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AI-Driven Relocation Tracking in Dynamic Kitchen Environments 14

AI-Driven Relocation Tracking in Dynamic Kitchen Environment...

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14th International Conference on computer and Knowledge Engineering, ICCKE 2024

作者： Esfahani, Arash Nasr Hosseini, Hamed Masouleh, Mehdi Tale Kalhor, Ahmad Sajedi, Hedieh Human and Robot Interaction Lab School of Electrical and Computer Engineering University of Tehran Tehran Iran School of Mathematics Statistics and Computer Science University of Tehran Tehran Iran

ISBN: (纸本)9798331511272

As smart homes become more prevalent in daily life, the ability to understand dynamic environments is essential which is increasingly dependent on AI systems. This study focuses on developing an intelligent algorithm which can navigate a robot through a kitchen, recognizing objects, and tracking their relocation. The kitchen was chosen as the testing ground due to its dynamic nature as objects are frequently moved, rearranged and replaced. Various techniques, such as SLAM feature-based tracking and deep learning-based object detection (e.g., Faster R-CNN), are commonly used for object tracking. Additionally, methods such as optical flow analysis and 3D reconstruction have also been used to track the relocation of objects. These approaches often face challenges when it comes to problems such as lighting variations and partial occlusions, where parts of the object are hidden in some frames but visible in others. The proposed method in this study leverages the YOLOv5 architecture, initialized with pre-trained weights and subsequently fine-tuned on a custom dataset. A novel method was developed, introducing a frame-scoring algorithm which calculates a score for each object based on its location and features within all frames. This scoring approach helps to identify changes by determining the best-associated frame for each object and comparing the results in each scene, overcoming limitations seen in other methods while maintaining simplicity in design. The experimental results demonstrate an accuracy of 97.72%, a precision of 95.83% and a recall of 96.84% for this algorithm, which highlights the efficacy of the model in detecting spatial changes. ©2024 IEEE.

关键词： Optical flows

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Dense 3D Gaussian Splatting Initialization for Sparse Image Data

Dense 3D Gaussian Splatting Initialization for Sparse Image ...

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45th Annual Conference on European Association for computer Graphics, EUROGRAPHICS 2024

作者： Seibt, S. Chang, T. von Rymon Lipinski, B. Latoschik, M.E. Game Tech Lab. Faculty of Computer Science Nuremberg Institute of Technology Nuremberg Germany Human-Computer Interaction Group Institute of Computer Science University of Wuerzburg Wuerzburg Germany

ISBN: (纸本)9783038682394

This paper presents advancements in novel-view synthesis with 3D Gaussian Splatting (3DGS) using a dense and accurate SfM point cloud initialization approach. We address the challenge of achieving photorealistic renderings from sparse image data, where basic 3DGS training may result in suboptimal convergence, thus leading to visual artifacts. The proposed method enhances precision and density of initially reconstructed point clouds by refining 3D positions and extrapolating additional points, even for difficult image regions, e.g. with repeating patterns and suboptimal visual coverage. Our contributions focus on improving "Dense Feature Matching for Structure-from-Motion" (DFM4SfM) based on a homographic decomposition of the image space to support 3DGS training: First, a grid-based feature detection method is introduced for DFM4SfM to ensure a well-distributed 3D Gaussian initialization uniformly over all depth planes. Second, the SfM feature matching is complemented by a geometric plausibility check, priming the homography estimation and thereby improving the initial placement of 3D Gaussians. Experimental results on the NeRF-LLFF dataset demonstrate that this approach achieves superior qualitative and quantitative results, even for fewer views, and the potential for a significantly accelerated 3DGS training with faster convergence. © 2024 The Authors.

