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622 results on '"Marković, Ivan"'

2. RAVE: A Framework for Radar Ego-Velocity Estimation

Author

Štironja, Vlaho-Josip, Petrović, Luka, Peršić, Juraj, Marković, Ivan, and Petrović, Ivan

Subjects
Computer Science - Robotics
Abstract

State estimation is an essential component of autonomous systems, usually relying on sensor fusion that integrates data from cameras, LiDARs and IMUs. Recently, radars have shown the potential to improve the accuracy and robustness of state estimation and perception, especially in challenging environmental conditions such as adverse weather and low-light scenarios. In this paper, we present a framework for ego-velocity estimation, which we call RAVE, that relies on 3D automotive radar data and encompasses zero velocity detection, outlier rejection, and velocity estimation. In addition, we propose a simple filtering method to discard infeasible ego-velocity estimates. We also conduct a systematic analysis of how different existing outlier rejection techniques and optimization loss functions impact estimation accuracy. Our evaluation on three open-source datasets demonstrates the effectiveness of the proposed filter and a significant positive impact of RAVE on the odometry accuracy. Furthermore, we release an open-source implementation of the proposed framework for radar ego-velocity estimation accompanied with a ROS interface.

Published
2024

3. GenDepth: Generalizing Monocular Depth Estimation for Arbitrary Camera Parameters via Ground Plane Embedding

Author

Koledić, Karlo, Petrović, Luka, Petrović, Ivan, and Marković, Ivan

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Robotics
Abstract

Learning-based monocular depth estimation leverages geometric priors present in the training data to enable metric depth perception from a single image, a traditionally ill-posed problem. However, these priors are often specific to a particular domain, leading to limited generalization performance on unseen data. Apart from the well studied environmental domain gap, monocular depth estimation is also sensitive to the domain gap induced by varying camera parameters, an aspect that is often overlooked in current state-of-the-art approaches. This issue is particularly evident in autonomous driving scenarios, where datasets are typically collected with a single vehicle-camera setup, leading to a bias in the training data due to a fixed perspective geometry. In this paper, we challenge this trend and introduce GenDepth, a novel model capable of performing metric depth estimation for arbitrary vehicle-camera setups. To address the lack of data with sufficiently diverse camera parameters, we first create a bespoke synthetic dataset collected with different vehicle-camera systems. Then, we design GenDepth to simultaneously optimize two objectives: (i) equivariance to the camera parameter variations on synthetic data, (ii) transferring the learned equivariance to real-world environmental features using a single real-world dataset with a fixed vehicle-camera system. To achieve this, we propose a novel embedding of camera parameters as the ground plane depth and present a novel architecture that integrates these embeddings with adversarial domain alignment. We validate GenDepth on several autonomous driving datasets, demonstrating its state-of-the-art generalization capability for different vehicle-camera systems.

Published
2023

4. A Distance-Geometric Method for Recovering Robot Joint Angles From an RGB Image

Author

Bilić, Ivan, Marić, Filip, Marković, Ivan, and Petrović, Ivan

Subjects
Computer Science - Robotics, Computer Science - Artificial Intelligence, Computer Science - Computational Geometry, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Autonomous manipulation systems operating in domains where human intervention is difficult or impossible (e.g., underwater, extraterrestrial or hazardous environments) require a high degree of robustness to sensing and communication failures. Crucially, motion planning and control algorithms require a stream of accurate joint angle data provided by joint encoders, the failure of which may result in an unrecoverable loss of functionality. In this paper, we present a novel method for retrieving the joint angles of a robot manipulator using only a single RGB image of its current configuration, opening up an avenue for recovering system functionality when conventional proprioceptive sensing is unavailable. Our approach, based on a distance-geometric representation of the configuration space, exploits the knowledge of a robot's kinematic model with the goal of training a shallow neural network that performs a 2D-to-3D regression of distances associated with detected structural keypoints. It is shown that the resulting Euclidean distance matrix uniquely corresponds to the observed configuration, where joint angles can be recovered via multidimensional scaling and a simple inverse kinematics procedure. We evaluate the performance of our approach on real RGB images of a Franka Emika Panda manipulator, showing that the proposed method is efficient and exhibits solid generalization ability. Furthermore, we show that our method can be easily combined with a dense refinement technique to obtain superior results., Comment: IFAC 2023

Published
2023

6. Cost-Effective Robot Arm Teleoperation Via Human Pose Tracking with Monocular Camera

Author

Parfaite, Bernard, Petrović, Luka, Štironja, Vlaho-Josip, Marković, Ivan, Petrović, Ivan, Ceccarelli, Marco, Series Editor, Corves, Burkhard, Advisory Editor, Glazunov, Victor, Advisory Editor, Hernández, Alfonso, Advisory Editor, Huang, Tian, Advisory Editor, Jauregui Correa, Juan Carlos, Advisory Editor, Takeda, Yukio, Advisory Editor, Agrawal, Sunil K., Advisory Editor, Pisla, Doina, editor, Carbone, Giuseppe, editor, Condurache, Daniel, editor, and Vaida, Calin, editor

Published
2024
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7. MOVRO: Loosely Coupled EKF-Based Monocular Visual Radar Odometry

Author

Štironja, Vlaho-Josip, Peršić, Juraj, Marković, Ivan, Petrović, Ivan, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Lee, Soon-Geul, editor, An, Jinung, editor, Chong, Nak Young, editor, Strand, Marcus, editor, and Kim, Joo H., editor

Published
2024
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10. Motion Planning in Dynamic Environments Using Context-Aware Human Trajectory Prediction

Author

Finean, Mark Nicholas, Petrović, Luka, Merkt, Wolfgang, Marković, Ivan, and Havoutis, Ioannis

Subjects
Computer Science - Robotics
Abstract

Over the years, the separate fields of motion planning, mapping, and human trajectory prediction have advanced considerably. However, the literature is still sparse in providing practical frameworks that enable mobile manipulators to perform whole-body movements and account for the predicted motion of moving obstacles. Previous optimisation-based motion planning approaches that use distance fields have suffered from the high computational cost required to update the environment representation. We demonstrate that GPU-accelerated predicted composite distance fields significantly reduce the computation time compared to calculating distance fields from scratch. We integrate this technique with a complete motion planning and perception framework that accounts for the predicted motion of humans in dynamic environments, enabling reactive and pre-emptive motion planning that incorporates predicted motions. To achieve this, we propose and implement a novel human trajectory prediction method that combines intention recognition with trajectory optimisation-based motion planning. We validate our resultant framework on a real-world Toyota Human Support Robot (HSR) using live RGB-D sensor data from the onboard camera. In addition to providing analysis on a publicly available dataset, we release the Oxford Indoor Human Motion (Oxford-IHM) dataset and demonstrate state-of-the-art performance in human trajectory prediction. The Oxford-IHM dataset is a human trajectory prediction dataset in which people walk between regions of interest in an indoor environment. Both static and robot-mounted RGB-D cameras observe the people while tracked with a motion-capture system., Comment: 20 pages, 13 figures, 5 tables

Published
2022

11. Lane Change Classification with Neural Networks for Automated Conversion of Logical Scenarios

Author

Diklić, Vjekoslav, Marković, Ivan, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Petrovic, Ivan, editor, Menegatti, Emanuele, editor, and Marković, Ivan, editor

Published
2023
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12. On Hand-Eye Calibration via On-Manifold Gauss-Newton Optimization

Author

Čolaković-Bencerić, Marta, Peršić, Juraj, Marković, Ivan, Petrović, Ivan, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Petrovic, Ivan, editor, Menegatti, Emanuele, editor, and Marković, Ivan, editor

Published
2023
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13. Recalibrating the KITTI Dataset Camera Setup for Improved Odometry Accuracy

Author

Cvišić, Igor, Marković, Ivan, and Petrović, Ivan

Subjects
Computer Science - Robotics, Computer Science - Computer Vision and Pattern Recognition
Abstract

Over the last decade, one of the most relevant public datasets for evaluating odometry accuracy is the KITTI dataset. Beside the quality and rich sensor setup, its success is also due to the online evaluation tool, which enables researchers to benchmark and compare algorithms. The results are evaluated on the test subset solely, without any knowledge about the ground truth, yielding unbiased, overfit free and therefore relevant validation for robot localization based on cameras, 3D laser or combination of both. However, as any sensor setup, it requires prior calibration and rectified stereo images are provided, introducing dependence on the default calibration parameters. Given that, a natural question arises if a better set of calibration parameters can be found that would yield higher odometry accuracy. In this paper, we propose a new approach for one shot calibration of the KITTI dataset multiple camera setup. The approach yields better calibration parameters, both in the sense of lower calibration reprojection errors and lower visual odometry error. We conducted experiments where we show for three different odometry algorithms, namely SOFT2, ORB-SLAM2 and VISO2, that odometry accuracy is significantly improved with the proposed calibration parameters. Moreover, our odometry, SOFT2, in conjunction with the proposed calibration method achieved the highest accuracy on the official KITTI scoreboard with 0.53% translational and 0.0009 deg/m rotational error, outperforming even 3D laser-based methods.

