Guiding Active Contours for Tree Leaf Segmentation and Identification

International audienceIn the process of tree identi cation from pictures of leaves in a natural background, retrieving an accurate contour is a challenging and crucial issue. In this paper we introduce a method designed to deal with the obstacles raised by such complex images, for simple and lobed tree leaves. A rst segmentation step based on a light polygonal leaf model is first performed, and later used to guide the evolution of an active contour. Combining global shape descriptors given by the polygonal model with local curvature-based features, the leaves are then classi ed over nearly 50 tree species

A Model-Based Approach for Compound Leaves Understanding and Identification

International audienceIn this paper, we propose a specific method for the identification of compound-leaved tree species, with the aim of integrating it in an educational smartphone application. Our work is based on dedicated shape models for compound leaves, designed to estimate the number and shape of leaflets. A deformable template approach is used to fit these models and produce a high-level interpretation of the image content. The resulting models are later used for the segmentation of leaves in both plain and natural background images, by the use of multiple region-based active contours. Combined with other botany-inspired descriptors accounting for the morphological properties of the leaves, we propose a classification method that makes a semantic interpretation possible. Results are presented over a set of more than 1000 images from 17 European tree species, and an integration in the existing mobile application Folia is considered

Understanding Leaves in Natural Images - A Model-Based Approach for Tree Species Identification

International audienceWith the aim of elaborating a mobile application, accessible to anyone and with educational purposes, we present a method for tree species identification that relies on dedicated algorithms and explicit botany-inspired descriptors. Focusing on the analysis of leaves, we developed a working process to help recognize species, starting from a picture of a leaf in a complex natural background. A two-step active contour segmentation algorithm based on a polygonal leaf model processes the image to retrieve the contour of the leaf. Features we use afterwards are high-level geometrical descriptors that make a semantic interpretation possible, and prove to achieve better performance than more generic and statistical shape descriptors alone. We present the results, both in terms of segmentation and classification, considering a database of 50 European broad-leaved tree species, and an implementation of the system is available in the iPhone application Folia

Distributed under Creative Commons Attribution License Contents 1 Intensity enhancement 2

We present a semi-automatic algorithm for Carotid lumen segmentation on CTA images. Our method is based on a variant of the minimal path method that models the vessel as a centerline and boundary. This is done by adding one dimension for the local radius around the centerline. The crucial step of our method is the definition of the local metrics to minimize. We have chosen to exploit the tubular structure of the vessels one wants to extract to built an anisotropic metric giving higher speed on the center of the vessels and also when the minimal path tangent is coherent with the vessels direction. Due to carotid stenosis or occlusions on the provided data, segmentation is refined using a region-based level sets

Human Activity Recognition with Pose-driven Attention to RGB

International audienceWe address human action recognition from multi-modal video data involving articulated pose and RGB frames and propose a two-stream approach. The pose stream is processed with a convolutional model taking as input a 3D tensor holding data from a sub-sequence. A specific joint ordering, which respects the topology of the human body, ensures that different convolutional layers correspond to meaningful levels of abstraction. The raw RGB stream is handled by a spatio-temporal soft-attention mechanism conditioned on features from the pose network. An LSTM network receives input from a set of image locations at each instant. A trainable glimpse sensor extracts features on a set of pre-defined locations specified by the pose stream, namely the 4 hands of the two people involved in the activity. Appearance features give important cues on hand motion and on objects held in each hand. We show that it is of high interest to shift the attention to different hands at different time steps depending on the activity itself. Finally a temporal attention mechanism learns how to fuse LSTM features over time. State-of-the-art results are achieved on the largest dataset for human activity recognition, namely NTU-RGB+D

Segmentation et suivi de l'endocarde dans des séquences IRM 3D par surface active

