Predictive Maintenance (PdM) for pipe clogging is a critical challenge in the industrial sector, particularly with the increasing adoption of Artificial Intelligence (AI) and the Internet of Things (IoT). Frequent clogging incidents, such as those faced by Orano/La Hague, lead to energy waste , operational inefficiencies, financial losses, and potential safety hazards, highlighting the critical need for effective maintenance solutions to protect both assets and personnel. This study proposes a novel hybrid approach that combines the strengths of data-centric and model-centric methodologies for Prognostic and Health Monitoring (PHM) of pipeline systems in constrained industrial environments. The approach utilizes passive acceleration measurements to predict clogging occurrences and quantify clogging severity under varying airflow rates . Experimental results indicate that the proposed method achieves up to 100% accuracy in clogging detection and robust performance across diverse operational conditions. This integrated methodology represents a significant step forward in predictive maintenance, offering scalable and adaptable solutions to enhance safety, operational efficiency, and cost-effectiveness in industrial settings.

A 3layered skin model is built to simulate hyperspectral reflectance using Monte Carlo simulations based on biological components, which include melanin volume fraction, water level, blood volume fraction, oxygen saturation, etc. A forward neural network is trained for mapping biological components and reflectance. Then a database, which contains 50,000 samples spectra from 450 to 750 nm with randomly given biological components information, are generated by this forward neural network. Support vector regression, inverse neural networks and random forest are applied for the regression analysis of reflectance data and melanin volume fraction. Dimensionality reduction is used to accelerate the training time. The performances of three regression methods are measured and show promising prediction results.

In stereo matching, the correctness of stereo pairs matches, also called confidence, is used to improve the dense disparity estimation result. In this paper, we propose a multi-modal deep learning approach for stereo matching confidence estimation. The input of our method is composed of two modalities, the initial disparity maps, and its reference color image. To effectively combine these two modalities, we explore and study multiple convolutional neural network (CNN) structures for our specific confidence prediction tasks. To the best of our knowledge, this is the first approach reported in the literature combining multiple modalities and patch based deep learning to predict the confidence. The experiments on KITTI datasets demonstrate that our multi-modal confidence network can significantly outperform the state-of-the-art methods.

Recently, trilateral filter based method is more accurate than other local stereo matching methods on Middlebury benchmark. The boundary strength term is the key part of this method, which is computed from a color edge map. In this paper, we prove that boundary strength term, computed from a depth edge map, produces more accurate results. Then, we propose a depth edge detection technique to obtain an accurate depth edge map and present a depth edge based trilateral filter method. The experimental evaluation on the Middlebury benchmark shows that the proposed method outperforms the original trilateral filter method and also other local methods.

Face recognition systems have been widely studied and integrated in various applications. Under certain spoofing attacks such as displaying high-resolution photos/videos or wearing masks made up of non-skin materials, they fail. In this work, we propose a new approach of skin and non-skin classification, based on spectral reflectance, providing more discriminative signatures. This approach allows synthesis of biological skin features based on photo-biophysics model. Here, a simple 2-layered skin model is applied which is proofed to be effective in skin and non-skin classification both from theoretical explanations and experimental results. This approach is compared to direct spectral reflectance analysis and classification. Experimental results can reach a classification rate up to 96.4%.

Adaptive support weight (ASW) methods represent the state of the art in local stereo matching, while the bilateral filter-based ASW method achieves outstanding performance. However, this method fails to resolve the ambiguity induced by nearby pixels at different disparities but with similar colors. In this paper, we introduce a novel trilateral filter (TF)-based ASW method that remedies such ambiguities by considering the possible disparity discontinuities through color discontinuity boundaries, i.e., the boundary strength between two pixels, which is measured by a local energy model. We also present a recursive TF-based ASW method whose computational complexity is O(N) for the cost aggregation step, and O(NLog2(N)) for boundary detection, where N denotes the input image size. This complexity is thus independent of the support window size. The recursive TF-based method is a nonlocal cost aggregation strategy. The experimental evaluation on the Middlebury benchmark shows that the proposed method, whose average error rate is 4.95%, outperforms other local methods in terms of accuracy. Equally, the average runtime of the proposed TF-based cost aggregation is roughly 260 ms on a 3.4-GHz Inter Core i7 CPU, which is comparable with state-of-the-art efficiency.