Published
2021

14. Feature-based Event Stereo Visual Odometry

Author

Hadviger, Antea, Cvišić, Igor, Marković, Ivan, Vražić, Sacha, and Petrović, Ivan

Subjects
Computer Science - Robotics, Computer Science - Computer Vision and Pattern Recognition
Abstract

Event-based cameras are biologically inspired sensors that output events, i.e., asynchronous pixel-wise brightness changes in the scene. Their high dynamic range and temporal resolution of a microsecond makes them more reliable than standard cameras in environments of challenging illumination and in high-speed scenarios, thus developing odometry algorithms based solely on event cameras offers exciting new possibilities for autonomous systems and robots. In this paper, we propose a novel stereo visual odometry method for event cameras based on feature detection and matching with careful feature management, while pose estimation is done by reprojection error minimization. We evaluate the performance of the proposed method on two publicly available datasets: MVSEC sequences captured by an indoor flying drone and DSEC outdoor driving sequences. MVSEC offers accurate ground truth from motion capture, while for DSEC, which does not offer ground truth, in order to obtain a reference trajectory on the standard camera frames we used our SOFT visual odometry, one of the highest ranking algorithms on the KITTI scoreboards. We compared our method to the ESVO method, which is the first and still the only stereo event odometry method, showing on par performance on the MVSEC sequences, while on the DSEC dataset ESVO, unlike our method, was unable to handle outdoor driving scenario with default parameters. Furthermore, two important advantages of our method over ESVO are that it adapts tracking frequency to the asynchronous event rate and does not require initialization., Comment: Submitted to Accepted to European Conference on Mobile Robots (ECMR) 2021

Published
2021

16. Ensemble of LSTMs and feature selection for human action prediction

Author

Petković, Tomislav, Petrović, Luka, Marković, Ivan, and Petrović, Ivan

Subjects
Computer Science - Robotics
Abstract

As robots are becoming more and more ubiquitous in human environments, it will be necessary for robotic systems to better understand and predict human actions. However, this is not an easy task, at times not even for us humans, but based on a relatively structured set of possible actions, appropriate cues, and the right model, this problem can be computationally tackled. In this paper, we propose to use an ensemble of long-short term memory (LSTM) networks for human action prediction. To train and evaluate models, we used the MoGaze dataset - currently the most comprehensive dataset capturing poses of human joints and the human gaze. We have thoroughly analyzed the MoGaze dataset and selected a reduced set of cues for this task. Our model can predict (i) which of the labeled objects the human is going to grasp, and (ii) which of the macro locations the human is going to visit (such as table or shelf). We have exhaustively evaluated the proposed method and compared it to individual cue baselines. The results suggest that our LSTM model slightly outperforms the gaze baseline in single object picking accuracy, but achieves better accuracy in macro object prediction. Furthermore, we have also analyzed the prediction accuracy when the gaze is not used, and in this case, the LSTM model considerably outperformed the best single cue baseline

Published
2021

17. Road Curb Detection: ADAS for a Road Sweeper Vehicle

Author

Bilić, Ivan, Popović, Goran, Savić, Tibor Bataljak, Marković, Ivan, Petrović, Ivan, Ceccarelli, Marco, Series Editor, Agrawal, Sunil K., Advisory Editor, Corves, Burkhard, Advisory Editor, Glazunov, Victor, Advisory Editor, Hernández, Alfonso, Advisory Editor, Huang, Tian, Advisory Editor, Jauregui Correa, Juan Carlos, Advisory Editor, Takeda, Yukio, Advisory Editor, Petrič, Tadej, editor, Ude, Aleš, editor, and Žlajpah, Leon, editor

Published
2023
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19. Human Intention Recognition for Human Aware Planning in Integrated Warehouse Systems

Author

Petković, Tomislav, Hvězda, Jakub, Rybecký, Tomáš, Marković, Ivan, Kulich, Miroslav, Přeučil, Libor, and Petrović, Ivan

Subjects
Computer Science - Robotics
Abstract

With the substantial growth of logistics businesses the need for larger and more automated warehouses increases, thus giving rise to fully robotized shop-floors with mobile robots in charge of transporting and distributing goods. However, even in fully automatized warehouse systems the need for human intervention frequently arises, whether because of maintenance or because of fulfilling specific orders, thus bringing mobile robots and humans ever closer in an integrated warehouse environment. In order to ensure smooth and efficient operation of such a warehouse, paths of both robots and humans need to be carefully planned; however, due to the possibility of humans deviating from the assigned path, this becomes an even more challenging task. Given that, the supervising system should be able to recognize human intentions and its alternative paths in real-time. In this paper, we propose a framework for human deviation detection and intention recognition which outputs the most probable paths of the humans workers and the planner that acts accordingly by replanning for robots to move out of the human's path. Experimental results demonstrate that the proposed framework increases total number of deliveries, especially human deliveries, and reduces human-robot encounters., Comment: To be presented in 28th Mediterranean Conference on Control and Automation (MED'2020) conference

Published
2020

21. Stereo Event Lifetime and Disparity Estimation for Dynamic Vision Sensors

Author

Hadviger, Antea, Marković, Ivan, and Petrović, Ivan

Subjects
Computer Science - Robotics, Computer Science - Computer Vision and Pattern Recognition
Abstract

Event-based cameras are biologically inspired sensors that output asynchronous pixel-wise brightness changes in the scene called events. They have a high dynamic range and temporal resolution of a microsecond, opposed to standard cameras that output frames at fixed frame rates and suffer from motion blur. Forming stereo pairs of such cameras can open novel application possibilities, since for each event depth can be readily estimated; however, to fully exploit asynchronous nature of the sensor and avoid fixed time interval event accumulation, stereo event lifetime estimation should be employed. In this paper, we propose a novel method for event lifetime estimation of stereo event-cameras, allowing generation of sharp gradient images of events that serve as input to disparity estimation methods. Since a single brightness change triggers events in both event-camera sensors, we propose a method for single shot event lifetime and disparity estimation, with association via stereo matching. The proposed method is approximately twice as fast and more accurate than if lifetimes were estimated separately for each sensor and then stereo matched. Results are validated on real-world data through multiple stereo event-camera experiments., Comment: Accepted to European Conference on Mobile Robots (ECMR) 2019

Published
2019

22. Stochastic Optimization for Trajectory Planning with Heteroscedastic Gaussian Processes

Author

Petrović, Luka, Peršić, Juraj, Seder, Marija, and Marković, Ivan

Subjects
Computer Science - Robotics
Abstract

Trajectory optimization methods for motion planning attempt to generate trajectories that minimize a suitable objective function. Such methods efficiently find solutions even for high degree-of-freedom robots. However, a globally optimal solution is often intractable in practice and state-of-the-art trajectory optimization methods are thus prone to local minima, especially in cluttered environments. In this paper, we propose a novel motion planning algorithm that employs stochastic optimization based on the cross-entropy method in order to tackle the local minima problem. We represent trajectories as samples from a continuous-time Gaussian process and introduce heteroscedasticity to generate powerful trajectory priors better suited for collision avoidance in motion planning problems. Our experimental evaluation shows that the proposed approach yields a more thorough exploration of the solution space and a higher success rate in complex environments than a current Gaussian process based state-of-the-art trajectory optimization method, namely GPMP2, while having comparable execution time., Comment: Accepted to European Conference on Mobile Robots (ECMR) 2019

Published
2019

23. Pedestrian Tracking by Probabilistic Data Association and Correspondence Embeddings

Author

Bićanić, Borna, Oršić, Marin, Marković, Ivan, Šegvić, Siniša, and Petrović, Ivan

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Computer Science - Robotics
Abstract

This paper studies the interplay between kinematics (position and velocity) and appearance cues for establishing correspondences in multi-target pedestrian tracking. We investigate tracking-by-detection approaches based on a deep learning detector, joint integrated probabilistic data association (JIPDA), and appearance-based tracking of deep correspondence embeddings. We first addressed the fixed-camera setup by fine-tuning a convolutional detector for accurate pedestrian detection and combining it with kinematic-only JIPDA. The resulting submission ranked first on the 3DMOT2015 benchmark. However, in sequences with a moving camera and unknown ego-motion, we achieved the best results by replacing kinematic cues with global nearest neighbor tracking of deep correspondence embeddings. We trained the embeddings by fine-tuning features from the second block of ResNet-18 using angular loss extended by a margin term. We note that integrating deep correspondence embeddings directly in JIPDA did not bring significant improvement. It appears that geometry of deep correspondence embeddings for soft data association needs further investigation in order to obtain the best from both worlds.

