Texture characterization from the metrological point of view is addressed in order to establish a physically relevant and directly interpretable feature. In this regard, a generic formulation is proposed to simultaneously capture the spectral and spatial complexity in hyperspectral images. The feature, named relative spectral difference occurrence matrix (RSDOM) is thus constructed in a multireference, multidirectional, and multiscale context. As validation, its performance is assessed in three versatile tasks. In texture classification on HyTexiLa, content-based image retrieval (CBIR) on ICONES-HSI, and land cover classification on Salinas, RSDOM registers 98.5% accuracy, 80.3% precision (for the top 10 retrieved images), and 96.0% accuracy (after post-processing) respectively, outcompeting GLCM, Gabor filter, LBP, SVM, CCF, CNN, and GCN. Analysis shows the advantage of RSDOM in terms of feature size (a mere 126, 30, and 20 scalars using GMM in order of the three tasks) as well as metrological validity in texture representation regardless of the spectral range, resolution, and number of bands.

Color is very important feature for image representation, it assumes very essential in the human visual recognition process. Although, several color extension of Local Binary Pattern processes the color texture from the three color channels separately (Marginal way). Aware of the high interaction that exists between different channels in the color image, this work introduces a new vector process to define a local descriptor for color images named Local Binary Quaternion Rotation Pattern (LBQRP). In this LBQRP purpose, the quaternion representation is used to represent color texture. The distance between two color can be expressed as the angle of rotation between two unit quaternions using the geodesic distance. After a LBQRP coding, local histograms are extracted and used as features. Experiments on three challenging color datasets: Vistex, Outex-TC13 and USPtex are carried out to evaluate the LBQRP performance in texture classification. Results show the high efficiency of the proposed approach facing to several stat-of-art methods.

A new hyperspectral texture descriptor, Relocated Spectral Difference Occurrence Matrix (rSDOM) is proposed. It assesses the distribution of spectral difference in a given neighborhood. For metrological purposes, rSDOM employs Kullback-Leibler pseudo-divergence (KLPD) for spectral difference calculation. It is generic and adapted for any spectral range and number of band. As validation, a texture classification scheme based on nearest neighbor classifier is applied on HyTexiLa dataset using rSDOM. The performance is close to Opponent Band Local Binary Pattern (OBLBP) with classification accuracy of 94.7%, but at a much-reduced feature size (0.24% of OBLBP's) and computational complexity.

Answering to metrological constraints typically required in the context of industrial and medical applications, a spectral difference space is introduced in this work. In this space, an acquired hyperspectral data is treated as measurements. Then, modelling the spectral difference space as multivariate Normal laws, a Gaussian mixture model is used in a classification task of remote sensing images. An encouraging result is obtained, comparing the proposed space with a data-driven one. Moreover, it offers a starting point in developing a directly interpretable spectral analysis tools.

Gradient extraction is important for a lot of metrological applications such as Control Quality by Vision. In this work, we propose a full-vector gradient for multi-spectral sensors. The full-vector gradient extends Di Zenzo expression to take into account the non-orthogonality of the acquisition channels thanks to a Gram matrix. This expression is generic and independent from channel count. Results are provided for a color and a multi-spectral snapshot sensor. Then, we show the accuracy improvement of the gradient calculation by creating a dedicated objective test and from real images.

In this paper, we study the importance of spectral sensitivity functions in constructing discriminative representation of hyperspectral images (HSI). The main goal of a such representation is to improve image content recognition by focusing the processing only on the most relevant spectral channels. The underlying hypothesis is that for a given category, each image content is better extracted through a specific set of spectral sensitivity functions. In this study, we fixed the number of spectral sensitivity functions to 3 for displaying purposes. Deep features are then extracted from the obtained trichromatic representation of HSI data to build a discriminative image signature. Finally, spectral sensitivity functions are compared in a Content-Based Image Retrieval (CBIR) paradigm. Exhaustive experiments have been conducted on a hyperspectral dataset. Obtained results show the usefulness of the whole spectrum to obtain a discriminative image representation compared to the RGB representation.