关键词： Gaussian distribution

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DFM4SFM - Dense Feature Matching for Structure from Motion

DFM4SFM - Dense Feature Matching for Structure from Motion

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2023 IEEE International Conference on Image Processing Challenges and Workshops, ICIPCW 2023

作者： Seibt, Simon Von Rymon Lipinski, Bartosz Chang, Thomas Latoschik, Marc Erich Nuremberg Institute of Technology Game Tech Lab Faculty of Computer Science Nuremberg Germany Institute of Computer Science University of Wuerzburg Human-Computer Interaction Group Wuerzburg Germany

ISBN: (纸本)9798350302585

Structure from motion (SfM) is a fundamental task in computer vision and allows recovering the 3D structure of a stationary scene from an image set. Finding robust and accurate feature matches plays a crucial role in the early stages of SfM. So in this work, we propose a novel method for computing image correspondences based on dense feature matching (DFM) using homographic decomposition: The underlying pipeline provides refinement of existing matches through iterative rematching, detection of occlusions and extrapolation of additional matches in critical image areas between image pairs. Our main contributions are improvements of DFM specifically for SfM, resulting in global refinement and global extrapolation of image correspondences between related views. Furthermore, we propose an iterative version of the Delaunay-triangulation-based outlier detection algorithm for robust processing of repeated image patterns. Through experiments, we demonstrate that the proposed method significantlv improves the reconstruction accuracy. © 2023 IEEE.

关键词： computer vision

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A 2D Geometry Based Grasping Pose Generation Algorithm for a Two-Finger Robot Hand 31

A 2D Geometry Based Grasping Pose Generation Algorithm for a...

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31st International Conference on Electrical Engineering, ICEE 2023

作者： Akbari, Arash Akbari, Arman Masouleh, Mehdi Tale University of Tehran Department of Computer Science Tehran Iran School of Electrical and Computer Engineering Human and Robot Interaction Laboratory Tehran Iran

ISBN: (纸本)9798350312560

Robot grasping is of paramount importance in industrial and service robotics. In recent years, various data-driven algorithms have been proposed to solve the problem of grasp detection and a part of them are based on reinforcement learning (RL) approaches. In a variety of proposed algorithms, random key points are being employed which will make the learning process inefficient and time-consuming. In this paper, a geometry-based algorithm is presented which can find grasp poses based on the geometry of the unknown object and propose the ones which may lead to successful grasping. For the grasp contacts computation part, the presented algorithm produces a finite number of key points based on the 2D shape of the object from a specific point of view. Afterward, it will narrow down the candidate points and output a finite number of successful grasp poses based on three grasp quality metrics for various unknown objects. Three approaches are proposed in order to achieve center points which can describe different parts of a 2D shape. Then, the obtained points are used as the center of circles which are tangent to the 2D shape contour. Also, a new grasp quality metric is proposed. The time of the grasp and the amount of object disorientation after grasping are considered as a metric to evaluate the successfulness of the grasp. Simulation results demonstrate that the proposed algorithm for unknown object grasping can find a finite number of successful grasp poses for different seen or unseen objects without using any random point © 2023 IEEE.

关键词： Geometry

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Enhancing Immersive Sensemaking with Gaze-Driven Recommendation Cues 25

Enhancing Immersive Sensemaking with Gaze-Driven Recommendat...

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30th International Conference on Intelligent User Interfaces, IUI 2025

作者： Tahmid, Ibrahim Asadullah North, Chris Davidson, Kylie Whitley, Kirsten Bowman, Doug Computer Science Virginia Tech BlacksburgVA United States Department of Computer Science Virginia Tech BlacksburgVA United States Center for Human-Computer Interaction Virginia Tech BlacksburgVA United States Department of Defense College ParkMD United States

ISBN: (纸本)9798400713064

Sensemaking is a complex task that places a heavy cognitive demand on individuals. With the recent surge in data availability, making sense of vast amounts of information has become a significant challenge for many professionals, such as intelligence analysts. Immersive technologies such as mixed reality offer a potential solution by providing virtually unlimited space to organize data. However, the difficulty of processing, filtering relevant information, and synthesizing insights remains. We proposed using eye-tracking data from mixed reality head-worn displays to derive the analyst's perceived interest in documents and words, and convey that part of the mental model to the analyst. The global interest of the documents is reflected in their color, and their order on the list, while the local interest of the documents is used to generate focused recommendations for a document. To evaluate these recommendation cues, we conducted a user study with two conditions: a gaze-aware system, EyeST, and a "Freestyle"system without gaze-based visual cues. Our findings reveal that the EyeST helped analysts stay on track by reading more essential information while avoiding distractions. However, this came at the cost of reduced focused attention and perceived system performance. The results of our study highlight the need for explainable AI in human-AI collaborative sensemaking to build user trust and encourage the integration of AI outputs into the immersive sensemaking process. Based on our findings, we offer a set of guidelines for designing gaze-driven recommendation cues in an immersive environment. © 2025 Copyright held by the owner/author(s).