Published
2019

24. Spatio-Temporal Multisensor Calibration Based on Gaussian Processes Moving Object Tracking

Author

Peršić, Juraj, Petrović, Luka, Marković, Ivan, and Petrović, Ivan

Subjects
Computer Science - Robotics
Abstract

Perception is one of the key abilities of autonomous mobile robotic systems, which often relies on fusion of heterogeneous sensors. Although this heterogeneity presents a challenge for sensor calibration, it is also the main prospect for reliability and robustness of autonomous systems. In this paper, we propose a method for multisensor calibration based on Gaussian processes (GPs) estimated moving object trajectories, resulting with temporal and extrinsic parameters. The appealing properties of the proposed temporal calibration method are: coordinate frame invariance, thus avoiding prior extrinsic calibration, theoretically grounded batch state estimation and interpolation using GPs, computational efficiency with O(n) complexity, leveraging data already available in autonomous robot platforms, and the end result enabling 3D point-to-point extrinsic multisensor calibration. The proposed method is validated both in simulations and real-world experiments. For real-world experiment we evaluated the method on two multisensor systems: an externally triggered stereo camera, thus having temporal ground truth readily available, and a heterogeneous combination of a camera and motion capture system. The results show that the estimated time delays are accurate up to a fraction of the fastest sensor sampling time.

Published
2019

25. Effect of the COVID-19 pandemic on surgery for indeterminate thyroid nodules (THYCOVID): a retrospective, international, multicentre, cross-sectional study

Author

Abdallah, A, Abentroth, AL, Acheimastos, V, Agunaoun, M, Al Bisher, HM, Al Ghuzlan, A, Alakus, H, Alkan, M, Almaraz Almaraz, MC, Amram, K, Anesidis, S, Anestiadou, E, Angelucci, D, Ansaldo, GL, Antonopoulou, MI, Arciniegas, M, Armellin, C, Arredondo Saldaña, G, Astl, J, Athanasakis, E, Avenia, S, Aydın, H, Baba, B, Babala, J, Banús, MV, Barba-Valadez, LA, Barcons, SV, Battafarano, F, Bayat, A, Bella, RMC, Benariba, F, Bernardi, S, Bignami, EG, Bitenc, M, Bitsianis, S, Bolaños de la Torre, JDD, Bonati, E, Bonetti, T, Borges, FA, Bouchagier, K, Boudina, M, Bourial, A, Breuskin, I, Brock, P, Bruns, C, Burlacu, MC, Burton, T, Buta, M, Buzanakov, D, Caliseo, C, Callanan, D, Calu, V, Cameselle-Teijeiro, JM, Camilo-Junior, DJ, Canberk, S, Candalise, V, Candanedo-Gonzalez, F, Carrillo Lizarazo, LJ, Carvalho, GB, Casallas, D, Casolino, C, Castellani, L, Castillo Morales, C, Chambon, G, Chatzipavlidou, V, Chernikov, R, Chorti, A, Chow, TCM, Chrisoulidou, A, Chrysos, E, Conrado-Neto, S, Cordova García, D, Corigliano, A, Crocco, A, Cuesta, A, Čukman, M, Curto, LS, Damilano, RA, D'Anna, R, De, M, De Virgilio, A, Dellaportas, D, Demarquet, L, Devresse, A, Di Meo, G, Diaz Pedrero, R, Dimitrov, D, Dmitry, Z, Domínguez Garijo, P, Dulgeroglu, O, Dural, AC, Eksi, A, El Hammoumi, M, El Kaoui, H, Eleni, G, Elliyanti, A, Ersöz, Ş, Escobar-Jiménez, M, Fedorova, L, Feeley, L, Fernández Rodríguez, E, Ferreli, F, Filoia, A, Fingeret, A, Francescato, A, Gaino, F, Galiandro, F, Gallegos-Hernández, JF, Garas, G, García Lorenzo, F, García-Chávez, JP, Gaudiello, M, Gay, S, Gerasimos, S, Gerek, M, Gervasi, R, Giordano, A, Gjeloshi, B, Gocký, L, Golubinskaya, E, González Romero, S, González-Mínguez, C, Goran, M, Gosman, A, Granados Garcia, M, Greco, E, Grünbart, M, Grützmann, R, Guerlain, J, Guirao, XG, Guzey, D, Hajjij, A, Hamdy, O, Hameed, MS, Hauth, LA, Hernández-Acevedo, JD, Hernandez-Carrillo, JF, Hevilla Sánchez, F, Hoi, H, Hongkwon, K, Hu Zhu, R, Huang, E, Hyeung Kyoo, K, Ignjatovic, V, Ioannidis, A, Iossa, A, Işık, A, James, D, Jung Hoon, L, Kara, H, Karajovic, J, Kartini, D, Khambri, D, Kholová, I, Kisiel, M, Knežević, M, Koh, YQ, Konca, C, Kosmidis, C, Kotsovolis, G, Kowalski, LP, Kralik, R, Kuczma, P, Kuravi, BG, Kurnia, A, Kyriaki, V, Lai, CM, Lallemant, B, Lardhi, AA, Leboulleux, S, Lee, JW, Lelli, G, Leutner, M, Lim, MY, Lim, CM, Llanos, A, Lo, X, Loderer, T, López-Corrales, MA, Ludwig, M, Magnabosco, FF, Maheo, C, Maia, AL, Makay, O, Maksimova, P, Mallick, S, Mallouk, C, Mamani, Z, Mandal, S, Manyalich Blasi, M, Marincola, G, Marulanda, M, Mavromati, M, Mayilvaganan, S, Metso, S, Micalizzi, A, Michalopoulos, A, Min-Su, K, Miron, A, Mishra, AK, Misso, C, Mittermair, C, Morosán Allo, Y, Mourad, M, Moysidis, M, Nabhan, F, Nasiri, R, Nastos, C, Ngiam, KY, Nomine-Criqui, C, Ntziovara, AM, Nuño Vázquez-Garza, JM, Nutautiene, V, Obtulovičová, K, O'Keeffe, L, Okudur, NO, Ossola, P, Ovejero Merino, E, Ozdemir, M, Pangonis, A, Panigoro, SS, Panuzi, A, Papaconstantinou, D, Pardo Matamoros, N, Paschou, S, Pasculli, A, Paterakis, K, Peiris, K, Pennestrì, F, Peppa, M, Perdikaris, P, Perdikaris, I, Pérez-Soto, RH, Piana, S, Piccoli, M, Pietrasanta, D, Placentino, G, Pliakos, I, Polistena, A, Pongtippan, A, Potard, G, Quinn, V, Rahul, P, Ramos, T, Rankin, A, Ratnayake, P, Reuto-Castillo, J, Ridolfo, A, Rios-Valencia, J, Riss, P, Rival, E, Rivillas, J, Roi, D, Rollo, EM, Romanchishen, A, Romito, M, Rotnagl, J, Rovcanin, B, Russo, G, Sabol, M, Saki, S, Saleh, S, Salih, A, Saltiki, A, Salvador-Camarmo, G, Samal, DK, Sánchez-Flores, S, Sapalidis, K, Sarin, D, Sarin, H, Savkovic, N, Scheffel, RS, Scheinpflug, AL, Scheuba, C, Scheyer, N, Schmidt, M, Senashova, O, Serafini, E, Serrano Arévalo, ML, Shank, J, Shindo, ML, Shoshkova, M, Shvan, M, Sičák, M, Silva, TG, Simó Guerrero, O, Skuletic, V, Slijepcevic, N, Slovic, Z, Soares, P, Somova, A, Soto, S, Spiezia, S, Stankovic, V, Stephenson, KJ, Straub, E, Summa, M, Surani, S, Syed, AA, Symeonidis, S, Taciak, A, Tarallo, M, Tarle, A, Tasis, N, Tausanovic, K, Tchabashvili, L, Thierry, M, Tokarczyk, U, Toma, EA, Topuz, S, Torresan, F, Uras, C, Vaccaro, C, Valdés de Anca, Á, Valentini, M, Varaldo, E, Vartanian, JG, Verras, GI, Vithanage, A, Wijayalathge, H, Wiriyaamornchai, P, Wong, YLC, Wongwattana, P, Xenaki, S, Xie, S, Xu, M, Yang, W, Yilmaz, S, Yılmaz, YF, Yotsov, T, Zahid, MT, Zielke, A, Medas, Fabio, Dobrinja, Chiara, Al-Suhaimi, Ebtesam Abdullah, Altmeier, Julia, Anajar, Said, Arikan, Akif Enes, Azaryan, Irina, Bains, Lovenish, Basili, Giancarlo, Bolukbasi, Hakan, Bononi, Marco, Borumandi, Farzad, Bozan, Mehmet Buğra, Brenta, Gabriela, Brunaud, Laurent, Brunner, Maximilian, Buemi, Antoine, Canu, Gian Luigi, Cappellacci, Federico, Cartwright, Sara Burchfield, Castells Fusté, Ignasi, Cavalheiro, Beatriz, Cavallaro, Giuseppe, Chala, Andres, Chan, Shun Yan Bryant, Chaplin, John, Cheema, Mustafa Sajjad, Chiapponi, Costanza, Chiofalo, Maria Grazia, Chrysos, Emmanuel, D'Amore, Annamaria, de Cillia, Michael, De Crea, Carmela, de Manzini, Nicolò, de Matos, Leandro Luongo, De Pasquale, Loredana, Del Rio, Paolo, Demarchi, Marco Stefano, Dhiwakar, Muthuswamy, Donatini, Gianluca, Dora, Jose Miguel, D'Orazi, Valerio, Doulatram Gamgaram, Viyey Kishore, Eismontas, Vitalijus, Kabiri, El Hassane, El Malki, Hadj Omar, Elzahaby, Islam, Enciu, Octavian, Eskander, Antoine, Feroci, Francesco, Figueroa-Bohorquez, David, Filis, Dimitrios, Gorostidi, François, Frías-Fernández, Pedro, Gamboa-Dominguez, Armando, Genc, Volkan, Giordano, Davide, Gómez-Pedraza, Antonio, Graceffa, Giuseppa, Griffin, James, Guerreiro, Sofia Cuco, Gupta, Karan, Gupta, Keshav Kumar, Gurrado, Angela, Hajiioannou, Jiannis, Hakala, Tommi, Harahap, Wirsma Arif, Hargitai, Lindsay, Hartl, Dana, Hellmann, Andrzej, Hlozek, Jiri, Hoang, Van Trung, Iacobone, Maurizio, Innaro, Nadia, Ioannidis, Orestis, Jang, J H Isabelle, Xavier-Junior, Jose Candido, Jovanovic, Milan, Kaderli, Reto Martin, Kakamad, Fahmi, Kaliszewski, Krzysztof, Karamanliev, Martin, Katoh, Hiroshi, Košec, Andro, Kovacevic, Bozidar, Kowalski, Luiz Paulo, Králik, Robert, Yadav, Sanjay Kumar, Kumorová, Adriána, Lampridis, Savvas, Lasithiotakis, Konstantinos, Leclere, Jean-Christophe, Leong, Eugene Kwong Fei, Leow, Melvin Khee-Shing, Lim, James Y, Lino-Silva, Leonardo S, Liu, Shirley Yuk Wah, Llorach, Núria Perucho, Lombardi, Celestino Pio, López-Gómez, Javier, Lori, Eleonora, Quintanilla-Dieck, Lourdes, Lucchini, Roberta, Madani, Amin, Manatakis, Dimitrios, Markovic, Ivan, Materazzi, Gabriele, Mazeh, Haggi, Mercante, Giuseppe, Meyer-Rochow, Goswin Yason, Mihaljevic, Olgica, Miller, Julie A, Minuto, Michele, Monacelli, Massimo, Mulita, Francesk, Mullineris, Barbara, Muñoz-de-Nova, José Luis, Muradás Girardi, Fábio, Nader, Saki, Napadon, Tangjaturonrasme, Nastos, Constantinos, Offi, Chiara, Ronen, Ohad, Oragano, Luigi, Orois, Aida, Pan, Yongqin, Panagiotidis, Emmanouil, Panchangam, Ramakanth Bhargav, Papavramidis, Theodosios, Parida, Pradipta Kumar, Paspala, Anna, Pérez, Òscar Vidal, Petrovic, Sabrina, Raffaelli, Marco, Ramacciotti, Constanza Fernanda, Ratia Gimenez, Tomas, Rivo Vázquez, Ángel, Roh, Jong-Lyel, Rossi, Leonardo, Sanabria, Alvaro, Santeerapharp, Alena, Semenov, Arseny, Seneviratne, Sanjeewa, Serdar, Altinay, Sheahan, Patrick, Sheppard, Sean C, Slotcavage, Rachel L, Smaxwil, Constantin, Kim, Soo Young, Sorrenti, Salvatore, Spartalis, Eleftherios, Sriphrapradang, Chutintorn, Testini, Mario, Turk, Yigit, Tzikos, George, Vabalayte, Kristina, Vargas-Osorio, Kelly, Vázquez Rentería, Rafael Sebastián, Velázquez-Fernández, David, Vithana, Sanura Malinda Pallegoda, Yücel, Levent, Yulian, Erwin Danil, Zahradnikova, Petra, Zarogoulidis, Paul, Ziablitskaia, Evgeniia, Zolotoukho, Anna, and Calò, Pietro Giorgio

