Jean-Pierre Da Costa

Estimating local multiple orientations

This paper focuses on the estimation of local orientation in an image where several orientations exist at the same location and at the same scale. Within this framework, Isotropic and Recursive Oriented Network (IRON), an operator based on an oriented network of parallel lines is introduced. IRON uses only a few parameters. Beyond the choice of a specific line homogeneity feature, the size and the shape of the network can be tuned. These parameters allow us to adapt our operator to the image studied. The implementation we propose for the network is recursive, relying on the rotation of the image instead of the rotation of the operator. IRON can proceed on a small computing support, and thus provides a local estimation of orientations. Herein, we test IRON on both synthetic and real images. Compared to some other orientation estimation methods such as Gabor filters or Steerable filters, our operator detects multiple orientations with both better accuracy and noise robustness, at a competitive computational cost thanks to its recursivity. Moreover, IRON offers better selectivity, particularly at small scale.

An image-guided atomistic reconstruction of pyrolytic carbons

A method for the generation of atomistic models of dense nanotextured carbons is presented. This method is based on the statistical analysis of high resolution transmission electron microscopy images and their three-dimensional (3D) extension through image synthesis under constraint. The resulting 3D images then serve as an external potential bringing the atoms to settle preferentially on the black areas during a conventional simulated annealing simulation. Application of this method to the case of two laminar pyrocarbons, differing in their degree of disorder, highlights the promising nature of this approach.

Analyses of the Temporal Dynamics of Fungal Communities Colonizing the Healthy Wood Tissues of Esca Leaf-Symptomatic and Asymptomatic Vines

Esca, a Grapevine Trunk Disease (GTD), is of major concern for viticulture worldwide. Our study compares the fungal communities that inhabit the wood tissues of vines that expressed or not foliar esca-symptoms. The trunk and rootstock tissues were apparently healthy, whether the 10 year-old plants were symptomatic or not. The only difference was in the cordon, which contained white rot, a typical form of esca, in 79% of symptomatic plants. Observations over a period of one year using a fingerprint method, Single Strand Conformation Polymorphism (SSCP), and the ITS-DNA sequencing of cultivable fungi, showed that shifts occurred in the fungal communities colonizing the healthy wood tissues. However, whatever the sampling time, spring, summer, autumn or winter, the fungi colonizing the healthy tissues of asymptomatic or symptomatic plants were not significantly different. Forty-eight genera were isolated, with species of Hypocreaceae and Botryosphaeriaceae being the most abundant species. Diverse fungal assemblages, made up of potentially plant-pathogenic and -protective fungi, colonized these non-necrotic tissues. Some fungi, possibly involved in GTD, inhabited the non-necrotic wood of young plants, but no increase in necrosis areas was observed over the one-year period.

A new adaptive framework for unbiased orientation estimation in textured images

This paper focuses on directional texture analysis. We propose a new approach for orientation estimation. This approach hinges on two classes of convolution masks, i.e. the gradient and the valleyness operators. We provide a framework for their optimization regarding bias reduction and noise robustness. As the gradient and the valleyness operators are complementary, we propose a combination named GV-JOE. This combination consists in using the gradient on inflexion pixels, the valleyness on crests and valleys, and a linear mixture of both elsewhere. We implement an adaptive selection of the size of our operators, in order to take into account the variations of the texture scale in the image. We apply our approach both on synthetic and natural textures. These experiments show that, when used separately, both classes of operators are more accurate than classical derivative approaches. In noisy cases, the GV-JOE implementation improves the robustness of our operators without affecting their accuracy. Moreover, compared to well-known orientation estimators, it gives the best estimates in the most difficult cases i.e. for high-frequency textures and low SNR.

Nonlinear prediction by kriging, with application to noise cancellation

Abstract A semi-parametric approach based on kriging is suggested for nonlinear prediction. It does not rely on any specific model structure, which makes the approach much more flexible than those based on parametric behavioural models. At the same time, accurate predictions are obtained for extremely short-training sequences. Various examples are presented to illustrate the robustness of the method, with a discussion of the prior choices concerning the parametric part of the model. Application on real data is considered in the context of a noise-cancellation problem in underwater acoustics.

