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Multidimensional image morphing-fast image-based rendering of open 3D and VR environments

虚拟现实与智能硬件(中英文) 2025年第2期7卷 155-172页

作者： Simon SEIBT Bastian KUTH Bartosz von Rymon LIPINSKI Thomas CHANG Marc Erich LATOSCHIK Game Tech Laboratory Faculty of Computer ScienceNuremberg Institute of TechnologyNuremberg 90489Germany Department of Electrical Engineering and Computer Science Coburg University of Applied Sciences and ArtsCoburg 96450Germany Human-Computer Interaction Group Institute of Computer ScienceUniversity of WuerzburgWuerzburg 97074Germany

Background In recent years,the demand for interactive photorealistic three-dimensional(3D)environments has increased in various fields,including architecture,engineering,and ***,achieving a balance between the quality and efficiency of high-performance 3D applications and virtual reality(VR)remains *** This study addresses this issue by revisiting and extending view interpolation for image-based rendering(IBR),which enables the exploration of spacious open environments in 3D and ***,we introduce multimorphing,a novel rendering method based on the spatial data structure of 2D image patches,called the image *** this approach,novel views can be rendered with up to six degrees of freedom using only a sparse set of *** rendering process does not require 3D reconstruction of the geometry or per-pixel depth information,and all relevant data for the output are extracted from the local morphing cells of the image *** detection of parallax image regions during preprocessing reduces rendering artifacts by extrapolating image patches from adjacent cells in *** addition,a GPU-based solution was presented to resolve exposure inconsistencies within a dataset,enabling seamless transitions of brightness when moving between areas with varying light *** Experiments on multiple real-world and synthetic scenes demonstrate that the presented method achieves high"VR-compatible"frame rates,even on mid-range and legacy hardware,*** achieving adequate visual quality even for sparse datasets,it outperforms other IBR and current neural rendering *** Using the correspondence-based decomposition of input images into morphing cells of 2D image patches,multidimensional image morphing provides high-performance novel view generation,supporting open 3D and VR ***,the handling of morphing artifacts in the parallax image regions remains a topic for future resea

关键词： computer graphics 3D real-time rendering computer vision Image morphing Virtual reality

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Real-Time Imitation of human Head Motions, Blinks and Emotions by Nao robot: A Closed-Loop Approach 11

Real-Time Imitation of Human Head Motions, Blinks and Emotio...

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11th RSI International Conference on robotics and Mechatronics, ICRoM 2023

作者： Rayati, Keyhan Feizi, Amirhossein Beigy, Alireza Shahverdi, Pourya Masouleh, Mehdi Tale Kalhor, Ahmad Louie, Wing-Yue Geoffrey University of Tehran Human and Robot Interaction Lab Electrical and Computer Engineering Tehran Iran Shahrood University of Technology Department of Electrical Engineering Shahrood Iran Oakland University Intelligent Robotics Laboratory Michigan United States

ISBN: (纸本)9798350308105

This paper introduces a novel approach for enabling real-time imitation of human head motion by a Nao robot, with a primary focus on elevating human-robot interactions. By using the robust capabilities of the MediaPipe as a computer vision library and the DeepFace as an emotion recognition library, this research endeavors to capture the subtleties of human head motion, including blink actions and emotional expressions, and seamlessly incorporate these indicators into the robot's responses. The result is a comprehensive framework which facilitates precise head imitation within human-robot interactions, utilizing a closed-loop approach that involves gathering real-time feedback from the robot's imitation performance. This feedback loop ensures a high degree of accuracy in modeling head motion, as evidenced by an impressive R2 score of 96.3 for pitch and 98.9 for yaw. Notably, the proposed approach holds promise in improving communication for children with autism, offering them a valuable tool for more effective interaction. In essence, proposed work explores the integration of real-time head imitation and real-time emotion recognition to enhance human-robot interactions, with potential benefits for individuals with unique communication needs. © 2023 IEEE.

关键词： Emotion Recognition

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SIVD: Dataset of Iranian Vehicles for Real-Time Multi-Camera Video Tracking and Recognition 8

SIVD: Dataset of Iranian Vehicles for Real-Time Multi-Camera...

