Espaces hybrides couleur : texture adaptés au comptage d'épis de blé

Une méthode d'analyse d'image texture - couleur basée sur la détermination d'un espace hybride couleur a été développée dans le cadre d'une étude de faisabilité d'un système de comptage (semi)-automatique des épis de blé dans un champ, pour une prédiction précoce de rendement. A cette fin, des analyses couleur et de texture (paramètres d'Haralick) sont utilisées pour permettre une nouvelle représentation des images prises dans un espace spécifique (espace hybride) construit à partir de connaissances a priori sur les images. Les méthodes classiques de classification et de segmentation des images, combinées à des informations morphologiques sur les épis, sont appliquées pour le comptage. Le taux de reconnaissance des épis est compris entre 73% et 85% sur les quelques images prises pendant le stade de floraison. Couplées avec le temps, la luminosité et en accord avec les modèles de développement du blé, les informations générées à partir de l'algorithme implémentés dans cette étude semblent significatives pour pouvoir évaluer le troisième apport d'engrais azotés qui se déroule au début de la floraison

Feasability study of a wheatears counting system per vision

Working on a feasibility study of wheatears counting, a colour component texture's analysis method was developed. The agronomic goal is yield prediction before harvest evaluating mean number of wheatears per squared meter according to the field variation knowledge. To this counting system, we evaluate six textural parameters (two statistical parameters and four Haralick features from co-occurrence matrix) on the main colour systems and vegetation indices used in agronomic applications. A new hybrid system provides a representation of wheatears' pictures taken under natural conditions with a better extraction of wheat. A method based on distances measurements (Euclidian, Mahalanobis) allows to extract wheatears with few errors corrected by mathematical morphology. Although we encounter difficulties from light intensity's variation and high entropy in the scene (ears' covering and shadows), results allow to extract disturbed wheatears and last recent images give an higher accuracy in segmentation.Dans le cadre d'une étude de faisabilité du dénombrement d'épis de blé par imagerie couleur, une méthode d'analyse de textures sur des composantes d'espaces couleurs a été développée. L'objectif agronomique est la prévision de rendement avant la moisson par évaluation du nombre moyen d'épis par unité de surface en tenant compte de la variabilité intra-parcellaire. Pour ce dénombrement par image, nous étudions six paramètres de texture (deux valeurs statistiques et quatre coefficients d'Haralick issus de la matrice de cooccurrence) que nous évaluons sur les composantes d'espaces couleurs utilisées en agronomie. Un nouvel espace hybride permet de créer une représentation d'images de blé prises en milieu naturel dans lesquelles l'extraction d'épis sera améliorée. La méthode basée sur des mesures de distances (Euclidienne, de Mahalanobis) permet d'extraire les épis avec quelques erreurs corrigées par de la morphologie mathématique. Malgré les difficultés dues à la variation de luminosité et à l'entropie élevée des scènes, les résultats permettent de trouver en partie les épis, et les dernières images en court de traitement permettent une meilleure segmentation

3D printing and morphological characterisation of polymeric composite scaffolds

© 2020 Elsevier Ltd. All rights reserved. This manuscript is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence http://creativecommons.org/licenses/by-nc-nd/4.0/.3D-printing is an efficient method of designing customised structures and producing synthetic bone grafts appropriate for bone implants. This research aimed to manufacture a new multi-functionalised 3D-printed poly(lactic acid)/carbonated hydroxyapatite (PLA/cHA) scaffolds with mass proportions of 100/0, 95/5 and 90/10 in a bid to verify their potential application in tissue regeneration. The filaments of these hybrid materials were obtained by extrusion technique and subsequently used to manufacture the 3D-printed scaffolds, using a fused deposition modelling (FDM) technique. The scaffolds were characterised based on their thermal properties, microstructure and geometry by differential scanning calorimetry (DSC), scanning electron microscopy (SEM) and energy dispersive x-ray spectroscopy (EDS), respectively, in addition to determination of their apparent porosities. The degradation of the scaffolds and the liberation of degradation products were evaluated in in vitro for different days under simulated physiological conditions. New microanalyses of mechanical behaviour of the materials: tensile and compression stresses, density, frequency analysis and optimisation with DSC were performed. While, evaluation of the surface luminance structure and the profile structure of the nanostructured PLA composite materials was done by SEM, in 3D printed form. The filter profile of cross-sectional view of the specimen was extracted and evaluated with Firestone curve of the Gaussian filter; checking the roughness and waviness profile of the structure. It was observed that the thermal properties of the composites were not affected by the manufacturing process. The microstructural analysis showed the effective incorporation of the ceramic filler in the polymer matrix as well as an acceptable PLA/cHA interaction. The degradation tests showed the presence of calcium and phosphorus in the studied medium, confirming their liberation from the composites during the incubation periods.Peer reviewedFinal Accepted Versio

