3D Convolutional Neural Networks Initialized from Pretrained 2D Convolutional Neural Networks for Classification of Industrial Parts

Deep learning methods have been successfully applied to image processing, mainly using 2D vision sensors. Recently, the rise of depth cameras and other similar 3D sensors has opened the field for new perception techniques. Nevertheless, 3D convolutional neural networks perform slightly worse than other 3D deep learning methods, and even worse than their 2D version. In this paper, we propose to improve 3D deep learning results by transferring the pretrained weights learned in 2D networks to their corresponding 3D version. Using an industrial object recognition context, we have analyzed different combinations of 3D convolutional networks (VGG16, ResNet, Inception ResNet, and EfficientNet), comparing the recognition accuracy. The highest accuracy is obtained with EfficientNetB0 using extrusion with an accuracy of 0.9217, which gives comparable results to state-of-the art methods. We also observed that the transfer approach enabled to improve the accuracy of the Inception ResNet 3D version up to 18% with respect to the score of the 3D approach alone.This paper has been supported by the project ELKARBOT under the Basque program ELKARTEK, grant agreement No. KK-2020/00092

Histogram-Based Descriptor Subset Selection for Visual Recognition of Industrial Parts

This article deals with the 2D image-based recognition of industrial parts. Methods based on histograms are well known and widely used, but it is hard to find the best combination of histograms, most distinctive for instance, for each situation and without a high user expertise. We proposed a descriptor subset selection technique that automatically selects the most appropriate descriptor combination, and that outperforms approach involving single descriptors. We have considered both backward and forward mechanisms. Furthermore, to recognize the industrial parts a supervised classification is used with the global descriptors as predictors. Several class approaches are compared. Given our application, the best results are obtained with the Support Vector Machine with a combination of descriptors increasing the F1 by 0.031 with respect to the best descriptor alone.This paper has been supported by the project SHERLOCK under the European Union’s Horizon 2020 Research & Innovation programme, grant agreement No. 820689

2D Image Features Detector And Descriptor Selection Expert System

Detection and description of keypoints from an image is a well-studied problem in Computer Vision. Some methods like SIFT, SURF or ORB are computationally really efficient. This paper proposes a solution for a particular case study on object recognition of industrial parts based on hierarchical classification. Reducing the number of instances leads to better performance, indeed, that is what the use of the hierarchical classification is looking for. We demonstrate that this method performs better than using just one method like ORB, SIFT or FREAK, despite being fairly slower.Comment: 10 pages, 5 figures, 5 table

Qualitative visual servoing for navigation

We propose in this article a novel approach for vision-based control of a robotic system during a navigation task. This technique is based on a topological representation of the environment in which the scene is directly described within the sensor space, by an image database acquired off-line. Before each navigation task, a preliminary step consists in localizing the current position of the robotic system. This is realized through an image retrieval scheme, by searching within the database the views that are the most similar to the one given by the camera. Then a classical shortest path finding algorithm enables to extract from the database a sequence of views that visually describe the environment the robot has to go through in order to reach the desired position. This article mainly focuses on the control law that is used for controlling the motions of the robotic system, by comparing the visual information extracted from the current view and from the image path. This control law does not need a CAD model of the environment, and does not perform a temporal path planning. Furthermore, the images from the path are not considered as successive desired positions that have to be consecutively reached by the camera. The qualitative visual servoing scheme proposed, based on cost functions, ensures that the robotic system is always able to observe some visual features initially detected on the image path. Experiments realized in simulation and with a real system demonstrate that this formalism enables to control a camera moving in a 3D environment.Dans cet article, une nouvelle méthode est proposée pour contrôler les mouvements d'un système robotique à l'aide d'un capteur de vision monoculaire durant une tâche de navigation. Cette approche s'appuie sur une représentation topologique de l'environnement, où la scène est directement décrite dans l'espace du capteur par une base d'images acquises hors-ligne. Lors de la navigation, une étape préalable de recherche d'images permet de localiser la position courante du robot, en mettant en relation la vue que sa caméra fournit avec celles stockées dans la base. Un algorithme classique de recherche de plus-court chemin permet alors d'extraire de la base un ensemble de vues caractérisant l'espace à parcourir afin de rejoindre la position désirée. Cet article se concentre principalement sur la loi de commande permettant de déduire les mouvements du robot en fonction des informations extraites de ce chemin et de la vue courante de la caméra. Notre méthode ne s'appuie pas sur un modèle 3D de la scène, et n'effectue pas une planification temporelle de la trajectoire à réaliser. De plus, les images du chemin ne sont pas considérées comme des positions désirées intermédiaires vers lesquelles doit converger la caméra. Le schéma d'asservissement visuel proposé, qualifié de qualitatif, repose sur des fonctions de coût, et assure que le robot peut toujours observer les amers visuels initialement détectés sur le chemin d'images. Des expériences réalisées en simulation et avec un système réel montrent que le formalisme proposé permet de contrôler les mouvements d'une caméra dans un environnement 3D

Ensemble of 6 DoF Pose estimation from state-of-the-art deep methods.

