Browsing by Author "Remazeilles, Anthony"
Now showing 1 - 4 of 4
Results Per Page
Sort Options
Item 3D Convolutional Neural Networks Initialized from Pretrained 2D Convolutional Neural Networks for Classification of Industrial Parts(Multidisciplinary Digital Publishing Institute (MDPI), 2021-02-04) Merino, Ibon; Azpiazu, Jon; Remazeilles, Anthony; Sierra, BasilioDeep 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.Item Histogram-Based Descriptor Subset Selection for Visual Recognition of Industrial Parts(2020-06-01) Merino, Ibon; Azpiazu, Jon; Remazeilles, Anthony; Sierra, Basilio; Tecnalia Research & Innovation; ROBOTICA_FLEX; Robótica MédicaThis 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.Item Making Bipedal Robot Experiments Reproducible and Comparable: The Eurobench Software Approach: The Eurobench Software Approach(2022-08-29) Remazeilles, Anthony; Dominguez, Alfonso; Barralon, Pierre; Torres-Pardo, Adriana; Pinto, David; Aller, Felix; Mombaur, Katja; Conti, Roberto; Saccares, Lorenzo; Thorsteinsson, Freygardur; Prinsen, Erik; Cantón, Alberto; Castilla, Javier; Sanz-Morère, Clara B.; Tornero, Jesús; Torricelli, Diego; Tecnalia Research & Innovation; Robótica Médica; Medical TechnologiesThis 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.Item Regulating Grip Forces through EMG-Controlled Protheses for Transradial Amputees(2021-11-25) Rasines, Irati; Prada, Miguel; Bobrov, Viacheslav; Agrawal, Dhruv; Martinez, Leire; Iriondo, Pedro; Remazeilles, Anthony; McIntyre, Joseph; Tecnalia Research & Innovation; Robótica MédicaThis 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.