Search results for "Pattern"
showing 10 items of 4203 documents
An original approach for gas chromatography-olfactometry detection frequency analysis: Application to gin
2012
Abstract Gas Chromatography-Olfactometry (GC-O) is a technique that lies halfway between physicochemical and sensory analysis and involves the perception of volatile flavour compounds, separated by gas chromatography, by the human nose. Of the different GC-O procedures available, detection frequency has been proved to be more rapid and more repeatable, even with an untrained panel. This characteristic regarding the panel is often not considered when dealing with the sensory attributes determined by assessors. An original approach to GC-O using the detection frequency procedure has been developed and tested on two types of gin and made it possible to benefit from sensory data. The panel cons…
Digital Correlation Method Based on Microgeometrical Texture Patterns for Strain Fields Measurements
2005
International audience; Digital correlation method is widely used in experimental mechanics to obtain the deformation field. Currently this method is applied with digital images of the initial and deformed surface sprayed with black or white painting. Speckle patterns are then captured and the correlation can be made with high 0.01 pixel accuracy in 2D-cases. In three-dimensions, the stereo-correlation can be used with a lower accuracy. The work presented in this paper, is a first approach based on the use of a 3D laser scanner in the objective of three-dimensional strain field measurement. The digital speckle patterns are not given by gray level but from the micro-geometrical surface textu…
Le grand débat national, une aide pour prendre des décisions locales?
2021
The Great National Debate, decided by Emmanuel Macron at the beginning of 2019 to respond to the Yellow Vests social movement, allowed the collection of citizens’ contributions on the ecological transition via an online platform. In this article, we use the corpus constituted by these contributions to identify areas where participants are asking for the development of bicycle paths and railway facilities. For this purpose, we have created a classification model to identify contributions dealing with the theme of transportation and proposed a method for extracting patterns that reflect the contributors’ proposals. We then represented these patterns on maps, using the contributors’ postal cod…
Incorporating depth information into few-shot semantic segmentation
2021
International audience; Few-shot segmentation presents a significant challengefor semantic scene understanding under limited supervision.Namely, this task targets at generalizing the segmentationability of the model to new categories given a few samples.In order to obtain complete scene information, we extend theRGB-centric methods to take advantage of complementary depthinformation. In this paper, we propose a two-stream deep neuralnetwork based on metric learning. Our method, known as RDNet,learns class-specific prototype representations within RGB anddepth embedding spaces, respectively. The learned prototypesprovide effective semantic guidance on the corresponding RGBand depth query ima…
hidden markov random fields and cuckoo search method for medical image segmentation
2020
Segmentation of medical images is an essential part in the process of diagnostics. Physicians require an automatic, robust and valid results. Hidden Markov Random Fields (HMRF) provide powerful model. This latter models the segmentation problem as the minimization of an energy function. Cuckoo search (CS) algorithm is one of the recent nature-inspired meta-heuristic algorithms. It has shown its efficiency in many engineering optimization problems. In this paper, we use three cuckoo search algorithm to achieve medical image segmentation.
Unsupervised learning of category-specific symmetric 3D keypoints from point sets
2020
Lecture Notes in Computer Science, 12370
3D landmark detection for augmented reality based otologic procedures
2019
International audience; Ear consists of the smallest bones in the human body and does not contain significant amount of distinct landmark points that may be used to register a preoperative CT-scan with the surgical video in an augmented reality framework. Learning based algorithms may be used to help the surgeons to identify landmark points. This paper presents a convolutional neural network approach to landmark detection in preoperative ear CT images and then discusses an augmented reality system that can be used to visualize the cochlear axis on an otologic surgical video.
Event-Based Trajectory Prediction Using Spiking Neural Networks
2021
International audience; In recent years, event-based sensors have been combined with spiking neural networks (SNNs) to create a new generation of bio-inspired artificial vision systems. These systems can process spatio-temporal data in real time, and are highly energy efficient. In this study, we used a new hybrid event-based camera in conjunction with a multi-layer spiking neural network trained with a spike-timing-dependent plasticity learning rule. We showed that neurons learn from repeated and correlated spatio-temporal patterns in an unsupervised way and become selective to motion features, such as direction and speed. This motion selectivity can then be used to predict ball trajectory…
Enhancement and assessment of WKS variance parameter for intelligent 3D shape recognition and matching based on MPSO
2016
This paper presents an improved wave kernel signature (WKS) using the modified particle swarm optimization (MPSO)-based intelligent recognition and matching on 3D shapes. We select the first feature vector from WKS, which represents the 3D shape over the first energy scale. The choice of this vector is to reinforce robustness against non-rigid 3D shapes. Furthermore, an optimized WKS-based method for extracting key-points from objects is introduced. Due to its discriminative power, the associated optimized WKS values with each point remain extremely stable, which allows for efficient salient features extraction. To assert our method regarding its robustness against topological deformations,…
Repérage précis de caméras multispectrales et de scanners 3D pour le recalage de données multicapteurs appliqué à l'étude du patrimoine
2012
Session "Atelier V3DPAT"; National audience; Nos travaux portent sur le recalage de données multi-capteurs et spécifiquement sur la projection de textures 2D sur des modèles 3D d'objet du patrimoine en pierre. Nous nous intéressons particulièrement aux textures acquises par imagerie multispectrale mais notre technique est également adaptée à d'autres systèmes optiques d'acquisition tels que l'imagerie thermique. Les modèles 3D, eux, sont acquis par un système de projection de franges. La difficulté du recalage multicapteur vient principalement de la variation de la représentation de l'objet. Ainsi, les points saillants d'un jeu de données ne correspondent pas forcément à ceux d'une autre re…