Published
2023
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29. Ensemble of LSTMs and Feature Selection for Human Action Prediction

Author

Petković, Tomislav, Petrović, Luka, Marković, Ivan, Petrović, Ivan, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Ang Jr, Marcelo H., editor, Asama, Hajime, editor, Lin, Wei, editor, and Foong, Shaohui, editor

Published
2022
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30. Human Intention Estimation based on Hidden Markov Model Motion Validation for Safe Flexible Robotized Warehouses

Author

Petković, Tomislav, Puljiz, David, Marković, Ivan, and Hein, Björn

Subjects
Computer Science - Robotics
Abstract

With the substantial growth of logistics businesses the need for larger warehouses and their automation arises, thus using robots as assistants to human workers is becoming a priority. In order to operate efficiently and safely, robot assistants or the supervising system should recognize human intentions in real-time. Theory of mind (ToM) is an intuitive human conception of other humans' mental state, i.e., beliefs and desires, and how they cause behavior. In this paper we propose a ToM based human intention estimation algorithm for flexible robotized warehouses. We observe human's, i.e., worker's motion and validate it with respect to the goal locations using generalized Voronoi diagram based path planning. These observations are then processed by the proposed hidden Markov model framework which estimates worker intentions in an online manner, capable of handling changing environments. To test the proposed intention estimation we ran experiments in a real-world laboratory warehouse with a worker wearing Microsoft Hololens augmented reality glasses. Furthermore, in order to demonstrate the scalability of the approach to larger warehouses, we propose to use virtual reality digital warehouse twins in order to realistically simulate worker behavior. We conducted intention estimation experiments in the larger warehouse digital twin with up to 24 running robots. We demonstrate that the proposed framework estimates warehouse worker intentions precisely and in the end we discuss the experimental results.

Published
2018
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31. Computationally efficient dense moving object detection based on reduced space disparity estimation

Author

Popović, Goran, Hadviger, Antea, Marković, Ivan, and Petrović, Ivan

Subjects
Computer Science - Robotics
Abstract

Computationally efficient moving object detection and depth estimation from a stereo camera is an extremely useful tool for many computer vision applications, including robotics and autonomous driving. In this paper we show how moving objects can be densely detected by estimating disparity using an algorithm that improves complexity and accuracy of stereo matching by relying on information from previous frames. The main idea behind this approach is that by using the ego-motion estimation and the disparity map of the previous frame, we can set a prior base that enables us to reduce the complexity of the current frame disparity estimation, subsequently also detecting moving objects in the scene. For each pixel we run a Kalman filter that recursively fuses the disparity prediction and reduced space semi-global matching (SGM) measurements. The proposed algorithm has been implemented and optimized using streaming single instruction multiple data instruction set and multi-threading. Furthermore, in order to estimate the process and measurement noise as reliably as possible, we conduct extensive experiments on the KITTI suite using the ground truth obtained by the 3D laser range sensor. Concerning disparity estimation, compared to the OpenCV SGM implementation, the proposed method yields improvement on the KITTI dataset sequences in terms of both speed and accuracy.