Texture Synthesis Using the Structure Tensor

This paper proposes a two-stage texture synthesis algorithm. At the first stage, a structure tensor map carrying information about the local orientation is synthesized from the exemplars data and used at the second stage to constrain the synthesis of the texture. Keeping in mind that the algorithm should be able to reproduce as faithfully as possible the visual aspect, statistics, and morphology of the input sample, the method is tested on various textures and compared objectively with existing methods, highlighting its strength in successfully synthesizing the output texture in many situations where traditional algorithms fail to reproduce the exemplars patterns. The promising results pave the way towards the synthesis of accurately large and multi-scale patterns as it is the case for carbon material samples showing laminar structures, for example.

Synthesis of solid textures based on a 2D example: application to the synthesis of 3D carbon structures observed by transmission electronic microscopy

We propose a novel parametric approach which aims at the synthesis of anisotropic solid textures from the analysis of a single 2D exemplar. This approach is an extension of the pyramidal scheme of Portilla and Simoncelli. It proceeds in three main steps: first, a 2D analysis of the example is performed which produces a set of reference statistics. Then, 3D reference statistics are inferred from the 2D ones thanks to specific anisotropy assumptions. The final step aims at the synthesis itself: the 3D target statistics are imposed on a random 3D block according to a specific multi resolution pyramidal scheme. The approach is applied to the synthesis of solid textures representative of the structure of dense pre-graphitic carbons. The samples are lattice fringe images obtained by high resolution transmission electronic microscopy (HRTEM). HRTEM samples with increasing structural order are used for the experimental evaluation. The produced solid textures exhibit anisotropy properties similar to those observed in the HRTEM samples. Such an approach can easily be extended to any 3D anisotropic structures showing stacks of layers such as wood grain images, seismic data, etc.

Structure tensor based synthesis of directional textures for virtual material design

Exemplar-based texture synthesis schemes are promising for virtual material design. They provide impressive results in many cases, but fail in difficult situations with large and multi-scale patterns, or with long range directional variations. Since a prior synthesis of a geometric layer may help in the synthesis of the texture layer, a two-stage structure/texture synthesis algorithm is proposed. At the first stage, a structure tensor map carrying information about the local orientation is synthesized from the exemplars data, and at the second stage, the synthesized tensor field is used to constrain the synthesis of the texture. Results show that the proposed approach not only yields better synthesized textures, but also successfully synthesizes the output texture in many situations where traditional algorithms fail to reproduce the exemplars patterns, which paves the way towards the synthesis of accurately large and multi-scale patterns as it is the case for pyrolytic carbon samples showing laminar structures observed by Transmission Electronic Microscopy.

Using orthogonal least squares identification for adaptive nonlinear filtering of GSM signals

The miniaturization of GSM handsets creates nonlinear acoustical echoes between the microphone and the loudspeaker when the signal level is high. Nonlinear adaptive filtering can tackle this problem but the computational complexity has to be reduced by restricting the number of coefficients introduced by the nonlinear models. This paper compares the performance of different nonlinear models. In a first training stage we use the OLS (orthogonal least squares) identification method to find models using the fewest coefficients along with a good fitting accuracy. In a second filtering stage these parsimonious models are used to adaptively filter the GSM signals.

Structure Tensor Riemannian Statistical Models for CBIR and Classification of Remote Sensing Images

This paper deals with parametric techniques for the description of texture on very high resolution (VHR) remote sensing images. These techniques focus on the property of anisotropy as described by the local structure tensor (LST). The novelty of this paper consists in proposing several comprehensive statistical frameworks to handle LST fields for rotation-invariant texture discrimination tasks. These frameworks are all based on probability models defined on the Riemannian manifold of positive definite matrices: a recent Riemannian Gaussian model on the affine-invariant metric space and a multivariate Gaussian distribution on the Log-Euclidean space. A thorough comparison of the proposed methods is performed with respect to some state-of-the-art texture analysis methods. Three experimental protocols are considered based on VHR remote sensing data. The first one consists of a content-based image retrieval (CBIR) protocol for browsing oyster field patches. The second one concerns a supervised classification protocol for grouping maritime pine forest stands in different age classes. The third one is, again, a CBIR protocol performed on the UC Merced land use/land cover patch collection. Tensor-based approaches show similar or even better results than the state-of-the-art texture analysis methods considered for comparison in all the experimental contexts.