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18th Chinese Conference on Biometric Recognition, CCBR 2024

作者： Ma, Shuai He, Jinrong Wang, Yao Bi, Yingzhou Yang, Li College of Mathematics and Computer Science Yan’an University Yan’an716000 China Yan’an University Affiliated Hospital Yan’an716000 China Guangxi Key Lab of Human-Machine Interaction and Intelligent Decision Nanning Normal University Nanning530100 China School Hospital Yan’an University Yan’an716000 China

ISBN: (纸本)9789819610709

Early diagnosis of pediatric adenoidal hypertrophy is crucial for timely identification of respiratory obstructions, thereby preventing declines in sleep quality that can lead to impaired growth, development, and cognitive function. Currently, the most common clinical diagnostic methods involve nasopharyngeal endoscopy and X-ray imaging assessments, which are time-consuming, labor-intensive, and costly. This paper presents an intelligent diagnostic model based on natural light facial images, employing a state space model (SSM). The model combines the local feature extraction capability of convolutional layers with the SSM’s ability to capture long-range dependencies for facial feature extraction. Furthermore, it incorporates a feature fusion strategy to enhance the features of facial images. Experimentation on a self-constructed dataset of adenoid facies images demonstrated that the model achieves an accuracy of 86.55% and an F1-score of 86.21%. The data and code are available at https://***/hejinrong/AF-SSM. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Medical imaging

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Open Game Data: Defining a Pipeline and Standards for Educational Data Mining and Learning Analytics with Video Game Data

Open Game Data: Defining a Pipeline and Standards for Educat...

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IEEE Symposium on Computational Intelligence and Games, CIG

作者： David J. Gagnon Luke Swanson Erik Harpstead Field Day Lab University of Wisconsin - Madison Madison USA Human-Computer Interaction Institute Carnegie Mellon University Pittsburgh USA

ISBN: (数字)9798350350678

ISBN: (纸本)9798350350685

In this paper we describe the need for a framework to support collaborative educational research with game data, then demonstrate a promising solution. We review existing efforts, explore a collection of use cases and requirements, then propose a new data architecture with related data standards. The approach provides modularity to the various stages of game data generation and analysis, exposing intermediate transformations and work products. Foregrounding flexibility, each stage of the pipeline generates datasets for use in other tools and workflows. A series of interconnected standards allow for the development of reusable analysis and visualization tools across games, while remaining responsive to the diversity of potential game designs. Finally, we demonstrate the feasibility of the approach through an existing implementation that uses this architecture to process and analyze data from a wide range of games developed by multiple institutions, at scale, supporting a variety of research projects.

关键词： Video games Reviews Pipelines Data visualization Collaboration Games Data mining

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RUBBING A PHYSICS BASED SYNTHESIS MODEL: FROM MOUSE CONTROL TO FRICTIONAL HAPTIC FEEDBACK 19

RUBBING A PHYSICS BASED SYNTHESIS MODEL: FROM MOUSE CONTROL ...

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19th Sound and Music Computing Conference, SMC 2022

作者： Onofrei, Marius George Fontana, Federico Serafin, Stefania Dept. of Design & Media Technology Aalborg University Copenhagen Denmark Human Computer Interaction Lab University of Udine Udine Italy Multisensory Experience Lab Aalborg University Copenhagen Denmark

ISBN: (纸本)9782958412609

This paper investigates three kinds of interactions for a friction based virtual music instrument. The sound synthesis model consists of a bank of mass-spring-dampers individually excited via rubbing. A nonlinear static friction model capable of reproducing the characteristic stick-slip phenomenon observed in frictional interaction is employed, allowing for dynamic variation of the sliding friction. The different controls developed allow for gradually increasing the interplay between performer and instrument. The key excitation parameters, e.g., the rubbing velocity and the rubbing normal force are controlled using three different interfaces: a standard mouse, a Sensel Morph, and a 3D Systems Touch X. The Sensel Morph is a touchpad with pressure sensitivity, allowing for a natural exertion of the normal force;the 3D Systems Touch X is a haptic device that renders both resistance to the applied normal force, as well as the stick-slip motion resulting from the friction interaction. A preliminary user study aiming to compare the experience of performing with the different interfaces was carried out. The results indicate that the haptic feedback provides a more intuitive and enjoyable experience. However, extra features do not necessarily improve the user interaction, as the results suggest a preference for the mouse over the Sensel. Copyright: © 2022 Marius George Onofrei et al.