Published
2018

32. Multi-agent Gaussian Process Motion Planning via Probabilistic Inference

Author

Petrović, Luka, Marković, Ivan, and Seder, Marija

Subjects
Computer Science - Robotics
Abstract

This paper deals with motion planning for multiple agents by representing the problem as a simultaneous optimization of every agent's trajectory. Each trajectory is considered as a sample from a one-dimensional continuous-time Gaussian process (GP) generated by a linear time-varying stochastic differential equation driven by white noise. By formulating the planning problem as probabilistic inference on a factor graph, the structure of the pertaining GP can be exploited to find the solution efficiently using numerical optimization. In contrast to planning each agent's trajectory individually, where only the current poses of other agents are taken into account, we propose simultaneous planning of multiple trajectories that works in a predictive manner. It takes into account the information about each agent's whereabouts at every future time instant, since full trajectories of each agent are found jointly during a single optimization procedure. We compare the proposed method to an individual trajectory planning approach, demonstrating significant improvement in both success rate and computational efficiency., Comment: Accepted for oral presentation at 12th IFAC Symposium on Robot Control (SYROCO 2018)

Published
2018

33. Human Intention Recognition in Flexible Robotized Warehouses based on Markov Decision Processes

Author

Petković, Tomislav, Marković, Ivan, and Petrović, Ivan

Subjects
Computer Science - Robotics, Computer Science - Human-Computer Interaction
Abstract

The rapid growth of e-commerce increases the need for larger warehouses and their automation, thus using robots as assistants to human workers becomes a priority. In order to operate efficiently and safely, robot assistants or the supervising system should recognize human intentions. Theory of mind (ToM) is an intuitive conception of other agents' mental state, i.e., beliefs and desires, and how they cause behavior. In this paper we present a ToM-based algorithm for human intention recognition in flexible robotized warehouses. We have placed the warehouse worker in a simulated 2D environment with three potential goals. We observe agent's actions and validate them with respect to the goal locations using a Markov decision process framework. Those observations are then processed by the proposed hidden Markov model framework which estimated agent's desires. We demonstrate that the proposed framework predicts human warehouse worker's desires in an intuitive manner and in the end we discuss the simulation results.

Published
2018

34. Elective surgery system strengthening: development, measurement, and validation of the surgical preparedness index across 1632 hospitals in 119 countries

