Recalage hétérogène de nuages de points 3D : Application à l'imagerie sous-marine

National audienceThe registration of two 3D point clouds is an essential step in many applications. The objective of our work is to estimate the isometric transformation to merge two heterogeneous point clouds obtained from two different sensors. In this paper, we present a new approach for 3D - 3D registration which is distinguished by the nature of the extracted signature on each point and by the similarity criterion used to measure the degree of similarity. The descriptor that we propose is invariant to the rotation and also to the translation and overcomes the problem of multi - resolution that is related to heterogeneous data. At the end, our approach has been tested on synthetic data and applied on heterogeneou s real data.Le recalage de deux nuages de points 3D est une étape essentielle dans de nombreuses applications. L’objectif de notre travail est d’estimer une transformation isométrique permettant de fusionner au mieux deux ensembles hétérogènes de points issus de deux capteurs différents. Dans cet article, nous présenterons une méthode de recalage 3D - 3D originale qui se distingue par la nature de la signature extraite en chaque point et par le critère de similarité utilisé pour mesurer le degré de ressemblance. Le descripteur que nous pr oposons est invariant à la rotation et à la translation et permet également de s’affranchir du problème de la multi - résolution relatif aux données hétérogènes. Dans le but de valider notre approche, nous l’avons testé sur des données synthétiques et nous l’avons appliqué sur des données réelles hétérogènes

People’s Re-identification Across Multiple Non-overlapping Cameras by Local Discriminative Patch Matching

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People Counting based on Kinect Depth Data

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3D-AD: 3D-autism dataset for repetitive behaviours with kinect sensor

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Enhancing Anomaly Detection in Melanoma Diagnosis Through Self-Supervised Training and Lesion Comparison

International audienceMelanoma, a highly aggressive form of skin cancer notorious for its rapid metastasis, necessitates early detection to mitigate complex treatment requirements. While considerable research has addressed melanoma diagnosis using convolutional neural networks (CNNs) on individual dermatological images, a deeper exploration of lesion comparison within a patient is warranted for enhanced anomaly detection, which often signifies malignancy. In this study, we present a novel approach founded on an automated, self-supervised framework for comparing skin lesions, working entirely without access to ground truth labels. Our methodology involves encoding lesion images into feature vectors using a state-of-the-art representation learner, and subsequently leveraging an anomaly detection algorithm to identify atypical lesions. Remarkably, our model achieves robust anomaly detection performance on ISIC 2020 without needing annotations, highlighting the efficacy of the representation learner in discerning salient image features. These findings pave the way for future research endeavors aimed at developing better predictive models as well as interpretable tools that enhance dermatologists' efficacy in scrutinizing skin lesions

Adaptive vision system for high velocity tooling machines

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People tracking in multi-camera systems: a review

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A structural representation of the customer's behavior for the characterization of the indecisiveness class

National audienceno abstrac

Application of Machine Learning to Signal Detection in Underwater Wireless Optical Communication Links

International audienceWe consider the application of a machine-learning (ML)-based method to the demodulation of the received signal in underwater wireless optical communication (UWOC) links. This approach is justified when the underwater optical channel is subject to strong variations due to various phenomena such as pointing errors and turbulences, which directly impact the received optical power, requiring accurate and agile channel estimation. The investigated ML method is based on the wellknown K-nearest neighbors (KNN). We demonstrate excellent link performance for different types of modulation schemes even under high data rates and low received optical powers, for instance, achieving effective bit rates of 2.96 and 2.54 Gbps using 16QAM and 32-QAM modulation schemes, respectively, at a received optical power of −16.4 dBm. We also discuss the implementation aspects of the proposed approach, including its computational complexity

Automatic detection of stereotyped movements in autistic children using the Kinect sensor

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