Annick Montanvert

Hierarchical image analysis using irregular tessellations

A novel multiresolution image analysis technique based on hierarchies of irregular tessellations generated in parallel by independent stochastic processes is presented. Like traditional image pyramids these hierarchies are constructed in a number of steps on the order of log(image-size) steps. However, the structure of a hierarchy is adapted to the image content and artifacts of rigid resolution reduction are avoided. Two applications of these techniques are presented: connected component analysis of labeled images and segmentation of gray level images. In labeled images, every connected component is reduced to a separate root, with the adjacency relations among the components also extracted. In gray level images the output is a segmentation of the image into a small number of classes as well as the adjacency graph of the classes. >

Deformable meshes with automated topology changes for coarse-to-fine three-dimensional surface extraction

This work presents a generic deformable model for extracting objects from volumetric data with a coarse-to-fine approach. This model is based on a dynamic triangulated surface which alters its geometry according to internal and external constraints to perform shape recovery. A new framework for topology changes is proposed to extract complex objects: within this framework, the model dynamically adapts its topology to the geometry of its vertices according to simple distance constraints. In order to speed up the process, an algorithm of pyramid construction with any reduction factor transforms the image into a set of images with progressively higher resolutions. This organization into a hierarchy, combined with a model which can adapt its sampling to the resolution of the workspace, enables a fast estimation of the shapes included in the image. After that, the model searches for finer and finer details while relying successively on the different levels of the pyramid.

Continuous Analogs of Digital Boundaries: A Topological Approach to Iso-Surfaces

The definition and extraction of objects and their boundaries within an image are essential in many imaging applications. Classically, two approaches are followed. The first considers the image as a sample of a continuous scalar field: boundaries are implicit surfaces in this field; they are often called iso-surfaces. The second considers the image as a digital space with adjacency relations and classifies elements of this space as inside or outside: boundaries are pairs composed of one inside element and one outside element; they are called digital boundaries. In this paper, we show that these two approaches are closely related. This statement holds for arbitrary dimensions. To do so, we propose a local method to construct a continuous analog of a digital boundary. The continuous analog is designed to satisfy properties in the Euclidean space that are similar to the properties of its counterpart in the digital space (e.g., connectedness, closeness, separation). It appears that this continuous analog is indeed a piecewise linear approximation of an iso-(hyper)surface (i.e., a triangulated iso-surface in the three-dimensional case). Furthermore, we derive significant digital boundary properties from its continuous analog using the Jordan–Brouwer separation theorem: new Jordan pairs, new adjacencies between boundary elements, new Jordan triples. We conclude this paper by illustrating the 3D case more precisely. In particular, we show that a digital boundary can be transformed directly into a triangulated iso-surface. The implementation of this transformation and its efficiency are discussed with a comparison with the classical marching-cubes algorithm.

Indusion: Fusion of Multispectral and Panchromatic Images Using the Induction Scaling Technique

The fusion of multispectral (MS) and panchromatic (PAN) images is a useful technique for enhancing the spatial quality of low-resolution MS images. Liu recently proposed the smoothing-filter-based intensity modulation (SFIM) fusion technique. This technique upscales MS images using bicubic interpolation and introduces high-frequency information of the PAN image into the MS images. However, this fusion technique is plagued by blurred edges if the upscaled MS images are not accurately coregistered with the PAN image. In the first part of this letter, we propose the use of the Induction scaling technique instead of bicubic interpolation to obtain sharper, better correlated, and hence better coregistered upscaled images. In the second part, we propose a new fusion technique derived from induction, which is named ldquoIndusion.rdquo In this method, the high-frequency content of the PAN image is extracted using a pair of upscaling and downscaling filters. It is then added to an upscaled MS image. Finally, a comparison of SFIM (with both bicubic interpolation and induction scaling) is presented along with the fusion results obtained by IHS, discrete wavelet transform, and the proposed Indusion techniques using Quickbird satellite images.

Fusion of hyperspectral and panchromatic images using multiresolution analysis and nonlinear PCA band reduction

This article presents a novel method for the enhancement of the spatial quality of hyperspectral (HS) images through the use of a high resolution panchromatic (PAN) image. Due to the high number of bands, the application of a pan-sharpening technique to HS images may result in an increase of the computational load and complexity. Thus a dimensionality reduction preprocess, compressing the original number of measurements into a lower dimensional space, becomes mandatory. To solve this problem, we propose a pan-sharpening technique combining both dimensionality reduction and fusion, making use of non-linear principal component analysis (NLPCA) and Indusion, respectively, to enhance the spatial resolution of a HS image. We have tested the proposed algorithm on HS images obtained from CHRIS-Proba sensor and PAN image obtained from World view 2 and demonstrated that a reduction using NLPCA does not result in any significant degradation in the pan-sharpening results.

Technical Section: A fast roughness-based approach to the assessment of 3D mesh visual quality

We propose in this paper a new objective metric for the visual quality assessment of 3D meshes. The metric can predict the extent of the visual difference between a reference mesh, which is considered to be of perfect quality, and a distorted version. The proposed metric is based on a mesh local roughness measure derived from Gaussian curvature. The perceptual distance between two meshes is computed as the difference between the normalized surface integrals of the local roughness measure. Experimental results from three subjective databases and comparisons with the state of the art demonstrate the efficacy of the proposed metric in terms of the execution time and the correlation with subjective scores. Finally, we show a simple application of the metric in which it is used to automatically determine the optimum quantization level of mesh vertex coordinates.

Hierarchical Image Analysis Using Irregular Tessellations

In this paper we have presented an image analysis technique in which a separate hierarchy is built over every compact object of the input. The approach is made possible by a stochastic decimation algorithm which adapts the structure of the hierarchy to the analyzed image. For labeled images the final description is unique. For gray level images the classes are defined by converging local processes and slight differences may appear. At the apex every root can recover information about the represented object in logirhtmic number of processing steps, and thus the adjacency graph can become the foundation for a reulational model of the scene.

Analyzing and filtering contour deformation

This article presents a general scheme for contour deformation analysis. It is based on a point-to-point multiresolution matching and an original representation of deformation. After a short presentation, we describe how this model allows us to detect and measure motion of articulated objects without any a priori knowledge. Then we show how a frequency analysis of our deformation representation is directly connected to a meaningful distinction between local and global deformations. It allows us to perform deformation analysis and deformation filtering. Filtering allows one to synthesize a contour corresponding to only a selection of the components of an observed deformation.

A framework for image magnification: induction revisited

An original image magnification method called induction was proposed recently, whose specificity is to state the problem of image magnification as an inverse problem of image reduction. The methods usually employed, like interpolation, fail to verify this constraint, which provides a formalism for magnification and a framework for evaluating the quality of the enlarged images. In this paper, we revisit the induction through a new interpretation using wavelets. We put forward major improvements including a direct implementation, much more efficient than the iterative algorithm proposed previously.

Fusion of Hyperspectral and panchromatic images using multiresolution analysis and nonlinear PCA band reduction

This paper presents a novel method for the enhancement of spatial quality of Hyperspectral (HS) images while making use of a high resolution panchromatic (PAN) image. Due to the high number of bands the application of a pansharpening technique to HS images may result in an increase of the computational load and complexity. Thus a dimensionality reduction preprocess, compressing the original number of measurements into a lower dimensional space, becomes mandatory. To solve this problem we propose a pansharpening technique combining both dimensionality reduction and fusion, exploited by non-linear Principal Component Analysis (NLPCA) and Indusion respectively, to enhance the spatial resolution of a hyperspectral image.