Author

Glasbey, James C, Abbott, Tom EF, Ademuyiwa, Adesoji, Adisa, Adewale, AlAmeer, Ehab, Alshryda, Sattar, Arnaud, Alexis P, Bankhead-Kendall, Brittany, Abou Chaar, M K, Chaudhry, Daoud, Costas-Chavarri, Ainhoa, Cunha, Miguel F, Davies, Justine I, Desai, Anant, Elhadi, Muhammed, Fiore, Marco, Fitzgerald, James Edward, Fourtounas, Maria, Fowler, Alex James, Futaba, Kay, Gallo, Gaetano, Ghosh, Dhruva, Gujjuri, Rohan R, Hamilton, Rebecca, Haque, Parvez, Harrison, Ewen M, Hutchinson, Peter, Hyman, Gabriella, Isik, Arda, Jayarajah, Umesh, Kaafarani, Haytham MA, Kadir, Bryar, Lawani, Ismail, Lederhuber, Hans, Li, Elizabeth, Löffler, Markus W, Lorena, Maria Aguilera, Mann, Harvinder, Martin, Janet, Mazingi, Dennis, McClain, Craig D, McLean, Kenneth A, Meara, John G, Ramos-De La Medina, Antonio, Mengesha, Mengistu, Minaya, Ana, Modolo, Maria Marta, Moore, Rachel, Morton, Dion, Nepogodiev, Dmitri, Ntirenganya, Faustin, Pata, Francesco, Pearse, Rupert, Picciochi, Maria, Pinkney, Thomas, Pockney, Peter, van Ramshorst, Gabrielle H, Richards, Toby, Roslani, April Camilla, Satoi, Sohei, Sayyed, Raza, Shaw, Richard, Simões, Joana FF, Smart, Neil, Sullivan, Richard, Sund, Malin, Sundar, Sudha, Tabiri, Stephen, Taylor, Elliott H, Venn, Mary L, Wickramasinghe, Dakshitha, Wright, Naomi, Yip, Sebastian Bernardo Shu, Bhangu, Aneel, Omar, Omar, Harrison, Ewen, Bhangu, Aneel A, Siaw-Acheampong, Kwabena, Benson, Ruth A, Bywater, Edward, Dawson, Brett E, Evans, Jonathan P, Heritage, Emily, Jones, Conor S, Kamarajah, Sivesh K, Khatri, Chetan, Khaw, Rachel A, Keatley, James M, Knight, Andrew, Lawday, Samuel, Mann, Harvinder S, Marson, Ella J, Mckay, Siobhan C, Mills, Emily C, Pellino, Gianluca, Tiwari, Abhinav, Trout, Isobel M, Wilkin, Richard JW, Abukhalaf, Sadi, Adamina, Michel, Ademuyiwa, Adesoji O, Agarwal, Arnav, Akkulak, Murat, Alameer, Ehab, Alderson, Derek, Alakaloko, Felix, Albertsmeier, Markus, Alser, Osaid, Alshaar, Muhammad, Augestad, Knut Magne, Ayasra, Faris, Azevedo, José, Bankhead-Kendall, Brittany K, Barlow, Emma, Beard, David, Blanco-Colino, Ruth, Brar, Amanpreet, Minaya-Bravo, Ana, Breen, Kerry A, Bretherton, Chris, Buarque, Igor Lima, Burke, Joshua, Caruana, Edward J, Chaar, Mohammad, Chakrabortee, Sohini, Christensen, Peter, Cox, Daniel, Cukier, Moises, Davidson, Giana H, Di Saverio, Salomone, Drake, Thomas M, Edwards, John G, Emile, Sameh, Farik, Shebani, Ford, Samuel, Garmanova, Tatiana, Gomes, Gustavo Mendonça Ataíde, Grecinos, Gustavo, Griffiths, Ewen A, Gruendl, Magdalena, Halkias, Constantine, Hisham, Intisar, Hutchinson, Peter J, Hwang, Shelley, Jenkinson, Michael D, Jonker, Pascal, Keller, Debby, Kolias, Angelos, Kruijff, Schelto, Leventoglu, Sezai, Litvin, Andrey, Loehrer, Andrew, Major, Piotr, Mashbari, Hassan N, Metallidis, Symeon, Mohan, Helen M, Moszkowicz, David, Moug, Susan, Ng-Kamstra, Joshua S, Maimbo, Mayaba, Negoi, Ionut, Niquen, Milagros, Olivos, Maricarmen, Oussama, Kacimi, Outani, Oumaima, Parreno-Sacdalanm, Marie Dione, Rivera, Carlos Jose Perez, Pinkney, Thomas D, Plas, Willemijn van der, Qureshi, Ahmad, Radenkovic, Dejan, Revell, Elliot J, Roberts, Keith, Roslani, April C, Rutegård, Martin, Segura-Sampedro, Juan José, Santos, Irène, Schache, Andrew, Schnitzbauer, Andreas A, Seyi-Olajide, Justina O, Sharma, Neil, Shaw, Catherine A, Shu, Sebastian, Soreide, Kjetil, Spinelli, Antonino, Stewart, Grant D, Townend, Philip, Tsoulfas, Georgios, Vidya, Raghavan, Vimalachandran, Dale, Warren, Oliver J, Wedderburn, Duane, EuroSurg, NA, European Society of Coloproctology (ESCP), NA, Global Initiative for Children's Surgery, NA, GlobalSurg, NA, GlobalPaedSurg, NA, ItSURG, NA, PTSurg, NA, SpainSurg, NA, Italian Society of Colorectal Surgery, NA, Association of Surgeons in Training, NA, Irish Surgical Research Collaborative (ISRC), NA, Transatlantic Australasian Retroperitoneal Sarcoma Working, NA, Italian Society of Surgical Oncology, NA, Booth, Lesley, Barker, Margaret, Barker, Neil, Cooke, Shirley, Doré, Suzanne, Horwood, Nigel, Runigamugabo, Emmy, Weir, Carrie Tierney, Bahrami-Hessari, Mike, Riaz, Asad, Shah, Jaffer, Safi, Mohammed, Thereska, Dariel, Dajti, Irida, Cheddadi, Riadh, Tidjane, Anisse, Quinteros, Carlos A, khelfaoui, Ahmed, Salem, Khalifa M, Riffi, Omar, Kacimi, Salah Eddine O, Loudjedi, Salim, Damerdji, Tidjani, Pantoja Pachajoa, Diana A, Palacios Huatuco, René M, Alvarez, Fernando A, Doniquian, Alejandro M, Abeldaño Zuñiga, Roberto A, Schlottmann, Francisco, Cobos, Carlos M, Gigena, Cecilia, Forneris, Agustin Albani, Duro, Agustin, García-Mansilla, Agustín M, Busnelli, Virginia Cano, Poggi, Catalina, Mercado, Pedro L, González, Marcos, Castro Lalin, Agustina F, Mayer, Horacio F, Brandariz, Rodrigo, Slullitel, Pablo A, Boudou, Rocio, Lobos, Pablo A, Uffelmann, María C, Petersen, Maria L, Luzzi, Emilia, Padilla Lichtenberger, Fernando L, Crespi Amor, María S, Zarratea, Celeste S, Esteves, Tomas A, Gemelli, Nicolas A, Tirapegui, Sebastián, Liyo, Juan, Scherñuk, Jordán, Boccalatte, Luis A, Balmaceda, Ruben D, D'Addino, Jose L, Caubet, María M, Calderón Arancibia, José A, Chwat, Carina, Morris, Brian, Avellaneda, Nicolas, Pedraza Salazar, Ivana I, Eskinazi, Diego G, Vargas, Lara, Muriel, María E, Lucchini, Sergio M, Gosselink, Martijn P, Davis, Amelia L, Barker, John C, Qin, Kirby R, Proud, David M, Cox, Daniel RA, Goh, Su Kah, Liu, David S, Wu, Damien M, Merrett, Neil D, Badiani, Sarit S, Sengupta, Shomik, Jain, Anshini, Steen, Christopher J, Wong, Enoch, Ip, Christopher CK, Leaning, Matthew G, McCartney, Conor B, Gananadha, Sivakumar, Yeap, Evie FW, Stevens, Sean G, Vu, Anh N, Martin, Sarah A, Stanley, Guy H M, Watson, David I, Townend, Philip J, Young, Thomas K, Cox, Georgia T, Dawson, Amanda C, Laura, Sharon E, Lun, Elizabeth W Y, Liang, Ina X, O'Neill, Christine J, Lott, Natalie J, Chuan, Alwin, Saravanan, SK, Gundara, Justin, Ong, Bee Shan, Nataraja, Ramesh M, Pacilli, Maurizio, Foley, Daniel M, Ooi, Geraldine J, Traeger, Luke, MacDermid, Ewan, Daruwalla, Jurstine, Hodgson, Russell, Heriot, Alexander G, Mulligan, Christopher S, Blefari, Nicholas D A, Purcell, Shaun S, Frankel, Adam J, Guerra, Glen R, Tefay, Joan S, Liang, Rhea W Y, Kroon, Hidde M, Farfus, Anthony W, Warren, Leigh R, Roy, Jennifer M, Whitfield, Robert J, Moller, Cea-Cea B, Davis, Sean S, Sammour, Tarik, Lam, Yick Ho, Kour, Kevin, Gan, Siang Wei, Coventry, Brendon J, Dawson, Joseph A, Batstone, Martin D, King, Sebastian K, Scott, Nathan J, Foo, Jonathan W, Shepherd, Talia, Page, Richard S, Choong, Peter F, Badgery, Henry E, Chong, Lynn, Taylor, Lillian, Hii, Michael W, Wright, Gavin M, Kong, Joseph CH, Watson, Matthew M, Bock, Jacob, Lidder, Surjit S, Elias, Patrick, Kanavathy, Sathisvaran, Koh, Cherry E, Chennakesavan, Srinivas Kondalsamy, Panuganti, Vishwakar, Latif, Haider, Yeung, Justin MC, Besson, Alex J, Tse, Eunice Q Y, Pitcher, Meron E, Taylor, Danielle L, Nahm, Christopher B, Lim, Alicia, Tree, Kevin, Aigner, Felix, Dawoud, Christopher, Foessleitner, Philipp, Zimmermann, Matthias, Wiedemann, Dominik, Findl, Oliver, Messner, Franka, Bauer, Marlies, Nägele, Felix, Kronberger, Irmgard E, Öfner, Dietmar, Härter, Bettina, Bicz, Nina Ru B, Zwittag, Paul M, Poier, Nikolaus, Navarro, Francisco Ruiz, Zebuhr, Yorck A, Köglberger, Paul, Wiesinger, Clemens G, Mathew, Erwin, Trivik-Barrientos, Felipe, Königsrainer, Ingmar, Djedovic, Gabriel, Cohnert, Tina U, Lumenta, David B, Singer, Georg, Leithner, Andreas, Kamolz, Lars-Peter, Andrianakis, Alexandros, Puchwein, Paul, Mikalauskas, Saulius, Kirchweger, Patrick, Függer, Reinhold, Mittermair, Christof, Russe, Elisabeth, Paal, Peter, Grünbart, Martin, de Cillia, Michael, Weiss, Helmut G, Steiner, Florian, Binder, Alf Dorian, Samadov, Elgun, Ibrahimli, Arturan, Muslumov, Gurbankhan, Bayramov, Nuru Y, Saunders, Jada M, Almoosa, Noora, Haj-Ibrahim, Huzifa, Maresch, Martin, Ezzdean, Weaam K, Juma, Isam M, Hasan, Layla H, Haider, Fayza HA, Alfaqawi, Ghassan Salman, Alam, Mohammed S, Islam, Shahnoor, Basher, AKM K, Mitul, Ashrarur Rahman, Islam, Nazmul, Oosterkamp, Antje E, Ahmed, Tanveer, Hannan, M Jafrul, Padmore, Greg M, Doyle, Alex F, LaCorbiniere, Karisha L, Boyce, Rico D R, Ragoobar, Paul T, Walkes, Keisha M Y, Haynes, Amelia A, Corbin, Sasha M, Litvina, Yauheniya A, Makhmudov, Anvar, Strypstein, Sébastien, Dhondt, Bert, Rasschaert, Ricky, Farid, Yasser, Wahib, El Mahdi, Pigeolet, Manon, Belle, Koen Van, De Wachter, Stefan, Komen, Niels, Vandeputte, Mathieu PJ, Jansen, Yanina JL, Stijns, Jasper, Eynde, Jef Van den, Olowo, Benedicte I, Feraudy, Israel C, Dragisic, Vedran, Martinovic, Vlatka, Barišić, Tatjana, Penava, Nikolina, Hudic, Igor, Delibegovic, Samir, Bogdanović, Gordana, Grgić, Gordana, Cerovac, Anis, Cerovac, Elmedina, Bedada, Alemayehu G, Baiocchi, Glauco, Wainstein, Alberto, Moisés, Elaine Christine D, Zani, Ana Carolina Tagliatti, de Campos Prado, Caio Antonio, Panis, Carolina, Rech, Daniel, Soares da Silva, Ruan Gabriel, Joviliano, Edwaldo E, Rezende, Ricardo F, Reis, Igor G N, Pires, Robinson E S, Christiano, Adriana Borgonovi, Consani, Heitor F X, Pugliesi, Felipe G, Takeda, Flavio R, Mariani, Alessandro W, Valadares, Ricardo J B, Andreollo, Nelson A, Lopes, Luiz Roberto, Tonello, Cristiano, Alonso, Nivaldo, dos Santos, Carlos Ferreira, Lima, Leonardo S, Salgado, Wilson, Pereira, Thiago H S, Gatti, Arthur Paredes, Oliva, Ramon N L, Nardi, Caroline N, Sousa, Alvaro F L, Ribeiro, Ivonizete P, Carvalho, Herica E F, Oliveira, Layze B, Schneider, Guilherme, Casteleins, William Augusto, Silva, Larissa M, Gomes, Carlos Augusto, da Cunha Viana Júnior, Alonço, Cruz, Ricardo P, Gomes, Gustavo MA, Buarque, Igor L, Barros, Aldo V, Marangon, Gustavo B, Flumignan, Ronald LG, Nakano, Luís CU, Pascoal, Patrícia IF, Santos, Brena C, Kuramoto, Danielle AB, Correia, Rebeca M, Amaral, Fabio CF, Flumignan, Carolina DQ, Dussán-Sarria, Jairo A, Simões, Romeo L, Amorim, Robson L, Silva, Jeancarllo S, Lyra Junior, Humberto F, Julio, Nathalia S, Gerber, Marlus T, dos Santos, José Mauro, de Oliveira, Joao Carlos C, Palamim, Camila VC, Marson, Fernando AL, Gomes, Iolanda M, Oliveira, Priscila R, Lima, Ana Lucia L M, Carvalho, Vladimir C, Silva, Jorge S, NA, Ulysses Ribeiro, Laporte, Gustavo Andreazza, Gonçalves, Mateus Capuzzo, Botacin, Lais S, Avelino, Melissa AG, Brito, Luiz Gustavo O, Hristova, Evguenia T, Stoyanov, Vladislav Valentinov, Sakakushev, Boris E, Dardanov, Dragomir D, Sokolov, Manol B, Mehta, Ananya, Gyokova, Elitsa H, Abdullahi, Mohamed, Karamanliev, Martin P, Dimitrov, Dobromir D, Slavchev, Mihail T, Atanasov, Boyko Ch, Yotsov, Tsanko I, Hadzhiev, Dimitar Bozhidarov, Stock, Simon E, Nwegbu, Chukwuemeka Gerald, Tanyi, Tanyi John, Brown, James A, Arneja, Jugpal S, Lee, Susan M, Kancherla, Ramya, Kidane, Biniam, Champagne, Pierre-Olivier, Ma, Xiya, Munro, Allana, McKeen, Dolores M, Glinka, Juan, Vasarhelyi, Edward M, Subramani, Yamini, Alfaro, Hilda, Shah, Ushma J, Gonzalez, Nelson J, MacNeil, S Danielle, Nagappa, Mahesh, Malthaner, 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Kandaswamy, Anyaugo, Ngozi S, Wuraola, Obafemi K, Odejinmi, Funlayo, Craven, Joanna M, West, Charlotte L, Barter, Reece A, Navaratnam, Devaraj M, Malik, Shahbaz S, Mahendran, Vimaladhithan, Zilvetti, Miguel, Lovegrove, Richard E, Board, Tim N, Zeiton, Moez, Sarwar, Safdar A, Duff, Sarah E, Hsabo, Elmuiz A, Nugur, Ashwani Kumar, Morris, Richard M, Lala, Anil K, Kwan-Feinberg, Rita O, Ross, Samuel W, Dressler, Jeremy A, Evans, Faye M, Wason, Shaun E L, Vogel, Keianna R, Wang, Joanna C, Sulciner, Megan L, Hirji, Sameer, Raoof, Mustafa, Wolff, Christopher J, kent, Ilan, Turan, Alparslan, Teixeira, Pedro G, Olson, Kristofor A, Patel, Neil D, Krishnamoorthy, Vijay, Moris, Dimitrios, Rice, Henry E, Gabr, Hesham, Satoskar, Savni R, Castater, Christine A, Jmaileh, Manal, Payne, Rachel E, Kwon, David S, Aarabi, Shahram, Escobar, Pedro F, Kronenfeld, Joshua P, Dalton, Michael K, Etchill, Eric W, Haut, Elliott R, Vervoort, Dominique, Dehal, Ahmed, McKenney, Mark, Elkbuli, Adel, Gosain, Ankush, Abdelsattar, Zaid M, Naunheim, Matthew R, Burks, Ciersten A, Zhou, Allen S, Heng, Marilyn, Abohashem, Shady, Lozano-Calderón, Santiago A, Reisenauer, Janani S, Mouawad, Nicolas J, Diehl, Thomas M, Eriksson, Evert A, Marwaha, Jayson S, Schroeppel, Thomas J, LaRocca, Christopher J, Chang, Grace H, Hassan, Romana, Nosanov, Lauren B, Rickard, Jennifer L, Avraham, Jacob B, Petrone, Patrizio, Sferra, Joseph J, Hadaya, Joseph, Mead, Brittany S, Hauptman, Jason S, Ahmad, Maleeha, Meola, Antonio, Chang, Steven D, Ko, Ara, Specht, Katherine E, Choudhry, Asad, Encalada, Ronald Zerna, Poggio, Juan L, Xing, Hang, Glass, Nina E, Hooker, Robert, Martins, Paulo N, Scott, Erin M, Bankhead, Brittany K, Giorgakis, Emmanouil, Nigh, Joe, Osborn, Tamara, Tay Lasso, Erika L, Beswick, Daniel M, Berndtson, Allison E, Kaups, Krista L, Chen, Lee-lynn, Farrell, Michael S, Boeck, Marissa A, Vaysburg, Dennis M, Kassam, Al-Faraaz, Turner, Kevin M, Dyas, Adam R, Coleman, Julia R, Folsom, Megan, Lam, Christopher M, Kumer, Sean C, Larson, Kelsey, Turner, Scott, Guidry, Christopher, Reddy, Madhuri, Berbel, German, Findley, Austin, Beahm, David, Bur, Andres, Marlor, Derek, Houndschell, Corey, Carver, Shea, Ulrich, Alissa, Bhutiani, Neal, DiChiacchio, Laura, Abdou, Hossam, Napolitano, Lena M, Ghebre, Rahel, Bass, Gary Alan, Kaplan, Lewis J, Martin, Niels D, Duffy, Caoimhe C, Abdelhamid, Sultan S, Daley, Brian J, Roland, Christina L, Dumas, Ryan P, Ban, Vin Shen, Rajesh, Aashish, Davies, Mark G, Purudappa, Prabhudev P, Walters, Camila B, Lin, Nicole, Ruzgar, Nensi M, Ullrich, Sarah J, Trostchansky, Ivan, Bonilla-Cal, Fernando, Castedo, Fabiola, Sobrero, Helena, Acuña, Gastón, Álvarez, Sofía M, Tarigo, Josefina, Carbajal, Ana C, Carbajal, Ana, Reyes, Antonio R, Al-Eryani, Fatima A, Alqousi, Nardeen N, Alattas, Zainab, Al-Saban, Rafat A, Al-Shehari, Mohammed M, ALHammadi, Nawal T, Shream, Sarah A, Al-Naggar, Hamza M, Al-Qalisi, Lina M, Nadeesh, Areej E, Al-Samawi, Hytham H, Bajjah, Hadeel M, AL-Ameri, Saba A, Albably, Jamal F, Ghannam, Rana A, Shamsan, Amatallah H, Meead, Abdullah A, Al- Zubaidi, Riham Q, Zulait, Mohammed A, Najeeb, Halah, Alsayadi, Musaed M, Al-Mashreqi, Seham K, Al-Jomai, Najla A, Alsayadi, Ramzi A, Al-Naggar, Moath M, Almarashi, Hassan A, Musaeed, Hasna M, Al-Raimi, Ibrahim M, Ghanem, Hossam N, Al-Zazay, Karim A, AL-Mahdi, Shehab A, Almontaser, Amatalaleem S, Savopoulos, Vanessa A S, Munthali, James, Kabongo, Kizito M C, Mushiwokufa, Willard, Ngulube, Allan, Ntoto, Crispin, Magama, Praise T, Dzinotyiwei, Daniel, Chivanga, Shelton K, Dube, Ngqabutho S, Sanchez, Ernesto C, Moyo, Assel T, Chengahomwe, Antony, Chinyowa, Simbarashe, Siamuchembu, Maphios, Bondera, Tafadzwa, and Mushawarima, Trust

