Noise Estimation of Polarization-Encoded Images by Peano-Hilbert Fractal Path

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A robust cost function for stereo matching of road scenes

International audienceIn this paper different matching cost functions used for stereo matching are evaluated in the context of intelligent vehicles applications. Classical costs are considered, like: sum of squared differences, normalized cross correlation or census transform that were already evaluated in previous studies, together with some recent functions that try to enhance the discriminative power of Census Transform (CT). These are evaluated with two different stereo matching algorithms: a global method based on graph cuts and a fast local one based on cross aggregation regions. Furthermore we propose a new cost function that combines the CT and alternatively a variant of CT called Cross-Comparison Census (CCC), with the mean sum of relative pixel intensity differences (DIFFCensus). Among all the tested cost functions, under the same constraints, the proposed DIFFCensus produces the lower error rate on the KITTI road scenes dataset 1 with both global and local stereo matching algorithms

PHROG: A Multimodal Feature for Place Recognition

International audienceLong-term place recognition in outdoor environments remains a challenge due to high appearance changes in the environment. The problem becomes even more difficult when the matching between two scenes has to be made with information coming from different visual sources, particularly with different spectral ranges. For instance, an infrared camera is helpful for night vision in combination with a visible camera. In this paper, we emphasize our work on testing usual feature point extractors under both constraints: repeatability across spectral ranges and long-term appearance. We develop a new feature extraction method dedicated to improve the repeatability across spectral ranges. We conduct an evaluation of feature robustness on long-term datasets coming from different imaging sources (optics, sensors size and spectral ranges) with a Bag-of-Words approach. The tests we perform demonstrate that our method brings a significant improvement on the image retrieval issue in a visual place recognition context, particularly when there is a need to associate images from various spectral ranges such as infrared and visible: we have evaluated our approach using visible, Near InfraRed (NIR), Short Wavelength InfraRed (SWIR) and Long Wavelength InfraRed (LWIR)

An evaluation of the pedestrian classification in a multi-domain multi-modality setup

The objective of this article is to study the problem of pedestrian classification across different light spectrum domains (visible and far-infrared (FIR)) and modalities (intensity, depth and motion). In recent years, there has been a number of approaches for classifying and detecting pedestrians in both FIR and visible images, but the methods are difficult to compare, because either the datasets are not publicly available or they do not offer a comparison between the two domains. Our two primary contributions are the following: (1) we propose a public dataset, named RIFIR , containing both FIR and visible images collected in an urban environment from a moving vehicle during daytime; and (2) we compare the state-of-the-art features in a multi-modality setup: intensity, depth and flow, in far-infrared over visible domains. The experiments show that features families, intensity self-similarity (ISS), local binary patterns (LBP), local gradient patterns (LGP) and histogram of oriented gradients (HOG), computed from FIR and visible domains are highly complementary, but their relative performance varies across different modalities. In our experiments, the FIR domain has proven superior to the visible one for the task of pedestrian classification, but the overall best results are obtained by a multi-domain multi-modality multi-feature fusion

A robust cost function for stereo matching of road scenes

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Analyse et traitement des images codées en polarisation

This work, which aims at providing tools for effective using of polarimetric imaging systems, includes the following objectives and developments: - algebric representation of polarization based on using quaternions: any non-depolarizing Mueller matrix is matched to a complex quaternion so that action of an optical system on a monochromatic wave writes as the geometric product of that quaternion by the multivector representing the Stokes vector. This way allows formal definition of concepts like distance, similarity and class membership. - optimisation of imaging polarimeters and quantification of measurement errors: systematic errors as well as image noise are considered jointly and minimisation is searched for from design of the system onwards. The method makes use of the Kronecker product. Figures of merit are introduced to define the characteristics of optimal polarimetric system. - estimate and filtering of noise on polarimetric images: two methods are proposed i) a new variant of the “scatter-plot” method combined with image vectorization through Piano-Hilbert route type, ii) data masking that relies on using the difference between two approximations of the Laplace operator. Performances and biases are estimated statistically based on the “Bootstrap” method. - classification and preview color encoding of polarimetric images: this color preview aims at facilitating interpretation of images according to their physical content. One method makes use of the polar decomposition (Mueller imagery), the other one uses mapping of the Poincaré sphere on parametric color spaces (Stokes imagery). Segmentation is based on K-means algorithms. Preview color encoding is illustrated with Mueller and Stokes images of biological tissues.Ce travail concerne les développements nécessaires à la mise en oeuvre efficace de systèmes imageurs polarimétriques et comporte plusieurs volets allant de la théorie amont à l'utilisation d'algorithmes de traitement d'images spécifiques. Les principaux objectifs et les développements réalisés se rapportent à: - la représentation algébrique des formalismes de polarisation en utilisant les quaternions : toute matrice de Mueller non dépolarisante peut être mise sous forme d'un quaternion complexe et l'action d'un système optique sur une onde monochromatique s'écrit comme le produit géométrique du quaternion correspondant à la matrice de Mueller par le multivecteur représentant le vecteur de Stokes. Cette utilisation de l'algèbre géométrique permet de définir formellement la notion de distance, de similitude et d'appartenance à une classe. - l'optimisation des polarimètres imageurs et le calcul de l'erreur sur les mesures : la démarche, basée sur l'utilisation du produit de Kronecker, prend en compte de façon conjointe les erreurs sytématiques et le bruit d'image et leur minimisation est obtenue dès l'étape de conception. Plusieurs fonctions de mérites sont introduites pour permettre la définition des caractéristiques du polarimètre optimal. - l'estimation et le filtrage du bruit des images polarimétriques : deux méthodes sont envisagées i) une nouvelle variante de la méthode du « scatter plot » combinée avec une vectorisation de l'image par un parcours de type Piano-Hilbert, ii) la méthode du masquage de données qui repose sur l'utilisation de la différence entre deux approximations de l'opérateur Laplacien. Les performances et les biais des deux estimateurs choisis sont étudiés statistiquement par la méthode du « Bootstrap ». - la classification et la prévisualisation couleur des images codées en polarisation : on propose une représentation colorée des images codées en polarisation, comme une aide à leur interprétation en fonction de leur contenu physique, qui utilise la décomposition polaire pour le cas de l'imagerie de Mueller et repose sur deux mappages entre la sphère de Poincaré et un espace de couleur paramétrique dans le cas de l'imagerie de Stokes. Le processus de segmentation est basé sur la famille des algorithmes des K-moyennes. Cette démarche est illustrée sur des images de Stokes et de Mueller de tissus biologiques

