Christine Fernandez-Maloigne

Evidential segmentation scheme of multi-echo MR images for the detection of brain tumors using neighborhood information

In this paper we propose and study an evidential segmentation scheme of multi-echo MR images for the detection of brain tumors. We show that the modeling by means of evidence theory is well suited to the processing of redundant and complementary data as the MR images. Moreover neighborhood relationship between voxels is taken into account via Dempsters combination rule. We show that using this information improves the classification results previously obtained and leads to a real region-based segmentation. Moreover, the combination of spatial information allows to compute a measure of conflict, which reflects the spatial organization of the data: the conflict is higher at the boundaries between different structures. Thus, it provides a new source of evidence that the specialist can aggregate with the segmentation results to soften its own decision.

A Bandelet-Based Inpainting Technique for Clouds Removal From Remotely Sensed Images

It is well known that removing cloud-contaminated portions of a remotely sensed image and then filling in the missing data represent an important photo editing cumbersome task. In this paper, an efficient inpainting technique for the reconstruction of areas obscured by clouds or cloud shadows in remotely sensed images is presented. This technique is based on the Bandelet transform and the multiscale geometrical grouping. It consists of two steps. In the first step, the curves of geometric flow of different zones of the image are determined by using the Bandelet transform with multiscale grouping. This step allows an efficient representation of the multiscale geometry of the images structures. Having well represented this geometry, the information inside the cloud-contaminated zone is synthesized by propagating the geometrical flow curves inside that zone. This step is accomplished by minimizing a functional whose role is to reconstruct the missing or cloud contaminated zone independently of the size and topology of the inpainting domain. The proposed technique is illustrated with some examples on processing aerial images. The obtained results are compared with those obtained by other clouds removal techniques.

Spatial and spectral quaternionic approaches for colour images

Hypercomplex or quaternions numbers have been used recently for both greyscale and colour image processing. Fast, numerous hypercomplex 2D Fourier transforms were presented as a generalization of the complex 2D Fourier transform to this new hypercomplex space. Thus, the major problem was to put an interpretation of what information the Fourier coefficients could provide. In this paper, we first define the conditions on the spectrum coefficients needed to reconstruct a colour image without loss of information through the inverse quaternionic Fourier transform process. The result is used to interpret the quaternionic spectrum coefficients of this specific colour Fourier transform. Secondly, with this apprehension of the quaternion numbers and the corresponding colour spectrum space, we define spatial and frequential strategies to filter colour images.

Vector Extension of Monogenic Wavelets for Geometric Representation of Color Images

Monogenic wavelets offer a geometric representation of grayscale images through an AM-FM model allowing invariance of coefficients to translations and rotations. The underlying concept of local phase includes a fine contour analysis into a coherent unified framework. Starting from a link with structure tensors, we propose a nontrivial extension of the monogenic framework to vector-valued signals to carry out a nonmarginal color monogenic wavelet transform. We also give a practical study of this new wavelet transform in the contexts of sparse representations and invariant analysis, which helps to understand the physical interpretation of coefficients and validates the interest of our theoretical construction.

Advanced Color Image Processing and Analysis

This volume does much more than survey modern advanced color processing. Starting with a historical perspective on ways we have classified color, it sets out the latest numerical techniques for analyzing and processing colors, the leading edge in our search to accurately record and print what we see. The human eye perceives only a fraction of available light wavelengths, yet we live in a multicolor world of myriad shining hues. Colors rich in metaphorical associations make us purple with rage or green with envy and cause us to see red. Defining colors has been the work of centuries, culminating in todays complex mathematical coding that nonetheless remains a work in progress: only recently have we possessed the computing capacity to process the algebraic matrices that reproduce color more accurately. With chapters on dihedral color and image spectrometers, this book provides technicians and researchers with the knowledge they need to grasp the intricacies of todays color imaging.

Feature points tracking: robustness to specular highlights and lighting changes

Since the precise modeling of reflection is a difficult task, most feature points trackers assume that objects are lambertian and that no lighting change occurs. To some extent, a few approaches answer these issues by computing an affine photometric model or by achieving a photometric normalization. Through a study based on specular reflection models, we explain explicitly the assumptions on which these techniques are based. Then we propose a tracker that compensates for specular highlights and lighting variations more efficiently when small windows of interest are considered. Experimental results on image sequences prove the robustness and the accuracy of this technique in comparison with the existing trackers. Moreover, the computation time of the tracking is not significantly increased.

A grouplet-based reduced reference image quality assessment

The past decades have witnessed the tremendous growth of digital image processing techniques for visual information representation and communication. Particularly, computational representation of perceived image quality has become a fundamental problem in computer vision and image processing. It is well known that the commonly used Peak Signal-to-Noise Ratio (PSNR), although analysis friendly, falls far short of this need. In this work, we propose a reduced reference (RR) perceptual image quality measure (IQM) based on the grouplet transform. Given a reference image and its “distorted” version, we first compute the grouplet transform in order to extract the information of textures and directions of both images. Then, contrast sensitivity function (CSF) filtering is performed to obtain same visual sensitivity information within both images. Thereafter, based on the properties of the human visual system (HVS), rational sensitivity thresholding is performed to obtain the sensitivity coefficients of both images. Finally, RR image quality assessment (IQA) is performed by comparing the sensitivity coefficients of both images.

Color image watermarking with adaptive strength of insertion

The paper presents a watermarking technique, specific to color images. The insertion and the detection are based on the 2D discrete wavelet transform, applied on each color component. Three color vectors are extracted from the wavelet coefficients. The insertion consists of modifying one vector for each location, with regard to the bit value and the vector triplet. The mark is extracted without the original image, by observing the scheme of each vector triplet. This new method has been shown to be resistant to JPEG compression, median filtering and noise adding. Moreover, each insertion is weighted by an adaptive strength, obtained by a retroactive process between the original and the watermarked images. This process allows optimizing the compromise between invisibility and robustness, considering the local image color content.

Texture and neural network for road segmentation

Road recognition is a main component in autonomous vehicle guidance system. We aim to determine whether an image element is part of the road or not, by using information from the elements proximity. The wide range of situations have to be processed is a problem, and they impose on the system a capacity of self-adaptation to new cases. A neural network based architecture has been chosen to implement the texture analysis. By applying a local texture recognition process to the whole image, we obtain a segmented image of the road.

Investigation and modeling of visual fatigue caused by S3D content using eye-tracking

3D has been one of the most important technologies of the last decade having an exponential progression. Nevertheless, several problems such as visual discomfort and visual fatigue have slowed its progression for homes. This factor decreases significantly the overall quality of experience from watching 3D content and reduces the level of satisfaction of a user. This study explores the accumulation of visual fatigue when watching 3D video in close to real-life conditions. In order to obtain more information about visual discomfort, an hour of eye-tracking experiments have been conducted. Investigations have been made by analyzing information provided both by users through questionnaires and visual gaze characteristics recording. Obtained results are compared to data produced when watching 2D. The deep statistical analysis showed that time and video content have an influence on video fatigue accumulation and visual functions. With respect to the obtained results, a model has been proposed based on video characteristics (motion activity, disparity range and changes) and the previous state of visual fatigue.