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A YOLO-based Method for Object Contour Detection and Recognition in Video Sequences

A YOLO-based Method for Object Contour Detection and Recogni...

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2024 workshop Cybersecurity Providing in Information and Telecommunication Systems, CPITS 2024

作者： Nazarkevych, Mariia Kostiak, Maryna Oleksiv, Nazar Vysotska, Victoria Shvahuliak, Andrii-Taras Lviv Polytechnic National University 12 Stepan Bandera str. Lviv79013 Ukraine Lviv Ivan Franko National University 1 Universytetska str. Lviv79000 Ukraine

A method for recognizing the contours of objects in a video data stream is proposed. The data will be uploaded using the video camera. Objects will be recognized in real-time. We will use YOLO—a method of identification and recognition of objects in real-time. Recognized objects will be recorded in a video sequence showing the contours of the objects. The approach proposed in the project reasonably synthesizes methods of artificial intelligence, theories of computer vision on the one hand, and pattern recognition on the other;it makes it possible to obtain control influences and mathematical functions for decision-making at every moment with the possibility of analyzing the influence of external factors and forecasting the flow of processes and refers to the fundamental problems of mathematical modeling of real processes. The installation of the neural network is shown in detail. The characteristics of the neural network and its capabilities are shown. Approaches to computer vision for object extraction are shown. Well-known methods are methods of expanding areas, methods based on clustering, contour selection, and methods using a histogram. The work envisages building a system for rapid identification of combat vehicles based on the latest image filtering methods developed using deep learning methods. The time spent on machine identification will be 10–20% shorter, thanks to the developed new information technology for detecting objects in conditions of rapidly changing information. © 2024 Copyright for this paper by its authors.

关键词： Artificial intelligence image recognition segmentation selection of objects tracking YOLO

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2nd International workshop on Value Engineering in Artificial Intelligence, VALE 2024

2nd International Workshop on Value Engineering in Artificia...

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2nd International workshop on Value Engineering in Artificial Intelligence, VALE 2024

ISBN: (纸本)9783031854620

The proceedings contain 16 papers. The special focus in this conference is on Value Engineering in Artificial Intelligence. The topics include: Value Learning for Value-Aligned Route Choice modeling via Inverse Reinforcement Learning;adversarial Behaviour in Moral Value Expression: A Statistical and Frame-Semantic Analysis of Social Media;vision Language Models as Values Detectors;leveraging Large Language Models to Identify the Values Behind Arguments;value Promotion Scheme Elicitation Using Natural Language Processing: A Model for Value-based Agent Architecture;neuro-Symbolic Classification of Basic Human Values;value-based Decision-Making in Software Agents: A Systematic Literature Review;modelling Moral Decision-Making in a Contractualist Artificial Agent;satisfaction in Negotiation by Structured Values and Propositions;towards the Incorporation of Social Values in Automated Negotiation Strategies;context Matters: Contextual Value-based Deliberation in Water Consumption Scenarios;Opportunities and Challenges of the Use of Forgetting in Symbolic XAI;Values-Aligned, Responsible Sharing (VaRS): A Methodology and a Blueprint;From the Pascal Wager to Value Engineering: A Glance at AI Risks and How to Address Them.

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Progressive Optimization of Deep Learning-based Fight Detection Model

Progressive Optimization of Deep Learning-Based Fight Detect...

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2024 IEEE International workshop on Technologies for Defense and Security, TechDefense 2024

作者： Mukhamediya, Azamat Zhumabek, Tilek Nazarbayev University School of Engineering and Digital Sciences Department of Electrical and Computer Engineering Astana010000 Kazakhstan Nazarbayev University School of Sciences and Humanities Department of Physics Astana010000 Kazakhstan

ISBN: (纸本)9798331505585

Building a fight detection system is essential to enhance public safety and develop automated security surveillance systems. Given the success of deep learning-based models in computer vision and pattern recognition, it is common to use deep convolutional and recurrent neural networks to accurately predict fight scenes. However, the required computational resources hinder the use of these models in memory-limited devices. In this work, we aim to obtain the fight detection model that satisfies both memory constraints and accurate classification performance. First, we design a complex model and then reduce its complexity by leveraging a three-stage progressive model optimization strategy. As a result, we obtained accurate detection models, which are ∼ 2 and ∼ 5 times lighter compared to the starting complex model. The experiments are performed using two publicly available datasets, namely Hockey and Movie Fights. © 2024 IEEE.

关键词： Convolutional neural networks

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Eighth International workshop on Advanced Patterning Solutions, IWAPS 2024

Eighth International Workshop on Advanced Patterning Solutio...

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8th International workshop on Advanced Patterning Solutions, IWAPS 2024

ISBN: (纸本)9781510686328

The proceedings contain 60 papers. The topics discussed include: from 1D-spot to 2D-plain: a computer vision based comprehensive approach for process window qualification;source-mask co-optimization study for typical EUV design rule patterns with 40 nm minimum pitch;edge placement error analysis through backscattered electron imaging;an effective methodology of filter, measure and align SEM image in model calibration;SEM contour extraction application on OPC model of contact layer;inverse lithography based on a physics-driven deep learning approach;achieving high-accuracy and noise-robust process window analysis through stepwise regression;and fast lithographic source optimization adopting RMSProp with iterative shrinkage-thresholding algorithm compressive sensing for high fidelity patterning.