International audience

Recently, a novel Non-Local Cost Aggregation (NLCA) algorithm based on a minimum spanning tree (MST) has been proposed [1] for stereo matching providing extremely low computational complexity and outstanding performance. However, since the MST is constructed only based on color cue, this approach suffers from some improper connections at the boundaries of two objects with similar color distribution. In this paper, we propose a new weight function which includes not only the color cue but also the boundary cue of the reference image. Because the boundary cue is more discriminative than the color cue to distinguish the object from the background with similar color, the previous improper connections can be avoided and a more faithful MST can be constructed. Experimental results performed on Middlebury benchmark demonstrate the effectiveness of the improvements. The improved algorithm achieves rank 18th out of 143 submissions, while the original algorithm ranks 31th.

This paper presents an effective method for 3-D face recognition using a novel geometric facial representation along with a local feature hybrid matching scheme. The proposed facial surface description is based on a set of facial depth maps extracted by multiscale extended Local Binary Patterns (eLBP) and enables an efficient and accurate description of local shape changes; it thus enhances the distinctiveness of smooth and similar facial range images generated by preprocessing steps. The following matching strategy is SIFT-based and performs in a hybrid way that combines local and holistic analysis, robustly associating the keypoints between two facial representations of the same subject. As a result, the proposed approach proves robust to facial expression variations, partial occlusions, and moderate pose changes, and the last property makes our system registration-free for nearly frontal face models. The proposed method was experimented on three public datasets, i.e. FRGC v2.0, Gavab, and Bosphorus. It displays a rank-one recognition rate of 97.6% and a verification rate of 98.4% at a 0.001 FAR on the FRGC v2.0 database without any face alignment. Additional experiments on the Bosphorus dataset further highlight the advantages of the proposed method with regard to expression changes and external partial occlusions. The last experiment carried out on the Gavab database demonstrates that the entire system can also deal with faces under large pose variations and even partially occluded ones, when only aided by a coarse alignment process.

In this study, we present a novel geometric representation for 3D faces in order to enhance distinctiveness of generally smooth range images. This novel face representation is based on Multi-Scale Extended Local Binary Patterns (ELBP) and enables accurate and fast description of local shape variations on range faces. When associated with the proposed SIFT-based local feature matching scheme, this novel geometric facial representation shows its discriminative power in 3D face recognition, displaying a rank-one recognition rate up to 97.2% and a verification rate of 98.4% at a 0.001 FAR respectively on the FRGC v2.0 database. Moreover, costly registration is not needed thanks to the relative tolerance of the proposed representation and the SIFT methodology to moderate pose changes as the ones existing in FRGC v2.0. Finally, additional experiments demonstrate that the entire system is also robust to facial expression variations.

A commonly accepted fact in the biometrics related domain is that fusing multiple classifiers for decision making generally leads to improved recognition performance. Meanwhile, the search for an optimal fusion strategy remains extraordinarily complex since the cardinality of the space of possible fusion schemes is exponentially proportional to the number of competing classifiers. In this paper, we propose a mixture of gated experts for 3D face recognition using an ensemble of 24 different classifiers. The mixture of gated experts is optimized using a Simulated Annealing-based algorithm. It automatically selects and fuses the most relevant similarity measurements. The experimental results of 3D face recognition achieved on the FRGC v2.0 dataset illustrate the effectiveness and stability of the proposed method. Additionally, as a learning-based method, it also has a good robustness to the variations of training database.

Local binary pattern (LBP) is a nonparametric descriptor, which efficiently summarizes the local structures of images. In recent years, it has aroused increasing interest in many areas of image processing and computer vision and has shown its effectiveness in a number of applications, in particular for facial image analysis, including tasks as diverse as face detection, face recognition, facial expression analysis, and demographic classification. This paper presents a comprehensive survey of LBP methodology, including several more recent variations. As a typical application of the LBP approach, LBP-based facial image analysis is extensively reviewed, while its successful extensions, which deal with various tasks of facial image analysis, are also highlighted.

3D Face recognition has been considered as a major solution to deal with unsolved issues of reliable 2D face recognition in recent years, i.e. lighting and pose variations. However, 3D techniques are currently limited by their high registration and computation cost. In this paper, an asymmetric 3D-2D face recognition method is presented, enrolling in textured 3D whilst performing automatic identification using only 2D facial images. The goal is to limit the use of 3D data to where it really helps to improve face recognition accuracy. The proposed approach contains two separate matching steps: Sparse Representation Classifier (SRC) is applied to 2D-2D matching, while Canonical Correlation Analysis (CCA) is exploited to learn the mapping between range LBP faces (3D) and texture LBP faces (2D). Both matching scores are combined for the final decision. Moreover, we propose a new preprocessing pipeline to enhance robustness to lighting and pose effects. The proposed method achieves better experimental results in the FRGC v2.0 dataset than 2D methods do, but avoiding the cost and inconvenience of data acquisition and computation of 3D approaches.