Published
2022
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37. Dense Disparity Estimation in Ego-motion Reduced Search Space

Author

Fućek, Luka, Marković, Ivan, Cvišić, Igor, and Petrović, Ivan

Subjects
Computer Science - Robotics
Abstract

Depth estimation from stereo images remains a challenge even though studied for decades. The KITTI benchmark shows that the state-of-the-art solutions offer accurate depth estimation, but are still computationally complex and often require a GPU or FPGA implementation. In this paper we aim at increasing the accuracy of depth map estimation and reducing the computational complexity by using information from previous frames. We propose to transform the disparity map of the previous frame into the current frame, relying on the estimated ego-motion, and use this map as the prediction for the Kalman filter in the disparity space. Then, we update the predicted disparity map using the newly matched one. This way we reduce disparity search space and flickering between consecutive frames, thus increasing the computational efficiency of the algorithm. In the end, we validate the proposed approach on real-world data from the KITTI benchmark suite and show that the proposed algorithm yields more accurate results, while at the same time reducing the disparity search space.

Published
2017

38. Mixture Reduction on Matrix Lie Groups

Author

Cesic, Josip, Markovic, Ivan, and Petrovic, Ivan

Subjects
Computer Science - Systems and Control, Computer Science - Robotics
Abstract

Many physical systems evolve on matrix Lie groups and mixture filtering designed for such manifolds represent an inevitable tool for challenging estimation problems. However, mixture filtering faces the issue of a constantly growing number of components, hence require appropriate mixture reduction techniques. In this letter we propose a mixture reduction approach for distributions on matrix Lie groups, called the concentrated Gaussian distributions (CGDs). This entails appropriate reparametrization of CGD parameters to compute the KL divergence, pick and merge the mixture components. Furthermore, we also introduce a multitarget tracking filter on Lie groups as a mixture filtering study example for the proposed reduction method. In particular, we implemented the probability hypothesis density filter on matrix Lie groups. We validate the filter performance using the optimal subpattern assignment metric on a synthetic dataset consisting of 100 randomly generated multitarget scenarios., Comment: IEEE Signal Processing Letters

Published
2017
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39. On wrapping the Kalman filter and estimating with the SO(2) group