Nous proposons un modèle de surface active 3D+T pour la segmentation et le suivi de la paroi endocardique du ventricule gauche dans des séquences cardiaques 3D. Pour réunir les étapes de segmentation et de suivi, la surface est dotée d'une structure divisée à la fois dans l'espace et le temps. Elle se modélise sous la forme d'une matrice de contours actifs planaires, connectés d'une coupe et d'une phase à l'autre, ce qui permet de maintenir une cohérence temporelle et spatiale. Dans une phase donnée, l'empilement des contours constitue un maillage triangulaire 3D de topologie cylindrique. La paroi ventriculaire est extraite par minimisation d'une énergie qui combine un terme contour et un nouveau terme basé région, exprimé selon le principe de la bande étroite

A setup to measure the temperature-dependent heating power of magnetically heated nanoparticles up to high temperature.

Magnetic heating, namely, the use of heat released by magnetic nanoparticles (MNPs) excited with a high-frequency magnetic field, has so far been mainly used for biological applications. More recently, it has been shown that this heat can be used to catalyze chemical reactions, some of them occurring at temperatures up to 700 °C. The full exploitation of MNP heating properties requires the knowledge of the temperature dependence of their heating power up to high temperatures. Here, a setup to perform such measurements is described based on the use of a pyrometer for high-temperature measurements and on a protocol based on the acquisition of cooling curves, which allows us to take into account calorimeter losses. We demonstrate that the setup permits to perform measurements under a controlled atmosphere on solid state samples up to 550 °C. It should in principle be able to perform measurements up to 900 °C. The method, uncertainties, and possible artifacts are described and analyzed in detail. The influence on losses of putting under vacuum different parts of the calorimeter is measured. To illustrate the setup possibilities, the temperature dependence of heating power is measured on four samples displaying very different behaviors. Their heating power increases or decreases with temperature, displaying temperature sensibilities ranging from -2.5 to +4.4% K-1. This setup is useful to characterize the MNPs for magnetically heated catalysis applications and to produce data that will be used to test models permitting to predict the temperature dependence of MNP heating power

TAAABLE: Text Mining, Ontology Engineering, and Hierarchical Classification for Textual Case-Based Cooking

International audienceThis paper presents how the Taaable project addresses the textual case-based reasoning challenge of the CCC, thanks to a combination of principles, methods, and technologies of various fields of knowledge-based system technologies, namely CBR, ontology engineering manual and semi-automatic), data and text-mining using textual resources of the Web, text annotation (used as an indexing technique), knowledge representation, and hierarchical classification. Indeed, to be able to reason on textual cases, indexing them by a formal representation language using a formal vocabulary has proven to be useful

X-ray emission from a rapidly accreting narrow-line Seyfert 1 galaxy at z=6.56

This study aims at identifying luminous quasars at $z>5.7$ among X-ray-selected sources in the eROSITA Final Equatorial-Depth Survey (eFEDS) in order to place a lower limit on black hole accretion well into the epoch of re-ionisation. We confirm the low significance detection with eROSITA of a previously known, optically faint $z=6.56$ quasar from the Subaru High-z Exploration of Low-luminosity Quasars (SHELLQs) survey. We obtained a pointed follow-up observation of the source with the Chandra X-ray telescope in order to confirm the eROSITA detection. Using new near-infrared spectroscopy, we derived the physical properties of the super-massive black hole. Finally, we used this detection to infer a lower limit on the black hole accretion density rate at $z>6$. The Chandra observation confirms the eFEDS source as the most distant blind X-ray detection to date. The derived X-ray luminosity is high with respect to the rest-frame optical emission of the quasar. With a narrow MgII line, low derived black hole mass, and high Eddington ratio, as well as its steep photon index, the source shows properties that are similar to local narrow-line Seyfert 1 galaxies, which are thought to be powered by young super-massive black holes. In combination with a previous high-redshift quasar detection in the field, we show that quasars with $L_{2-10 \, \mathrm{keV}} >10^{45} \, \mathrm{erg \, s^{-1}}$ dominate accretion onto super-massive black holes at $z\sim 6$.Comment: Accepted for publication in A&