This paper addresses the localization and super resolution of license plate in a unifying approach. Higher quality license plate can be obtained using super resolution on successive lower resolution plate images. All existing methods assume that plate zones are correctly extracted from every frame. However, the accurate localization needs a sufficient quality of the image, which is not always true in real video. Super-resolution on all pixels is a possible but much time consuming alternative. We propose a framework which interlaces successfully these two modules. First, coarse candidates are found by an weak but fast license plate detection based on edge map sub-sampling. Then, an improved fast MAP-based super-resolution, using local phase accurate registration and edge preserving prior, applied on these regions of interest. Finally, our robust ICHT-based localizer rejects false-alarms and localizes the high resolution license plate more accurately. Experiments which were conducted on synthetic and real data, proved the robustness of our approach with real-time possibility.

Classifier fusion is considered as one of the best strategies for improving performance of general purpose classification systems. On the other hand, fusion strategy space strongly depends on classifiers, features and data spaces. As the cardinality of this space is exponential, one needs to resort to a heuristic to find a sub-optimal fusion strategy. In this work, we present a new adaptive feature and score level fusion strategy (AFSFS) based on adaptive genetic algorithm. AFSFS tunes itself between feature and matching score level, and improves the final performance over the original on both levels, and as a fusion method, it does not only contain fusion strategy to combine the most relevant features so as to achieve adequate and optimized results, but also has the extensive ability to select the most discriminative features. Experiments are provided on the FRGC database showing that the proposed method produces significantly better results than the baseline fusion methods.

In this paper, we introduce a new approach for partial 3D face recognition, which makes use of shape decomposition over the rigid part of a face. To explore the descriptiveness of shape dissimilarity over an isometric part of a face, which has lower probability to be influenced by expression, we transform a 3D shape to a 2D domain using conformal mapping and use shape decomposition as a similarity measurement. In our work we investigate several classifiers as well as several shape descriptors for recognition purposes. Recognition tests on a subset of the FRGC data set show approximately 80% rank-one recognition rate using only the eyes and nose part of the face.

This paper presents a novel texture descriptor based on line-segment features for text detection in images and video sequences, which is applied to build a robust car license plate localization system. Unlike most of existing approaches which use low level features (color, edge) for text/non-text discrimination, our aim is to exploit more accurate perceptual information. A - scale and rotation invariant - texture descriptor which describes the directionality, regularity, similarity, alignment and connectivity of group of segments is proposed. A improved algorithm for feature extraction based on local connective Hough transform has been also investigated. The robustness of our approach is proved throughout a real-time detection/verification scheme of car license plate. First, all possible candidates are detected using a rule based method, which is very robust to illumination change and in varying poses. Then, true license plates are identified by the mean of a SVM classifier trained with proposed descriptor. Comparison and evaluation are conducted with two complex datasets.

In the recent years, 3D Face recognition has emerged as a major solution to deal with the unsolved issues for reliable 2D face recognition, i.e. lighting condition and viewpoint variations. However, 3D method is currently limited by its registration and computation cost. In this paper, we propose to investigate a solution named asymmetric face recognition scheme, enrolling people in 3D environment but performing identification in 2D. The goal is to limit the use of 3D data to where it really helps to improve recognition performances. In our approach, Local Binary Patterns (LBP) is used as an efficient facial representation for both 2D texture images and 3D range images. A weighted Chi square distance is used as matching score between the 2D LBP facial representations; Canonical Correlation Analysis (CCA) is applied to learn the mapping between LBP-based range face images (3D) and LBP facial texture images (2D). Both matching scores are further fused to obtain the final result. Compared with the traditional 2D/2D algorithms, the proposed asymmetric face recognition scheme achieves better accuracy; while avoiding the high cost of data acquisition and computation in 3D/3D approaches.

3D face has recently emerged as a major trend in face recognition. While 3D face is reputed to be relatively invariant to lighting conditions and pose, one still needs to cope with facial expression variations for a reliable face recognition solution. Many works in the literature try to define some invariants with respect to facial expressions. In this paper, we consider facial expressions as results of moving parts of a facial surface, and thus a careful study of anatomical face enables us to set up a region-based face surface matching, giving more importance to stable facial regions in the comparison. The very first experiments show the effectiveness of our approach.

In this paper we present a new texture feature extraction approach. Existing methods are generally time consuming and sensible to image complexity in terms of texture's regularity, directionality and coarseness. So that we propose a method which provide both rapidity and accuracy to extract and characterize texture features. It's based on Hough Transform technique combined with an extremity segment's neighbourhood analysis and a new computation algorithm to extract segments and detect regularity. Experimental results show that this approach is robust and can be applied not only to texture analysis but also to detect text within video images.