Judgements of style: People, pigeons, and Picasso

Judgements of and sensitivity to style are ubiquitous. People become sensitive to the structural regularities of complex or “polymorphous” categories through exposure to individual examples, which allows them respond to new items that are of the same style as those previously experienced. This thesis investigates whether a dimension reduction mechanism could account for how people learn about the structure of complex categories. That is, whether through experience, people extract the primary dimensions of variation in a category and use these to analyse and categorise subsequent instances. We used Singular Value Decomposition (SVD) as the method of dimension reduction, which yields the main dimensions of variation of pixel-based stimuli (eigenvectors). We then tested whether a simple autoassociative network could learn to distinguish paintings by Picasso and Braque which were reconstructed from only these primary dimensions of variation. The network could correctly classify the stimuli, and its performance was optimal with reconstructions based on just the first few eigenvectors. Then we reconstructed the paintings using either just the first 10 (early reconstructions) or all 1,894 eigenvectors (full reconstructions), and asked human participants to categorise the images. We found that people could categorise the images with either the early or full reconstructions. Therefore, people could learn to distinguish category membership based on the reduced set of dimensions obtained from SVD. This suggests that a dimension reduction mechanism analogous to SVD may be operating when people learn about the structure and regularities in complex categories

Vision-based techniques for automatic marine plankton classification

Plankton are an important component of life on Earth. Since the 19th century, scientists have attempted to quantify species distributions using many techniques, such as direct counting, sizing, and classification with microscopes. Since then, extraordinary work has been performed regarding the development of plankton imaging systems, producing a massive backlog of images that await classification. Automatic image processing and classification approaches are opening new avenues for avoiding time-consuming manual procedures. While some algorithms have been adapted from many other applications for use with plankton, other exciting techniques have been developed exclusively for this issue. Achieving higher accuracy than that of human taxonomists is not yet possible, but an expeditious analysis is essential for discovering the world beyond plankton. Recent studies have shown the imminent development of real-time, in situ plankton image classification systems, which have only been slowed down by the complex implementations of algorithms on low-power processing hardware. This article compiles the techniques that have been proposed for classifying marine plankton, focusing on automatic methods that utilize image processing, from the beginnings of this field to the present day.Funding for open access charge: Universidad de Málaga / CBUA. Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. The authors wish to thank Alonso Hernández-Guerra for his frm support in the development of oceanographic technology. Special thanks to Laia Armengol for her help in the domain of plankton. This study has been funded by Feder of the UE through the RES-COAST Mac-Interreg pro ject (MAC2/3.5b/314). We also acknowledge the European Union projects SUMMER (Grant Agreement 817806) and TRIATLAS (Grant Agreement 817578) from the Horizon 2020 Research and Innovation Programme and the Ministry of Science from the Spanish Government through the Project DESAFÍO (PID2020-118118RB-I00)

A robust framework for medical image segmentation through adaptable class-specific representation

Medical image segmentation is an increasingly important component in virtual pathology, diagnostic imaging and computer-assisted surgery. Better hardware for image acquisition and a variety of advanced visualisation methods have paved the way for the development of computer based tools for medical image analysis and interpretation. The routine use of medical imaging scans of multiple modalities has been growing over the last decades and data sets such as the Visible Human Project have introduced a new modality in the form of colour cryo section data. These developments have given rise to an increasing need for better automatic and semiautomatic segmentation methods. The work presented in this thesis concerns the development of a new framework for robust semi-automatic segmentation of medical imaging data of multiple modalities. Following the specification of a set of conceptual and technical requirements, the framework known as ACSR (Adaptable Class-Specific Representation) is developed in the first case for 2D colour cryo section segmentation. This is achieved through the development of a novel algorithm for adaptable class-specific sampling of point neighbourhoods, known as the PGA (Path Growing Algorithm), combined with Learning Vector Quantization. The framework is extended to accommodate 3D volume segmentation of cryo section data and subsequently segmentation of single and multi-channel greyscale MRl data. For the latter the issues of inhomogeneity and noise are specifically addressed. Evaluation is based on comparison with previously published results on standard simulated and real data sets, using visual presentation, ground truth comparison and human observer experiments. ACSR provides the user with a simple and intuitive visual initialisation process followed by a fully automatic segmentation. Results on both cryo section and MRI data compare favourably to existing methods, demonstrating robustness both to common artefacts and multiple user initialisations. Further developments into specific clinical applications are discussed in the future work section

Pattern Recognition

Pattern recognition is a very wide research field. It involves factors as diverse as sensors, feature extraction, pattern classification, decision fusion, applications and others. The signals processed are commonly one, two or three dimensional, the processing is done in real- time or takes hours and days, some systems look for one narrow object class, others search huge databases for entries with at least a small amount of similarity. No single person can claim expertise across the whole field, which develops rapidly, updates its paradigms and comprehends several philosophical approaches. This book reflects this diversity by presenting a selection of recent developments within the area of pattern recognition and related fields. It covers theoretical advances in classification and feature extraction as well as application-oriented works. Authors of these 25 works present and advocate recent achievements of their research related to the field of pattern recognition