS-NEXT

COASTLINE EXTRACTION IN VHR IMAGERY USING MATHEMATICAL MORPHOLOGY WITH SPATIAL AND SPECTRAL KNOWLEDGE

In this article, we are dealing with the problem of coastline extraction in Very High Resolution (VHR) multispectral images (Quickbird) on the Normandy Coast (France). Locating precisely the coastline is a crucial task in the context of coastal resource management and planning. In VHR imagery, some details on coastal zone become visible and the coastline definition depends on the geomorphologic context. According to the type of coastal units (sandy beach, wetlands, dune, cliff), several definitions for the coastline has to be used. So in this paper we propose a new approach in two steps based on morphological tools to extract coastline according to their context. More precisely, we first perform two detections of possible coastline pixels (respectively without false positive and without false negative). To do so, we apply a recent extension to multivariate images of the hit-or-miss transform, the morphological template matching tool, and rely on expert knowledge to define the sought templates. We then combine these two results through a double thresholding procedure followed by a final marker-based watershed to locate the exact coastline. In order to assess the performance and reliability of our method, results are compared with some ground-truth given by expert visual analysis. This comparison is made both visually and quantitatively. Results show the high performance of our method and its relevance to the problem under consideration

Pure and achromatic spin-orbit shaping of light from Fresnel reflection off space-variant anisotropic media

International audienceWe propose an approach to achieve achromatic pure geometric phase shaping of light from anisotropic media by exploiting the fundamental laws of electromagnetic waves. Its practical implementation requires a cover layer made of a dispersive isotropic medium. The approach applies to any anisotropic material and any geometric phase spatial distribution and preserves the geometric phase reversal upon the reversal of the incident polarization handedness. An experimental demonstration is made over the whole visible range but can be extended to any wavelength range without conceptual issues

A New Spatio-Spectral Morphological Segmentation For Multi-Spectral Remote-Sensing Images

International audienceA general framework of spatio-spectral segmentation for multi-spectral images is introduced in this paper. The method is based on classification-driven stochastic watershed (WS) by Monte Carlo simulations, and it gives more regular and reliable contours than standard WS. The present approach is decomposed into several sequential steps. First, a dimensionality-reduction stage is performed using the factor-correspondence analysis method. In this context, a new way to select the factor axes (eigenvectors) according to their spatial information is introduced. Then, a spectral classification produces a spectral pre-segmentation of the image. Subsequently, a probability density function (pdf) of contours containing spatial and spectral information is estimated by simulation using a stochastic WS approach driven by the spectral classification. The pdf of the contours is finally segmented by a WS controlled by markers from a regularization of the initial classification

Automatic classification of skin lesions using color mathematical morphology-based texture descriptors

SPIE : Society of Photo-Optical Instrumentation EngineersInternational audienceIn this paper an automatic classification method of skin lesions from dermoscopic images is proposed. This method is based on color texture analysis based both on color mathematical morphology and Kohonen Self-Organizing Maps (SOM), and it does not need any previous segmentation process. More concretely, mathematical morphology is used to compute a local descriptor for each pixel of the image, while the SOM is used to cluster them and, thus, create the texture descriptor of the global image. Two approaches are proposed, depending on whether the pixel descriptor is computed using classical (i.e. spatially invariant) or adaptive (i.e. spatially variant) mathematical morphology by means of the Color Adaptive Neighborhoods (CANs) framework. Both approaches obtained similar areas under the ROC curve (AUC): 0.854 and 0.859 outperforming the AUC built upon dermatologists' predictions (0.792)

3D morphological modeling of concrete using multiscale Poisson polyhedra

Supplementary file (library of Poisson polyhedra) available at: https://people.cmm.minesparis.psl.eu/users/willot/PoissonLibrary.tgzInternational audienceThis paper aims at developing a random morphological model for concrete mi-crostructures. A 3D image of concrete is obtained by micro-tomography and is used in conjunction with the concrete formulation to build and validate the model through morphological measurements. The morphological model is made up of two phases, cor-responding to the matrix, or cement paste and to the aggregates. The set of aggregates in the sample is modeled as a combination of Poisson polyhedra of different scales. An algorithm is introduced to generate polyhedra packings in the continuum space. The latter is validated with morphological measurements