Marc Acheroy

A versatile wavelet domain noise filtration technique for medical imaging

We propose a robust wavelet domain method for noise filtering in medical images. The proposed method adapts itself to various types of image noise as well as to the preference of the medical expert; a single parameter can be used to balance the preservation of (expert-dependent) relevant details against the degree of noise reduction. The algorithm exploits generally valid knowledge about the correlation of significant image features across the resolution scales to perform a preliminary coefficient classification. This preliminary coefficient classification is used to empirically estimate the statistical distributions of the coefficients that represent useful image features on the one hand and mainly noise on the other. The adaptation to the spatial context in the image is achieved by using a wavelet domain indicator of the local spatial activity. The proposed method is of low complexity, both in its implementation and execution time. The results demonstrate its usefulness for noise suppression in medical ultrasound and magnetic resonance imaging. In these applications, the proposed method clearly outperforms single-resolution spatially adaptive algorithms, in terms of quantitative performance measures as well as in terms of visual quality of the images.

Automatic 3D face authentication

Abstract This paper presents automatic face authentication from facial surface analysis. This geometrical approach was motivated by difficulties encountered when considering frontal face recognition. Apart from being less sensitive to viewpoint and lighting conditions, the method exploits information which is complementary to gray level based approaches, enabling the fusion with those techniques. A 3D acquisition system based on structured light and adapted to facial surface capture is presented. It is cheap and fast while offering a sufficient resolution for face recognition purposes. The acquisition system and the 3D face comparison algorithm were designed to be integrated in security applications with cooperative scenario.

Texture classification using Gabor filters

Abstract An unsupervised texture classification scheme is proposed in this paper. The texture features are based on the image local spectrum which is obtained by a bank of Gabor filters. The fuzzy clustering algorithm is used for unsupervised classification. In many applications, this algorithm depends on assumptions made about the number of subgroups present in the data. Therefore we discuss ideas behind cluster validity measures and propose a method for choosing the optimal number of clusters.

Face verification from 3D and grey level clues

Abstract We address in this paper automatic face verification from 3D facial surface and grey level analysis. 3D acquisition is performed by a structured light system, adapted to face capture and allowing grey level acquisition in alignment. The 3D facial shapes are compared and the residual error after 3D matching is used as a first similarity measure. A second similarity measure is derived from grey level comparison. As expected, fusing 3D and intensity information increases verification performances. The acquisition system, the 3D and grey level comparison algorithms were designed to be integrated in security applications in which individuals cooperate.

Multi-modal identity verification using expert fusion

The contribution of this paper is to compare paradigms coming from the classes of parametric, and non-parametric techniques to solve the decision fusion problem encountered in the design of a multi-modal biometrical identity verification system. The multimodal identity verification system under consideration is built of d modalities in parallel, each one delivering as output a scalar number, called score, stating how well the claimed identity is verified. A decision fusion module receiving as input the d scores has to take a binary decision: accept or reject the claimed identity. We have solved this fusion problem using parametric and non-parametric classifiers. The performances of all these fusion modules have been evaluated and compared with other approaches on a multi-modal database, containing both vocal and visual biometric modalities. ” 2000 Elsevier Science B.V. All rights reserved.

Fusion of PolSAR and PolInSAR data for land cover classification.

Abstract The main research goal of this study is to investigate the complementarity and fusion of different frequencies (L- and P-band), polarimetric SAR (PolSAR) and polarimetric interferometric (PolInSAR) data for land cover classification. A large feature set was derived from each of these four modalities and a two-level fusion method was developed: Logistic regression (LR) as ‘feature-level fusion’ and the neural-network (NN) method for higher level fusion. For comparison, a support vector machine (SVM) was also applied. NN and SVM were applied on various combinations of the feature sets. The results show that for both NN and SVM, the overall accuracy for each of the fused sets is better than the accuracy for the separate feature sets. Moreover, that fused features from different SAR frequencies are complementary and adequate for land cover classification and that PolInSAR is complementary to PolSAR information and that both are essential for producing accurate land cover classification.

Optimization of fuzzy expert systems using genetic algorithms and neural networks

In this paper, fuzzy logic theory is used to build a specific decision-making system for heuristic search algorithms. Such algorithms are typically used for expert systems. To improve the performance of the overall system, a set of important parameters of the decision-making system is identified. Two optimization methods for the learning of the optimum parameters, namely genetic algorithms and gradient-descent techniques based on a neural network formulation of the problem, are used to obtain an improvement of the performance. The decision-making system and both optimization methods are tested on a target recognition system. >

Feature extraction using the constrained gradient

Abstract Low-level operators are needed in most computer vision applications in order to get relevant image primitives. In this paper, we present a line and a corner detector. Both operators use specific constraints on the gradient of the image intensity. The operators are applied to satellite and aerial images. The line detector is very useful for extracting roads even on the noisy SAR images, while the corner detector enables to detect salient points such as corners of buildings in aerial images. The information brought by these detectors completes the edge-based description of an image.

Automatic Face Authentication from 3D surface

This paper presents automatic face authentication based on facial surface analysis. The success of a previous profile-based approach, exclusively relying on geometrical features of the external contour, led us to consider the full facial surface. This motivation was further supported by the independence of viewpoint and lighting conditions of 3D information. The geometry also carries information which is complementary to grey-level based approaches, supporting the combination with those techniques. The facial surface is captured by a system based on structured light and adapted to face to deliver a cheap, fast and sufficiently precise solution. Typical applications concern security in cooperative situations.

Improving mine recognition through processing and Dempster–Shafer fusion of ground-penetrating radar data

A methodfor modeling andcombination of measures extractedfrom a ground-penetrating radar (GPR) in terms of belief functions within the Dempster-Shafer framework is presentedandillustratedon a real GPR data set. A starting point in the analysis is a preprocessed C-scan of a sand-lane containing some mines andfalse alarms. In order to improve the selection of regions of interest on such a preprocessed C-scan, a methodfor detecting suspectedareas is developed, basedon region analysis aroundthe local maxima. Once the regions are selected, a detailedanalysis of the chosen measures is performed for each of them. Two sets of measures are extracted and modeledin terms of belief functions. Finally, for every suspected region, masses assigned by each of the measures are combined, leading to a first guess on whether there is a mine or a non-dangerous object in the region. The region selection methodimproves detection, while the combination methodresults in significant improvements, especially in eliminating most of the false alarms.