关键词： Information filtering

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Segmentation and Immersive Visualization of Brain Lesions Using Deep Learning and Virtual Reality 19

Segmentation and Immersive Visualization of Brain Lesions Us...

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19th ACM SIGGRAPH International Conference on Virtual-Reality Continuum and its Applications in Industry, VRCAI 2024

作者： Kelley, Brendan Plabst, Lucas Plabst, Lena Natural User Interaction Lab Colorado State University Fort CollinsCO United States Department of Human-Computer Interaction Julius Maximilian University of Würzburg Würzburg Germany

ISBN: (纸本)9798400713484

Magnetic resonance imaging (MRIs) are commonly used for diagnosing potential neurological disorders, however preparation and interpretation of MRI scans requires professional oversight. Additionally, MRIs are typically viewed as single cross sections of the affected regions which does not always capture the full picture of brain lesions and can be difficult to understand due to 2D's inherent abstraction of our 3D world. To address these challenges we propose a immersive visualization pipeline that combines deep learning image segmentation techniques using a VGG-16 model trained on MRI fluid attenuated inversion recovery (FLAIR) with virtual reality (VR) immersive analytics. Our visualization pipeline begins with our VGG-16 model predicting which regions of the brain are potentially affected by a disease. This output, along with the original scan, are then volumentrically rendered. These renders can then be viewed in VR using an head mounted display (HMD). Within the HMD users can move through the volumentric renderings to view the affected regions and utilize planes to view cross sections of the MRI scans. Our work provides a potential pipeline and tool for diagnosis and care. © 2024 ACM.

关键词： Image segmentation

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Korean Language NLP Model Based Emotional Analysis of LGBTQ Social Media Communities 17

Korean Language NLP Model Based Emotional Analysis of LGBTQ ...

引用

17th International Conference on Ubiquitous Information Management and Communication, IMCOM 2023

作者： Chi, Younghyun Kim, Jang Hyun Sun, Seungjong College of Computing and Informatics Sungkyunkwan University Department of Computer Science Suwon Korea Republic of Intelligence Interaction Sungkyunkwan University Department of Interaction Science Department of Human-Artificial Seoul Korea Republic of

ISBN: (纸本)9781665453486

Sexual minorities are increasingly gaining social visibility and legal rights guarantees at the constitutional level across much of the world, from South America, the United States, and Europe to Japan, Taiwan, and Thailand. At the same time, the COVID-19 pandemic has brought on significant mental health challenges for the public due to accompanying social and economic impact and measures, most of them adverse. Given pre-existing studies highlighting the minority demographic's vulnerability to depression and other mental health symptoms, and the increasing availability of accessible NLP tools, datasets, and models, this paper uses an emotional classification model to analyze emotional trends in queer communities on social media. Using KoBERT with a pre-labelled dataset containing some forty thousand scraped social media posts labelled with emotions, patterns of emotional expression on Twitter in the queer community is revealed. Resulting data provided a validation of the viability of this method of analyzing trends in negative and positive emotional expression as well as the impact COVID-19 had on online queer communities in early 2020 but revealed limitations. © 2023 IEEE.

关键词： Natural language processing systems

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Generating a General Culturing Microorganism Pattern Using a Delta Parallel Robot and Cam-in-Hand Calibration Method 11

Generating a General Culturing Microorganism Pattern Using a...