Mathematical morphology is a popular framework for non‐linear image processing, first introduced for binary and grey‐level images, then extended to colour and multivariate images. Various pseudo‐morphologies have been proposed as solutions to the problem of ordering multivariate data. Despite the lack of some properties, pseudo‐morphologies have proved useful in various applications, such as filtering or texture classification. The authors propose to improve the existing colour probabilistic pseudo‐morphology by changing the way the local pseudo‐extrema are chosen. They show the usefulness of the new construction in the context of noise reduction in colour images using the open‐close close‐open filter, by highlighting the improvement over the original construction and comparing the authors’ results with other relevant morphological and pseudo‐morphological approaches.

Facing the increasing number of multi and hyperspectral image acquisitions, in particular for medical and industrial applications, we need accurate features to analyse and assess the content complexity in a metrological way. In this paper, we explore an original way to compute texture features for spectral images in a full-band and vector process. To do it, we developed a dedicated approach for Mathematical Morphology using distance function. Thanks to this, we extend the classical mathematical morphology to spectral images. We show in this paper the scientific construction and preliminary results.

In Wireless Sensor Networks, where the communicating nodes are not mobile, the channel variations are mainly temporal and caused by the random displacement of human beings in the environment. In this paper, we propose to model these kinds of variations for transmissions at 433 and 868 MHz according to the number of persons in motion. The proposed approach is independent of the environment geometry and the spatial variability of the channel, but is complementary to the models that take into account these variations. It relies on a narrowband measurements campaign in order to predict the channel behaviour with a statistical identification method. Finally, a temporal modelling compliant to building monitoring applications is used to assess the impact of people motion on the transmission quality through the computation of the associated Bit Error Rates.

The energy efficiency of a wireless sensor depends of three criteria: the transmission power (transmission range), the radio links quality (BER) and finally the message length. In this paper, we propose to reduce the overall consumption of a sensor by adapting the transmission power according to the last two criteria. The originality of our approach is to improve an energy model by the use of a realistic propagation model and a realistic BER estimation. Thus, from this complete model we show the propagation channel influence on the adjustment of the transmission power. The consequences on the energy consumption are illustrated in terms of lifetime on a forwarding sensor. Simulation results demonstrate clearly that significant gains on the lifetime of a sensor are obtained by adjusting the transmission power in function of the frame length.

Fractal dimension is a very useful metric for the analysis of the images with self-similar content, such as textures. For its computation there exist several approaches, the probabilistic algorithm being accepted as the most elegant approach. However, all the existing methods are defined for 1-D signals or binary images, with extension to grayscale images. Our purpose is to propose a color version of the probabilistic algorithm for the computation of the fractal dimension. To validate this new approach, we also propose an extension of the existing algorithm for the generation of probabilistic fractals, in order to obtain color fractal images. Then we show the results of our experiments and conclude this paper.

Lacunarity is a very useful metric for the multi-scale analysis of the images that exhibit fractal properties. For its computation there exist several approaches, the probabilistic algorithm being accepted as the most elegant approach. However, all the existing methods are defined for one dimensional signals or binary images with extension to grey-scale images. We propose a colour expression of the lacunarity based on the probabilistic algorithm for the computation of the fractal dimension. To validate this new approach, we used both synthetic colour fractal images and natural fractal images. We comment our results and spot several issues regarding the interpretation of lacunarity curves, then we conclude.

For a complex writing such as Egyptian hieroglyphs, combining the works done in hierarchical modeling and fuzzy grammar definitions seems natural. This paper introduces the hierarchical-fuzzy-attributed graph (FHAG), extended from fuzzy-attributed graph, which models attributes by fuzzy-tree grammar. We give a formal definition of FHAGs and introduce adapted measurements for matching them. FHAG is then integrated in a recognition system based on single models comparisons for handwritten Egyptian hieroglyphs.