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8th International Iranian Conference on Signal Processing and Intelligent Systems, ICSPIS 2022

作者： Siahkali, Farbod Alavi, Seyed Amirmahdi Masouleh, Mehdi Tale University of Tehran Human and Robot Interaction Laboratory School of Electrical and Computer Engineering Tehran Iran Islamic Azad University Human and Robot Interaction Laboratory School of Computer Science Central Tehran Branch Tehran Iran

ISBN: (纸本)9781665476232

In this paper, a new publicly available 1 web-Scraped Iranian Vehicle Dataset (SIVD) for simultaneous real-time vehicle tracking and recognition is proposed. The datasets provided for Iranian cars in the literature have two fundamental problems. First, the lack of images from different angles, and second, the small number of classes compared to the dispersion of car models in the real world. Therefore, for the purposes of this paper, Iranian vehicle images from car sales websites are collected, and the SIVD dataset is proposed which contains 29 classes and 36,705 images. This paper aims at developing a classification network for Iranian vehicle recognition and implement a real-time tracking system using the YOLOv5 network to perform real-time vehicle model recognition and tracking tasks simultaneously. The ResNet50 achieved an accuracy of 99.29%, the highest among the investigated classification networks.1https://***/Farbod-Siahkali/Iranian-Vehicle-Tracking-and-Recognition © 2022 IEEE.

关键词： Model automobiles

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Experimental Study on Automatically Assembling Custom Catering Packages With a 3-DOF Delta robot Using Deep Learning Methods

Experimental Study on Automatically Assembling Custom Cateri...

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Iranian Conference on electrical engineering (ICEE)

作者： Reihaneh Yourdkhani Arash Tavoosian Navid Asadi Khomami Mehdi Tale Masouleh Electrical and Computer Engineering Department Human and Robot Interaction Laboratory University of Tehran Mechanical Engineering Department University of Tehran

ISBN: (数字)9798350376340

ISBN: (纸本)9798350376357

This paper introduces a pioneering experimental study on the automated packing of a catering package using a two-fingered gripper affixed to a 3-degree-of-freedom Delta parallel robot. A distinctive contribution lies in the application of a deep learning approach to tackle this challenge. A custom dataset, comprising 1,500 images, is meticulously curated for this endeavor, representing a noteworthy initiative as the first dataset focusing on Persian-manufactured products. The study employs the YOLOV5 model for object detection, followed by segmentation using the FastSAM model. Subsequently, rotation angle calculation is facilitated with segmentation masks, and a rotated rectangle encapsulating the object is generated. This rectangle forms the basis for calculating two grasp points using a novel geometrical approach involving eigenvectors. An extensive experimental study validates the proposed model, where all pertinent information is seamlessly transmitted to the 3-DOF Delta parallel robot. The proposed algorithm ensures real-time detection, calibration, and the fully autonomous packing process of a catering package, boasting an impressive over 80% success rate in automatic grasping. This study marks a significant stride in advancing the capabilities of robotic systems for practical applications in packaging automation.

关键词： Deep learning YOLO Parallel robots Image segmentation 3-DOF Focusing Grasping

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Mapping human Grasping to 3-Finger Grippers: A Deep Learning Perspective

Mapping Human Grasping to 3-Finger Grippers: A Deep Learning...

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Iranian Conference on electrical engineering (ICEE)

作者： Fatemeh Naeinian Elnaz Balazadeh Mehdi Tale Masouleh Human and Robot Interaction Laboratory School of Electrical and Computer Engineering University of Tehran Tehran Iran Human and Robot Interaction Laboratory University of Tehran Tehran Iran

ISBN: (数字)9798350376340

ISBN: (纸本)9798350376357

Grasping is a fundamental aspect of human interaction with the environment, and replicating this ability in robotic systems is challenging due to the complexity of hand-object interactions. This paper explores the mapping of human grasping behavior to a 3-finger robotic gripper using a deep learning approach. The study focuses on the development of a model that predicts grasping points based on RGB images of objects and finger coordinates during the approaching state. The research contributes a novel dataset of human-like grasping with 3 fingers, capturing various object orientations and scenarios. Finger coordinates are extracted in both the approaching and grasping states by using Mediapipe. The proposed model employs a pretrained Faster R-CNN for object detection, coupled with fully connected networks for regression of grasping points. Evaluation metrics include Intersection over Union (IoU), grasp angle, and a proposed metric, Grasp Configurational Accuracy (GCA). The trained network achieves compelling results, demonstrating 88.2% GCA, 96.5% in grasp angle, and a 45.7% IoU in grasp rectangle representation. Results indicate the efficacy of the approach, offering a promising avenue for advancing robotic grasping capabilities.

