Anne-Sophie Capelle-Laizé

Underwater image enhancement by attenuation inversionwith quaternions

In this paper, an underwater image enhancement method using quaternions is presented. This work aims to improve color rendition and contrast of the objects, as if the scene has been taken out of water. This method is based on light attenuation inversion after processing a color space contraction using quaternions. Applied to the white, the attenuation gives a hue vector caracterizing the water color. Using this reference axis, geometrical transformations into the color space are computed with quaternions. Pixels of water areas of processed images are moved to gray or colors with a low saturation whereas the objects remain fully colored. Thus, the contrast of the observed scene is significantly improved and the difference between the background and the rest of the image is increased, giving a first approach towards a pre-segmentation step.

Perceptual color hit-or-miss transform: application to dermatological image processing

The hit-or-miss transform is a mathematical morphological processing designed to find objects in image. Its extension to grayscale domain is not unique, but Barat’s method is the most appropriate to find specific objects with bandwidth in space and color evolutions. This tolerance is possible with the use of non-flat structuring elements. In this paper, a color extension of the Barat’s method is presented. For this purpose, a new mathematical morphology method, based on the concept of convergence in the CIELAB space and where the definition of non-flat structuring elements is possible, is used. A comparison with the existing approaches in the literature is done. Results are given and commented on synthetic and real images.

Toward a full-band texture features for spectral images

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.

How to specify or identify the most accurate multispectral distance function for mathematical morphology

We developed a distance-based formalism adapted to colour mathematical morphology. To extend the morphological operators to multispectral domain, a suitable distance function between two spectra has to be specified and selected. In order to compare the behaviour of different distance functions, we apply linear transformations on basic spectrum. Then we compare the impact of the distance function choice during morphological process on real spectral images.

Blind image steganalysis based on evidential K-Nearest Neighbors

Blind steganalysis techniques are able to detect the presence of secret messages embedded in digital media files, such as images, video, and audio, with an unknown steganography algorithm. This paper present an image steganalysis method based on Evidential K-Nearest Neighbors (EV-knn). Originality of this work is the use of theoretical framework of Belief functions on different subspaces of features vectors. Classifications obtained in subspaces are combined using specific combination function and to provide classification of a given image (cover or stego). The proposed approach is evaluated with the classical nsf5 steganographic method that hides messages in JPEG images. Compared to Ensemble Classifier steganalysis algorithm, the proposed approach significantly increases the performance of classification.

Toward a Complete Inclusion of the Vector Information in Morphological Computation of Texture Features for Color Images

In this paper, we explore an original way to compute texture features for color images in a vector process. To do it, we used a dedicated approach for color mathematical morphology using distance function. We show in this paper the scientific construction of morphological spectra and preliminary results using Outex database.

Grassland species characterization for plant family discrimination by image processing

Pasture species belonging to poaceae and fabaceae families constitute of essential elements to maintain natural and cultivated regions. Their balance and productivity are key factors for good functioning of the grassland ecosystems. The study is based on a process of image processing. First of all an individual signature is defined while considering geometric characteristics of each family. Then, this signature is used to discriminate between these families. Our approach focuses on the use of shape features in different situations. Specifically, the approach is based on cutting the representative leaves of each plant family. After cutting, we obtain leaves sections of different sizes and random geometry. Then, the shape features are calculated. Principal component analysis is used to select the most discriminatory features. The results will be used to optimize the acquisition conditions. We have a discrimination rate of more than 90% for the experiments carried out in a controlled environment. Experiments are being carried out to extend this study in natural environments.

TBM for color image processing: a quantization algorithm

In this paper, we propose a color image quantization algorithm based upon TBM. In this context, we consider that the color quantization problem can be viewed as clustering problem of the color-space into P clusters. Using TBM, we define a top-down evidential clustering algorithm which iteratively decreases the number of clusters of the color space into P clusters. This convergence is ensured using a novel criterion based upon the pignistic probability function. The P clusters provide the new reduced color palette and a quantized color image is computed. This quantization method is completely automatic and preserves the final result from any initial condition. Experiments on various images show the algorithm efficiency for color quantization and highlight the efficiency of TBM for color image processing

Vector texture features for colour images: Construction and elements of comparison

In this paper, we explore an original way to compute texture features for colour images in a vector process. To do it, we used a dedicated approach for colour mathematical morphology using distance function. We show in this paper the scientific construction of morphological spectra. We compare the obtained morphological spectra using different colour mathematical morphology methods existing in from the literature. First classification results are given using a fractal image database.

Fusion of imprecise data applied to image quality assessment

The estimation of dependence relationships between variables is generally performed using probabilistic models. However, these models are not adapted to imprecise data and they cannot easily take into account symbolic information such as experts opinions. On the contrary, evidence theory also called theory of belief function, allow to integrate these kinds of uncertainties. In this paper we propose regression analysis based on a fuzzy extension of belief function theory, applied to image quality assessment problem. For a given input vector x of relevant images feature, the method provides a prediction regarding the value of the output variable y which represents the score of subjective image quality test, namely the DMOS value. To validate the proposed approach, experiments are conducted on LIVE image database. The proposed measure is compared with algorithms based on general regression as neural networks and Support Vector Machine (SVM). The framework of this paper is of nature subjective and results show that our approach performs well and illustrate the interest of the theory of belief function in this context.