Multimodality semantic segmentation based on polarization and color images

International audienceSemantic segmentation gives a meaningful class label to every pixel in an image. It enables intelligent devices to understand the scene and has received sufficient attention during recent years. Traditional imaging systems always apply their methods on RGB, RGB-D or even RGB combined with geometric information. However, for outdoor applications, strong reflection or poor illumination appears to reduce the visualization of the real shape or texture of the objects, thus limiting the performance of semantic segmentation algorithms. To tackle this problem , this paper adopts polarization imaging as it can provide complementary information by describing some imperceptible light properties, which varies from different materials. For acceleration, SLIC superpixel segmen-tation is used to speed up the system. HOG and LBP features are extracted from both color and polarization images. After quantization using visual codebooks, Joint Boosting classifier is trained to label each pixel based on the quantized features. The proposed method was evaluated both on Day-set and Dusk-set. The experimental results show that using polarization setup can provide complementary information to improve the semantic segmentation accuracy. Especially, a large improvement on Dusk-set shows its capacity for intelligent vehicle applications under dark illumination condition

A mixed-reality framework based on depth camera for safety testing of autonomous navigation systems*

International audienceSimulation testing is an essential stage to preparing vehicles for a variety of possible and dangerous situations in order to validate autonomous driving algorithms on mobile systems. However, transferring the model issued from the algorithms to reality can be challenging. Mixedreality environments facilitate testing models on real vehicles with reduced financial and safety risks. By introducing virtual elements to the agent's environment perception, mixed-reality frameworks can reduce the risks and costs associated with testing on real roads while enabling researchers to explore a greater number of potentially critical situations. This paper presents a mixed reality framework based on depth cameras. The framework uses an augmentation approach to combine objects from two environments (virtual and real) in a single world. The implementation details of the proposed method are discussed, and the qualitative and quantitative analysis of the experimental results demonstrate the potential of the proposed framework

Segmentation-Based vs. Regression-Based Biomarker Estimation : A Case Study of Fetus Head Circumference Assessment from Ultrasound Images

International audienceThe fetus head circumference (HC) is a key biometric to monitor fetus growth during pregnancy, which is estimated from ultrasound (US) images. The standard approach to automatically measure the HC is to use a segmentation network to segment the skull, and then estimate the head contour length from the segmentation map via ellipse fitting, usually after post-processing. In this application, segmentation is just an intermediate step to the estimation of a parameter of interest. Another possibility is to estimate directly the HC with a regression network. Even if this type of segmentation-free approaches have been boosted with deep learning, it is not yet clear how well direct approach can compare to segmentation approaches, which are expected to be still more accurate. This observation motivates the present study, where we propose a fair, quantitative comparison of segmentation-based and segmentation-free (i.e., regression) approaches to estimate how far regression-based approaches stand from segmentation approaches. We experiment various convolutional neural networks (CNN) architectures and backbones for both segmentation and regression models and provide estimation results on the HC18 dataset, as well agreement analysis, to support our findings. We also investigate memory usage and computational efficiency to compare both types of approaches. The experimental results demonstrate that even if segmentation-based approaches deliver the most accurate results, regression CNN approaches are actually learning to find prominent features, leading to promising yet improvable HC estimation results