关键词： Measurement Deep learning Three-dimensional displays robot kinematics Pipelines Grasping Predictive models

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JoTyMo: Joint Type and Movement Detection from RGB Images of Before and After Interacting with Articulated Objects

JoTyMo: Joint Type and Movement Detection from RGB Images of...

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RSI/ISM International Conference on robotics and Mechatronics (ICROM)

作者： Ehsan Forootan Hamed Ghasemi Mehdi Tale Masouleh Ahmad Kalhor Behzad Moshiri Human and Robot Interaction Laboratory Electrical and Computer Engineering Department University of Tehran

This paper presents a CNN-based network architecture aimed to classify and detect joint types within Articulated objects, specifically the Push-P joints, P-joints, R-joints and objects lacking joints. The movement modeling of the detected objects is explored by processing pre and post interaction RGB images within the SAPIEN PartNet-Mobility dataset. In order to achieve this, an architecture is proposed to leverage consecutive CNN encoders based on the VGG architecture, in order to classify joints based on pre and post-interaction images. Additionally, in order to detect the effect-point and movement vector, a convolutional encoder is applied for each joint type. Moreover, extensive evaluation is made to showcase the results, achieving 96% accuracy in joint classification and 94% accuracy in regression on the considered dataset. Elaborate details regarding network architectures, training procedures, and testing methodologies are available at source code repository.

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Dynamic Modeling, Parameter Identification and Nonlinear Model Predictive Control of a Cable-Suspended Parallel robot with Four Cables 8

Dynamic Modeling, Parameter Identification and Nonlinear Mod...

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8th International Conference on Control, Instrumentation and Automation, ICCIA 2022

作者： Ghanatian, Mohammad Hairi Yazdi, Mohammad Reza Masouleh, Mehdi Tale Human Robot Interaction Laboratory School of Mechanical Engineering University of Tehran Tehran Iran School of Mechanical Engineering University of Tehran Tehran Iran Human Robot Interaction Laboratory School of Electrical and Computer Engineering University of Tehran Tehran Iran

ISBN: (纸本)9781665495691

In this research, a nonlinear model predictive controller is designed for the trajectory tracking of spatial cable-suspended parallel robots with four cables. The dynamic model of this robot is derived firstly using the Newton-Euler approach and is subsequently verified using the Simscape environment. Moreover, an identification method based on the Genetic Algorithm is presented to identify the unknown inertial and frictional parameters in the model. It is observed that the Interior-Point optimization method is able to solve the optimization problem resulting from the proposed model predictive controller in real-time. Also, the effectiveness of the designed controller, combined with the presented identification approach, is investigated by assessing the controller performance when the estimated values are used in the model, compared to the cases where the precise values are utilized. Three groups of various inertial and frictional parameter values are considered in these assessments, and it is observed that when the estimated values are utilized in the model, the control of the robot is performed with an average RMSE of 1.1073mm, which is only 0.75716mm more than the average RMSE when the precise values are utilized. The results obtained from the simulations reveal the promising performance of the proposed model predictive controller when combined with the suggested identification method. © 2022 IEEE.

关键词： Genetic algorithms

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Experimental Study on Autonomous Food Packaging with Delta Parallel robot and Two Fingered Gripper

Experimental Study on Autonomous Food Packaging with Delta P...