Deep learning methods have revolutionized computer vision since the appearance of AlexNet in 2012. Nevertheless, 6 degrees of freedom pose estimation is still a difficult task to perform precisely. Therefore, we propose 2 ensemble techniques to refine poses from different deep learning 6DoF pose estimation models. The first technique, merge ensemble, combines the outputs of the base models geometrically. In the second, stacked generalization, a machine learning model is trained using the outputs of the base models and outputs the refined pose. The merge method improves the performance of the base models on LMO and YCB-V datasets and performs better on the pose estimation task than the stacking strategy.This paper has been supported by the project PROFLOW under the Basque program ELKARTEK, grant agreement No. KK-2022/00024

Regulating Grip Forces through EMG-Controlled Protheses for Transradial Amputees

This study aims to evaluate different combinations of features and algorithms to be used in the control of a prosthetic hand wherein both the configuration of the fingers and the gripping forces can be controlled. This requires identifying machine learning algorithms and feature sets to detect both intended force variation and hand gestures in EMG signals recorded from upper-limb amputees. However, despite the decades of research into pattern recognition techniques, each new problem requires researchers to find a suitable classification algorithm, as there is no such thing as a universal ’best’ solution. Consideration of different techniques and data representation represents a fundamental practice in order to achieve maximally effective results. To this end, we employ a publicly-available database recorded from amputees to evaluate different combinations of features and classifiers. Analysis of data from 9 different individuals shows that both for classic features and for time-dependent power spectrum descriptors (TD-PSD) the proposed logarithmically scaled version of the current window plus previous window achieves the highest classification accuracy. Using linear discriminant analysis (LDA) as a classifier and applying a majority-voting strategy to stabilize the individual window classification, we obtain 88% accuracy with classic features and 89% with TD-PSD features.This paper is supported by European Union’s Horizon 2020 research and innovation programme under the Grant Agreement no 779967, project RobotUnion

Making Bipedal Robot Experiments Reproducible and Comparable: The Eurobench Software Approach

This study describes the software methodology designed for systematic benchmarking of bipedal systems through the computation of performance indicators from data collected during an experimentation stage. Under the umbrella of the European project Eurobench, we collected approximately 30 protocols with related testbeds and scoring algorithms, aiming at characterizing the performances of humanoids, exoskeletons, and/or prosthesis under different conditions. The main challenge addressed in this study concerns the standardization of the scoring process to permit a systematic benchmark of the experiments. The complexity of this process is mainly due to the lack of consistency in how to store and organize experimental data, how to define the input and output of benchmarking algorithms, and how to implement these algorithms. We propose a simple but efficient methodology for preparing scoring algorithms, to ensure reproducibility and replicability of results. This methodology mainly constrains the interface of the software and enables the engineer to develop his/her metric in his/her favorite language. Continuous integration and deployment tools are then used to verify the replicability of the software and to generate an executable instance independent of the language through dockerization. This article presents this methodology and points at all the metrics and documentation repositories designed with this policy in Eurobench. Applying this approach to other protocols and metrics would ease the reproduction, replication, and comparison of experiments.This study is supported by the European Union’s Horizon 2020 research and innovation program under Grant Agreement no 779963, project Eurobench

Exploring Cosmic Origins with CORE: Cosmological Parameters

We forecast the main cosmological parameter constraints achievable with theCORE space mission which is dedicated to mapping the polarisation of the CosmicMicrowave Background (CMB). CORE was recently submitted in response to ESA'sfifth call for medium-sized mission proposals (M5). Here we report the resultsfrom our pre-submission study of the impact of various instrumental options, inparticular the telescope size and sensitivity level, and review the great,transformative potential of the mission as proposed. Specifically, we assessthe impact on a broad range of fundamental parameters of our Universe as afunction of the expected CMB characteristics, with other papers in the seriesfocusing on controlling astrophysical and instrumental residual systematics. Inthis paper, we assume that only a few central CORE frequency channels areusable for our purpose, all others being devoted to the cleaning ofastrophysical contaminants. On the theoretical side, we assume LCDM as ourgeneral framework and quantify the improvement provided by CORE over thecurrent constraints from the Planck 2015 release. We also study the jointsensitivity of CORE and of future Baryon Acoustic Oscillation and Large ScaleStructure experiments like DESI and Euclid. Specific constraints on the physicsof inflation are presented in another paper of the series. In addition to thesix parameters of the base LCDM, which describe the matter content of aspatially flat universe with adiabatic and scalar primordial fluctuations frominflation, we derive the precision achievable on parameters like thosedescribing curvature, neutrino physics, extra light relics, primordial heliumabundance, dark matter annihilation, recombination physics, variation offundamental constants, dark energy, modified gravity, reionization and cosmicbirefringence. (ABRIDGED

Planck intermediate results: XVI. Profile likelihoods for cosmological parameters

We explore the 2013 Planck likelihood function with a high-precision multi-dimensional minimizer (Minuit). This allows a refinement of the ΛCDM best-fit solution with respect to previously-released results, and the construction of frequentist confidence intervals using profile likelihoods. The agreement with the cosmological results from the Bayesian framework is excellent, demonstrating the robustness of the Planck results to the statistical methodology. We investigate the inclusion of neutrino masses, where more significant differences may appear due to the non-Gaussian nature of the posterior mass distribution. By applying the Feldman-Cousins prescription, we again obtain results very similar to those of the Bayesian methodology. However, the profile-likelihood analysis of the cosmic microwave background (CMB) combination (Planck+WP+highL) reveals a minimum well within the unphysical negative-mass region. We show that inclusion of the Planck CMB-lensing information regularizes this issue, and provide a robust frequentist upper limit ∑ mν ≤ 0.26 eV (95% confidence) from the CMB+lensing+BAO data combination. Reproduced with permission from Astronomy & Astrophysics, © ESO 201