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DROP: Dynamics Responses from Human Motion Prior and Projective Dynamics 23

DROP: Dynamics Responses from Human Motion Prior and Project...

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SIGGRAPH Asia Conference on Technical Communications (SA Technical Communications)

作者： Jiang, Yifeng Won, Jungdam Ye, Yuting Liu, C. Karen Stanford Univ Stanford CA 94305 USA Seoul Natl Univ Seoul South Korea Meta Real Labs Res Menlo Pk CA USA

ISBN: (纸本)9798400703157

Synthesizing realistic human movements, dynamically responsive to the environment, is a long-standing objective in character animation, with applications in computer vision, sports, and healthcare, for motion prediction and data augmentation. Recent kinematicsbased generative motion models offer impressive scalability in modeling extensive motion data, albeit without an interface to reason about and interact with physics. While simulator-in-the-loop learning approaches enable highly physically realistic behaviors, the challenges in training often affect scalability and adoption. We introduce DROP, a novel framework for modeling Dynamics Responses of humans using generative mOtion prior and Projective dynamics. DROP can be viewed as a highly stable, minimalist physics-based human simulator that interfaces with a kinematics-based generative motion prior. Utilizing projective dynamics, DROP allows flexible and simple integration of the learned motion prior as one of the projective energies, seamlessly incorporating control provided by the motion prior with Newtonian dynamics. Serving as a modelagnostic plug-in, DROP enables us to fully leverage recent advances in generative motion models for physics-based motion synthesis. We conduct extensive evaluations of our model across different motion tasks and various physical perturbations, demonstrating the scalability and diversity of responses.

关键词： Generative Models physics-based Motion Synthesis Hybrid Methods

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Multiclass Image Classification based on Quantum‐Inspired Convolutional Neural Network 6

Multiclass Image Classification Based on Quantum‐Inspired C...

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6th International workshop on computer modeling and Intelligent Systems, CMIS 2023

作者： Ahmed, Hamza Kamel Tantawi, Baraa Sayed, Gehad Ismail New Cairo Egypt Computer Science Department School of Science and Technology Troy University United States

Multiclass image classification is considered a challenging task in computer vision that requires correctly classifying an image into one of the multiple distinct groups. In recent years, quantum machine learning has emerged as a topic of significant interest among researchers. Using quantum concepts such as superposition and entanglement, quantum machine learning algorithms provide a more efficient method of processing and classifying high-dimensional image data. This paper proposes a new image classification model using quantum-inspired convolutional neural network architecture or, shortly, QCNN. The proposed model consists of two main phases;pre-processing and classification based on the QCNN phase. Seven benchmark datasets with different characteristics are adopted to evaluate the performance of the proposed model. The experimental results revealed that the proposed QCNN outperformed its classical version. Additionally, the results demonstrated the effectiveness of the proposed model compared with the state-of-the-art models. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Image classification

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A Conceptual Framework for Predictive Digital Dairy Twins: Integrating Explainable AI and Hybrid modeling 10

A Conceptual Framework for Predictive Digital Dairy Twins: I...

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10th International Food Operations and Processing Simulation workshop, FoodOPS 2024, Held at the 21st International Multidisciplinary modeling and Simulation Multiconference, I3M 2024

作者： Jox, Dana Hummel, Darius Hinrichs, Jörg Krupitzer, Christian Department of Food Informatics & Computational Science Hub University of Hohenheim Fruwirthstr.21 Stuttgart70599 Germany Department of Soft Matter Science and Dairy Technology University of Hohenheim Garbenstr.21 Stuttgart70599 Germany

ISBN: (纸本)9791281988118

This vision paper presents a new framework for developing a digital twin for the dairy *** quality and process challenges that are often specific to each dairy company may arise during processing and must be *** approaches can be used to solve the different problems, such as food technology experiments or *** framework addresses specific challenges, such as the inconsistency in raw materials and the intricate biochemical dynamics of dairy *** enables predictive detection and management of critical process states in the *** use a hybrid modeling approach that integrates white-box models that explain the physical, chemical, and biological mechanisms of dairy processes with black-box machine learning models to improve predictive accuracy and process *** framework centers around implementing explainable artificial intelligence (XAI) to connect empirical food science with advanced data-driven *** is integrated to ensure transparency and interpretability of predictions and decisions made by the digital twin, supporting better decision-making processes within the dairy *** proposed model anticipates quality changes, identifies potential deviations in real time, and elucidates the underlying causes. © 2024 The Authors.

关键词： Digital dairy twin Explainable ML modeling Food process analysis physics-informed ML

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BinDev: A Metric of Geometric Accuracy for Plasma-etch 3D modeling Using computer vision : Yield Methodologies 34

BinDev: A Metric of Geometric Accuracy for Plasma-etch 3D Mo...