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

作者： Pasiar, Navid Mohammadi, Arvin Ahangar, Sajjad Khomami, Navid Asadi Masouleh, Mehdi Tale University of Tehran Human and Robot Interaction Lab. Department of Mechanical Eng. Tehran Iran University of Tehran Human and Robot Interaction Lab. Tehran Iran University of Tehran Human and Robot Interaction Lab. School of Electrical and Computer Eng. Tehran Iran

ISBN: (纸本)9798350308105

Robots can play a vital role in laboratory tasks, especially in culturing microorganisms. Currently, many of these operations are performed manually, which leads to biased and irreproducible results. This paper explores a calibration method for minimizing errors of a performing two certain laboratory tasks of swabbing and pipetting. This leads to enhanced accuracy and productivity. This research intends to culture microorganisms in a petri-dish in a specific pattern, measuring the coordinates of each object in the output image so the object location in the real world can be calculated. This research investigates the Cam-in-Hand method for a Delta Parallel Robot, meaning that the camera is mounted on the End-Effector. In this approach, a method of translating image coordinates into real-world coordinates is introduced. It is essential to provide an additional coordinate (in this case $z -$value) before the conversion, given that the camera outputs a 2D image. A vector-based image, in SVG format, as input to the algorithm generates a set of coordinates which determines the main points for swabbing or pipetting operation. Using this data, an array of coordinates is linearly interpolated in 3D space for the swabbing operation. Conversely, the trajectory generated for pipetting uses the 4-5-6-7 interpolating polynomial. The robot then adheres to following the interpolated array of coordinates as a function of time, using a PID controller. The pipetting device is built with a 3D printer using PLA materials. The calibration is done in different heights. The Camin-Hand method leads to ±1 cm precision as a result. Minimum, maximum, and mean errors are 0.092,1.61 and 0.76 respectively for the central point. © 2023 IEEE.

关键词： Calibration

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Virtual Displays for Knowledge Work: Extending or Replacing Physical Monitors for More Flexibility and Screen Space

Virtual Displays for Knowledge Work: Extending or Replacing ...

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Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), IEEE Conference on

作者： Leonardo Pavanatto Doug A. Bowman Department of Computer Science Center for Human-Computer Interaction Virginia Tech USA

ISBN: (数字)9798350374490

ISBN: (纸本)9798350374506

In this paper, we explore the potential of virtual displays in sup-porting knowledge work, focusing on scenarios where conventional physical monitors fall short. Our research investigates the feasibility and productivity costs of extending or replacing physical monitors with augmented and virtual reality displays. We present three com-pelling use cases, illustrating how virtual displays enhance flexibility, adaptability, and customization in remote and diverse work settings. Lessons learned from user studies highlight hardware and interface design challenges, emphasizing the need for larger resolution and field of view in head-worn displays (HWD). We conclude the paper with research opportunities and a call to address the evolving demands of knowledge work interfaces.

关键词： Productivity Three-dimensional displays Head-mounted displays Focusing Virtual reality User interfaces Hardware

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Planet Purifiers: A Collaborative Immersive Experience Proposing New Modifications to HOMER and Fishing Reel interaction Techniques

Planet Purifiers: A Collaborative Immersive Experience Propo...

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Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), IEEE Conference on

作者： Alexander Giovannelli Fionn Murphy Trey Davis Chaerin Lee Rehema Abulikemu Matthew Gallagher Sahil Sharma Lee Lisle Doug A. Bowman Department of Computer Science Center for Human-Computer Interaction Virginia Tech Blacksburg VA USA

ISBN: (数字)9798331514846

ISBN: (纸本)9798331525637

This paper presents our solution to the 2025 3DUI Contest challenge. We aimed to develop a collaborative, immersive experience that raises awareness about trash pollution in natural landscapes while enhancing traditional interaction techniques in virtual environments. To achieve these objectives, we created an engaging multiplayer game where one user collects harmful pollutants while the other user provides medication to impacted wildlife using enhancements to traditional interaction techniques: HOMER and Fishing Reel. We enhanced HOMER to use a cone volume to reduce the precise aiming required by a selection raycast to provide a more efficient means to collect pollutants at large distances, coined as FLOW-MATCH. To improve the animal feed distribution to wildlife far away from the user with Fishing Reel, we created RAWR-XD, an asymmetric bi-manual technique to more conveniently adjust the reeling speed using the non-selecting wrist rotation of the user.

关键词： Wrist Three-dimensional displays Pollution Planets Wildlife Collaboration Virtual environments Immersive experience Games Food products

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