关键词： Stick-slip

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DFM4SFM - Dense Feature Matching for Structure from Motion

DFM4SFM - Dense Feature Matching for Structure from Motion

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Image Processing Challenges (ICIPC), IEEE International Conference on

作者： Simon Seibt Bartosz Von Rymon Lipinski Thomas Chang Marc Erich Latoschik 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

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.

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Lightweight Method for Plant Disease Identification Using Deep Learning

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Intelligent Automation & Soft Computing 2023年第7期37卷 525-544页

作者： Jianbo Lu Ruxin Shi Jin Tong Wenqi Cheng Xiaoya Ma Xiaobin Liu Guangxi Key Lab of Human-machine Interaction and Intelligent Decision Nanning Normal UniversityNanning530001China School of Computer and Information Engineering Nanning Normal UniversityNanning530001China School of Logistics Management and Engineering Nanning Normal UniversityNanning530001China Hangzhou Hikvision Digital Technology Co.Ltd. Hangzhou310052China

In the deep learning approach for identifying plant diseases,the high complexity of the network model,the large number of parameters,and great computational effort make it challenging to deploy the model on terminal devices with limited computational *** this study,a lightweight method for plant diseases identification that is an improved version of the ShuffleNetV2 model is *** the proposed model,the depthwise convolution in the basic module of ShuffleNetV2 is replaced with mixed depthwise convolution to capture crop pest images with different resolutions;the efficient channel attention module is added into the ShuffleNetV2 model network structure to enhance the channel features;and the ReLU activation function is replaced with the ReLU6 activation function to prevent the gen-eration of large *** are conducted on the public dataset *** results show that the proposed model achieves an accuracy of 99.43%,which is an improvement of 0.6 percentage points compared to the ShuffleNetV2 *** to lightweight network models,such as MobileNetV2,MobileNetV3,EfficientNet,and EfficientNetV2,and classical convolutional neural network models,such as ResNet34,ResNet50,and ResNet101,the proposed model has fewer parameters and higher recognition accuracy,which provides guidance for deploying crop pest identification methods on resource-constrained devices,including mobile terminals.

关键词： Plant disease identification mixed depthwise convolution lightweight ShuffleNetV2 attention mechanism

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Relectrode: A reconfigurable electrode for multi-purpose sensing based on microfluidics 21

Relectrode: A reconfigurable electrode for multi-purpose sen...

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2021 CHI Conference on human Factors in Computing Systems: Making Waves, Combining Strengths, CHI 2021

作者： Sun, Wei Chen, Yanjun Zhan, Simon Han, Teng State Key Laboratory of Computer Science Beijing Key Lab of Human-Computer Interaction Institute of SoftwareChinese Academy of Sciences Beijing China

ISBN: (纸本)9781450380966

In this paper, we propose a reconfgurable electrode, RElectrode, using a microfuidic technique that can change the geometry and material properties of the electrode to satisfy the needs for sensing a variety of diferent types of user input through touch/touchless gestures, pressure, temperature, and distinguish between diferent types of objects or liquids. Unlike the existing approaches, which depend on the specifc-shaped electrode for particular sensing (e.g., coil for inductive sensing), RElectrode enables capacity, inductance, resistance/pressure, temperature, pH sensings all in a single package. We demonstrate the design and fabrication of the microfuidic structure of our RElectrode, evaluate its sensing performance through several studies, and provide some unique applications. RElectrode demonstrates technical feasibility and application values of integrating physical and biochemical properties of microfuidics into novel sensing interfaces. © 2021 ACM.

关键词： Electrodes

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Robotic Sorting of Mechanical and Electrical Parts: An Autonomous vision-Based Approach in a Practical Case Study

Robotic Sorting of Mechanical and Electrical Parts: An Auton...