Author

Markovic, Ivan, Cesic, Josip, and Petrovic, Ivan

Subjects
Computer Science - Robotics
Abstract

This paper analyzes directional tracking in 2D with the extended Kalman filter on Lie groups (LG-EKF). The study stems from the problem of tracking objects moving in 2D Euclidean space, with the observer measuring direction only, thus rendering the measurement space and object position on the circle---a non-Euclidean geometry. The problem is further inconvenienced if we need to include higher-order dynamics in the state space, like angular velocity which is a Euclidean variables. The LG-EKF offers a solution to this issue by modeling the state space as a Lie group or combination thereof, e.g., SO(2) or its combinations with Rn. In the present paper, we first derive the LG-EKF on SO(2) and subsequently show that this derivation, based on the mathematically grounded framework of filtering on Lie groups, yields the same result as heuristically wrapping the angular variable within the EKF framework. This result applies only to the SO(2) and SO(2)xRn LG-EKFs and is not intended to be extended to other Lie groups or combinations thereof. In the end, we showcase the SO(2)xR2 LG-EKF, as an example of a constant angular acceleration model, on the problem of speaker tracking with a microphone array for which real-world experiments are conducted and accuracy is evaluated with ground truth data obtained by a motion capture system., Comment: 19th International Conference on Information Fusion (FUSION), Special Session on Directional Estimation

Published
2017

40. Moving object tracking employing rigid body motion on matrix Lie groups

Author

Cesic, Josip, Markovic, Ivan, and Petrovic, Ivan

Subjects
Computer Science - Robotics
Abstract

In this paper we propose a novel method for estimating rigid body motion by modeling the object state directly in the space of the rigid body motion group SE(2). It has been recently observed that a noisy manoeuvring object in SE(2) exhibits banana-shaped probability density contours in its pose. For this reason, we propose and investigate two state space models for moving object tracking: (i) a direct product SE(2)xR3 and (ii) a direct product of the two rigid body motion groups SE(2)xSE(2). The first term within these two state space constructions describes the current pose of the rigid body, while the second one employs its second order dynamics, i.e., the velocities. By this, we gain the flexibility of tracking omnidirectional motion in the vein of a constant velocity model, but also accounting for the dynamics in the rotation component. Since the SE(2) group is a matrix Lie group, we solve this problem by using the extended Kalman filter on matrix Lie groups and provide a detailed derivation of the proposed filters. We analyze the performance of the filters on a large number of synthetic trajectories and compare them with (i) the extended Kalman filter based constant velocity and turn rate model and (ii) the linear Kalman filter based constant velocity model. The results show that the proposed filters outperform the other two filters on a wide spectrum of types of motion., Comment: 19th International Conference on Information Fusion (FUSION), Special Session on Directional Estimation

Published
2017

43. NOSeqSLAM: Not only Sequential SLAM

Author

Maltar, Jurica, Marković, Ivan, Petrović, Ivan, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Silva, Manuel F., editor, Luís Lima, José, editor, Reis, Luís Paulo, editor, Sanfeliu, Alberto, editor, and Tardioli, Danilo, editor

Published
2020
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45. Prognostic and Clinical Significance of PD-L1, EGFR and Androgen Receptor (AR) Expression in Triple-Negative Breast Cancer (TNBC) Patients

Author

Medić-Milijić, Nataša, Jovanić, Irena, Nedeljković, Milica, Marković, Ivan, Spurnić, Igor, Milovanović, Zorka, Ademović, Nejla, Tomić, Tijana, Tanić, Nasta, Tanić, Nikola, Medić-Milijić, Nataša, Jovanić, Irena, Nedeljković, Milica, Marković, Ivan, Spurnić, Igor, Milovanović, Zorka, Ademović, Nejla, Tomić, Tijana, Tanić, Nasta, and Tanić, Nikola

Abstract

Simple Summary: Breast cancer is the most commonly occurring malignancy and the leading cause of cancer-related death in women. The most aggressive breast cancer subtype is triple-negative breast cancer (TNBC). It is associated with high recurrence rates, a high incidence of distant metastases and poor overall survival. The aim of this study was to investigate the role of PD-L1 (programmed death-ligand 1), EGFR (epidermal growth factor receptor) and androgen receptor (AR) expression in TNBC promotion and progression. To that end, we analyzed the immunoexpression of these genes in 125 postoperative samples of TNBC patients and correlated the obtained results with clinicopathological parameters and survival. According to the obtained results, we believe that a concurrent examination of PD-L1, AR, EGFR and Ki67 (in addition) protein expression may be more useful in predicting TNBC clinical course than the analysis of the individual expression of any of these proteins. Specifically, our results showed that a simultaneous low expression of PD-L1, low expression of AR and high expression of EGFR, followed by an elevated expression of Ki67, constitute a ‘high risk’ profile of TNBC. If we combine these results with our previous findings, which showed that reduced PTEN/high PI3K/high mTOR expression represents an unfavorable condition, we could have a promising formula for better prediction of TNBC outcome and a better starting point for new treatment modalities., Triple-negative breast cancer (TNBC) is the most aggressive breast cancer subtype and is associated with high recurrence rates, a high incidence of distant metastases and poor overall survival. The aim of this study was to investigate the role of PD-L1, EGFR and AR expression in TNBC promotion and progression. To that end, we analyzed the immunohistochemical expression of these genes in 125 TNBC patients and their relation to clinicopathological parameters and survival. An elevated expression of PD-L1 was significantly correlated with higher tumor and nuclear grade, while a low expression was correlated with loco-regional recurrence without any influence on survival. Contrary to this, the expression of AR showed a positive impact on the DFI and a negative association with tumor grade. Furthermore, PD-L1 and AR demonstrated simultaneous expression, and further co-expression analysis revealed that a positive expression of PD-L1/AR notably correlates with tumor and nuclear grade and has a significant impact on a longer DFI and OS, while a negative PD-L1/AR expression is significantly associated with metastases. Therefore, our results suggest that positive PD-L1/AR expression is beneficial for TNBC patients. In addition, an elevated expression of EGFR contributes to metastases and a worse DFI and OS. In conclusion, we think that low PD-L1/low AR/high EGFR expression followed by high Ki67 expression constitutes a ‘high risk’ profile of TNBC.

Published
2024

46. A review of the international early recommendations for departments organization and cancer management priorities during the global COVID-19 pandemic: applicability in low- and middle-income countries

Author

Belkacemi, Yazid, Grellier, Noemie, Ghith, Sahar, Debbi, Kamel, Coraggio, Gabriele, Bounedjar, Adda, Samlali, Redouane, Tsoutsou, Pauletta G., Ozsahin, Mahmut, Chauvet, Marie-Pierre, Turkan, Sedat, Boussen, Hamouda, Kuten, Abraham, Tesanovic, Dusanka, Errihani, Hassan, Benna, Farouk, Bouzid, Kamel, Idbaih, Ahmed, Mokhtari, Karima, Popovic, Lazar, Spano, Jean-Philippe, Lotz, Jean-Pierre, Cherif, Aziz, To, Hahn, Kovcin, Vladimir, Arsovski, Oliver, Beslija, Semir, Dzodic, Radan, Markovic, Ivan, Vasovic, Suzana, Stamatovic, Liljana, Radosavljevic, Davorin, Radulovic, Sinisa, Vrbanec, Damir, Sahraoui, Souha, Vasev, Nino, Stojkovski, Igor, Risteski, Milan, Freixa, Salvador Villà, Krengli, Marco, Radosevic, Nina, Mustacchi, Giorgio, Filipovic, Mladen, Kerrou, Khaldoun, Taghian, Alphonse G., Todorovic, Vladimir, Geara, Fady, and Gligorov, Joseph

Published
2020
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50. Architect Dušan Babić: Reconstruction of identity

Author

Marković Ivan R. and Milovanović Milan P.

Subjects
belgrade, dušan babić, modernist architecture, modern architects group (grupa arhitekata modernog pravca - gamp), Architecture, NA1-9428
Abstract

To this day, the Belgrade oeuvre of architect Dušan Babić has not been subjected to broader historiographic research, making the elements of his architectural vocabulary all the more difficult to define and evaluate. The available archival material does provide valuable insights into the life of architect Babić and his works in the capital of the Kingdom of Yugoslavia between 1927 and 1946. He produced over fifty designs for residential and mixed use buildings (comprising both commercial and residential space), several churches and the crematorium in Belgrade, simultaneously participating in at least ten big state competitions with notable entries, such as the designs for the Terazije slope, the Princes' Palace in the Dedinje Royal Compound, the Veterans Club building, etc. In the diversity of movements, styles and trends developing in Serbia between the two world wars as a result of the surge of predominantly West European cultural influences, the application and modification of modern architecture principles formed the mainstay of Babić's creative endevours. Stereometric forms, cubic volumes, unadorned facades, and strict lines were refined with often more freely interpreted bas-relief ornamentation, free standing sculptures or geometric forms. Babić made an effort to turn each and every structure into a unique architectural experience, especially by dynamically interpreted drawings of perspective and accentuated details.

Published
2020

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