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Iranian Conference on electrical engineering (ICEE)

作者： Mona Mohades Mojtahedi Arvin Mohammadi Mehdi Tale Masouleh Human and Robot Interaction Laboratory School of Electrical and Computer Engineering University of Tehran Tehran Iran Human and Robot Interaction Laboratory School of Mechanical Engineering University of Tehran Tehran Iran

ISBN: (数字)9798350376340

ISBN: (纸本)9798350376357

In response to the increasing supply and demand in the food industry, automated food packaging has emerged as a substitute for the manual labor that was previously required. The surge in automation is evident, with robotics playing a pivotal role in tasks ranging from sorting and packaging to labeling, and notably, pick-and-place operations which is the specific focus of this study. Leveraging robots for these tasks not only improves precision and speed but also takes packaging hygiene into account, resulting in a substantial cost reduction and heightened efficiency. Delta Parallel robots are among the robotic systems well-suited for high-speed pick-and-place tasks. In this experimental study, the focus is on packaging several scattered chocolates, where both the container box and the chocolates will be randomly positioned and oriented. This is achieved by employing a Delta Parallel robot paired with a two-fingered gripper, designed to incur minimal damage to the product when compared to other types of end-effectors. The positioning of the box and products will be determined through a classical image processing approach, specifically Edge Detection and Hough Transform, implemented using the OpenCV Python library. Following identification, the objects are localized through camera-robot calibration, and the resulting coordinates are transmitted to the robot as target points. Extensive testing has demonstrated that this study, employing visual perception for pick-and-place tasks, achieved a success rate of 82%. This study showcases the potential of automating packaging tasks in the food industry through the integration of robotic systems and artificial intelligence. The approach proves effective across diverse tasks, encompassing the packaging of variously shaped food items and drugs, applicable across different industries.

关键词： Parallel robots Accuracy Service robots Shape Image color analysis robot kinematics Food industry

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Deep Learning-Based Imitation of human Actions for Autonomous Pick-and-Place Tasks

Deep Learning-Based Imitation of Human Actions for Autonomou...

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Iranian Conference on electrical engineering (ICEE)

作者： Anushe Saadati Mehdi Tale Masouleh Ahmad Kalhor Human and Robot Interaction Laboratory School of Electrical and Computer Engineering University of Tehran Tehran Iran

ISBN: (数字)9798350376340

ISBN: (纸本)9798350376357

In robotics, attempts have been made to imitate movements through learning from demonstrations. There are two types of demonstration-based learning. Expert-based learning is expensive and time-consuming since it necessitates a paired observation-action procedure. Conversely, visual-based learning is more practical to collect and makes robots more adaptable to many kinds of tasks. But this problem still needs to be answered completely, and it's still a difficult assignment. This paper presents a comprehensive approach to replicating human actions in robots for object manipulation. As the main contribution of this paper, This research employs a deep learning methodology to allow robots to imitate human actions in grasping objects, utilizing human movement as a reference. A real dataset with 718 images and 142 videos has been collected in this paper. then The model is trained for object detection and object segmentation. To imitate human actions, a video of a person completing a task is recorded first. Following the subject's movements in the video allows for the extraction of the motion path. The robot then proceeds along the path that was extracted. The DPR impressively demonstrates the practical application, showcasing its capacity to imitate and execute human actions.

关键词： Deep learning Accuracy Tracking Video sequences Object detection Grasping Real-time systems

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Developing a Reinforcement Learning-Based Actor-Critic Neural Network Controller for General Form of Serial Chain robots with Torque Constraints

SSRN

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SSRN 2024年

作者： Bahrami, Vahid Masouleh, Mehdi Tale Kalhor, Ahmad Human and Robot Interaction Laboratory School of Electrical and Computer Engineering University of Tehran Tehran Iran

This paper uses reinforcement learning techniques to introduce a novel approach to the controller design for a wide range of serial chain robots and cable-driven serial chain robots. The method addresses the regulation problem by designing a regulator and steady-state torques without requiring knowledge of the Coriolis vector, a complex part of the robot's dynamic model. The approach relies on Denavit-Hartenberg parameters to determine regulator and desired steady-state torques, using an actor-critic neural network for adaptation and a multi-layer perceptron neural network for desired steady-state torque. Stability is ensured through Lyapunov functions, with only the mass matrix and gravity vector required. The proposed control scheme offers adaptability, robustness, scalability, and optimization features compared to traditional controllers. Simulations on a 4-degrees-of-freedom cable-driven serial chain robot validate the proposed control method's effectiveness. A comparative benchmark is performed, demonstrating that the reinforcement learning controller outperforms other controllers in terms of regulation error, reducing it by at least 17.14\% compared to deep deterministic policy gradient and robust integral of the sign of the error methods. © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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