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International Conference on Control, Instrumentation, and Automation

作者： Mohammad Yousefi Mahdi Dalvand Navid Asadi Khomami Mehdi Tale Masouleh Mohammad Reza Haeri Yazdi Human and Robot Interaction Lab. School of Mechanical Engineering University of Tehran Tehran Iran Human and Robot Interaction Lab. School of Electrical and Computer Engineering University of Tehran Tehran Iran School of Mechanical Engineering University of Tehran Tehran Iran

ISBN: (数字)9798331509972

ISBN: (纸本)9798331509989

In today’s industrial landscape, automation has become increasingly vital, particularly in the deployment of robots for tasks such as sorting machine components. The use of robotic systems enhances process accuracy and speed, resulting in significant cost reductions and improved productivity compared to manual labor. This paper aims to design and develop an automated sorting system for various types of mechanical and electrical parts, utilizing image processing and machine vision algorithms with a Delta parallel robot equipped with a two-finger gripper. The target mechanical and electrical parts in this study are screws, nuts, metal washers, rubber washers, retaining rings, rectangular keys, wall plugs, resistors, potentiometers, capacitors, batteries, ICs, and LEDs. The YOLOv3 algorithm and Adaptive Thresholding method are employed to detect and distinguish objects from the background, and size measurement is achieved with the help of a custom marker with known dimensions. In this study, transfer learning based on pre-trained weights of YOLOv3 for the COCO dataset is applied. The proposed system attains a final mean Average Precision (mAP@0.5) value exceeding 0.95 for part detection using YOLOv3. Additionally, it demonstrates an overall pick-and-place success rate exceeding $\mathbf{9 0 \%}$.

关键词： YOLO Parallel robots Automation Thresholding (Imaging) Service robots Robot kinematics Transfer learning Size measurement Grippers Sorting

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Learning Robust Representations with Information Bottleneck and Memory Network for RGB-D-based Gesture Recognition

Learning Robust Representations with Information Bottleneck ...

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International Conference on computer vision (ICCV)

作者： Yunan Li Huizhou Chen Guanwen Feng Qiguang Miao School of Computer Science and Technology Xidian University China Xi’an Key Laboratory of Big Data and Intelligent Vision China Key Laboratory of Smart Human-Computer Interaction and Wearable Technology of Shaanxi Province China

Although previous RGB-D-based gesture recognition methods have shown promising performance, researchers often overlook the interference of task-irrelevant cues like illumination and background. These unnecessary factors are learned together with the predictive ones by the network and hinder accurate recognition. In this paper, we propose a convenient and analytical framework to learn a robust feature representation that is impervious to gesture-irrelevant factors. Based on the Information Bottleneck theory, two rules of Sufficiency and Compactness are derived to develop a new information-theoretic loss function, which cultivates a more sufficient and compact representation from the feature encoding and mitigates the impact of gesture-irrelevant information. To highlight the predictive information, we further integrate a memory network. Using our proposed content-based and contextual memory addressing scheme, we weaken the nuisances while preserving the task-relevant information, providing guidance for refining the feature representation. Experiments conducted on three public datasets demonstrate that our approach leads to a better feature representation and achieves better performance than state-of-the-art methods. The code of our method is available at: https://***/Carpumpkin/InBoMem.

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The Interactive Network Visualization of the interactions Between Topologically Associating Domains in the Genome of Fruit Fly

The Interactive Network Visualization of the Interactions Be...

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17th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2024

作者： Mali, Samira Annavarapu, Swetha Laboratory for Theoretical and Computational Molecular Biophysics Virginia Tech Blacksburg VA United States The Distributed Virtual Environments Lab Center for Human-Computer Interaction Virginia Tech BlacksburgVA United States 3ADP Inc AlpharettaGA United States

In this work, we created a network visualization to help you understand how Topologically Associating Do mains (TADs) interact with each other across the genome based on where the TAD is located, whether it is near the center of the nucleus or near the edge of the nucleus. This visualization can reveal how the dense regions and sparse regions of chromosome interactions are distributed in one view. The pilot study demonstrates how network visualization of TAD-TAD interactions can quickly answer numerous major questions in 3D genome and epigenetics field without requiring the development of Machine Learning methods or Algorithms to un lock Heatmap structures. The questions include but are not limited to, determining many-way interactions and interactions between TADs belonging to various epigenetic classes. © 2024 by SCITEPRESS–Science and Technology Publications, Lda.

关键词： Visualization

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

Generating a General Culturing Microorganism Pattern Using a...

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

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

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.

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