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34th Annual SEMI Advanced Semiconductor Manufacturing Conference, ASMC 2023

作者： Xie, Yutong Davaji, Benyamin Doerschuk, Peter C. Lal, Amit Chakarov, Ivan Wen, Sandy Hargrove, Michael Fried, David Cornell University United States Lam Research United States

ISBN: (纸本)9781665456395

physics-based semiconductor device and fabrication process modeling provides design and validation solutions for state-of-The-Art CMOS and MEMS devices. In this paper, a metric for the geometric differences between two structures visualized by CD-SEM images is defined, and a computer-vision-based algorithm is developed to evaluate the metric. This algorithm is implemented using Python and SEMulator3D®, a physics-based process modeling software system for semiconductor and MEMS devices. computer vision tools, such as filters, thresholding, and morphology operations, are used to extract geometric features from CD-SEM images and pattern matching and symmetric difference are used to compute the metric. Examples of the metric quantifying the geometric similarity between a simulated nanostructure and an experimental CD-SEM image of the fabricated nanostructure are presented. The data consists of eight classes of nanostructures which are defined, fabricated in the cleanroom with 36 combination of layout parameters, and imaged with CD-SEM. © 2023 IEEE.

关键词： Nanostructures

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workshop on Longitudinal Disease Tracking and modeling with Medical Images and Data, LDTM 2024, 5th International workshop on Multiscale Multimodal Medical Imaging, MMMI 2024, 1st workshop on Machine Learning for Multimodal/-sensor Healthcare Data, ML4MHD2024 and workshop on Multimodal Learning and Fusion Across Scales for Clinical Decision Support, ML-CDS 2024 held in conjunction with the 27th International Conference on Medical Image Computing and computer Assisted Intervention, MICCAI 2024

Workshop on Longitudinal Disease Tracking and Modeling with ...

引用

workshop on Longitudinal Disease Tracking and modeling with Medical Images and Data, LDTM 2024, 5th International workshop on Multiscale Multimodal Medical Imaging, MMMI 2024, 1st workshop on Machine Learning for Multimodal/-sensor Healthcare Data, ML4MHD2024 and workshop on Multimodal Learning and Fusion Across Scales for Clinical Decision Support, ML-CDS 2024 held in conjunction with the 27th International Conference on Medical Image Computing and computer Assisted Intervention, MICCAI 2024

ISBN: (纸本)9783031845246

The proceedings contain 21 papers. The special focus in this conference is on Longitudinal Disease Tracking and modeling with Medical Images and Data. The topics include: Disease Progression Modelling and Stratification for Detecting Sub-trajectories in the Natural History of Pathologies: Application to Parkinson’s Disease Trajectory Modelling;back to the Future: Challenges of Sparse and Irregular Medical Image Time Series;Individualized Multi-horizon MRI Trajectory Prediction for Alzheimer’s Disease;towards Longitudinal Characterization of Multiple Sclerosis Atrophy Employing SynthSeg Framework and Normative modeling;segHeD: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints;Longitudinal Segmentation of MS Lesions via Temporal Difference Weighting;registration of Longitudinal Liver Examinations for Tumor Progress Assessment;Tracking Lesion Evolution Using a Boundary Enhanced Approach for MS Change Segmentation (BEAMS);a Radiological-based Coordinate System for the Human Body: A Proof-of-Concept;language Models Meet Anomaly Detection for Better Interpretability and Generalizability;A Diffusion Model Embedded WCSAU-Net for 3D MRI Brain Tumor Segmentation;predicting Human Brain States with Transformer;Cross-Modality Image Quality Prediction for Time-Resolved CT from Breathing Signals;RATNUS: Rapid, Automatic Thalamic Nuclei Segmentation Using Multimodal MRI Inputs;HyperMM: Robust Multimodal Learning with Varying-Sized Inputs;HEMIT: H&E to Multiplex-Immunohistochemistry Image Translation with Dual-Branch Pix2pix Generator;physics-Informed Latent Diffusion for Multimodal Brain MRI Synthesis;medPromptX: Grounded Multimodal Prompting for Chest X-Ray Diagnosis;predicting Stroke Through Retinal Graphs and Multimodal Self-supervised Learning;multimodality for Diagnosis of Asian Choroidal Vasculopathy: Results from a Novel Dataset and Deep-Learning Experiments;multimodality Frequency Feature Customized Learning for Pediatric Ventricu

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MMSports 2023 - proceedings of the 6th International workshop on Multimedia Content Analysis in Sports, Co-located with: MM 2023

MMSports 2023 - Proceedings of the 6th International Worksho...

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6th ACM International workshop on Multimedia Content Analysis in Sports, MMSports 2023, co-located with ACM Multimedia 2023

ISBN: (纸本)9798400702693

The proceedings contain 20 papers. The topics discussed include: SkiTech: an alpine skiing and snowboarding dataset of 3D body pose, sole pressure, and electromyography;personalized speech-based heart rate categorization using weighted-instance learning;generating factually consistent sport highlights narrations;DeepSportradar-v2: a multi-sport computer vision dataset for sport understandings;video-based skill assessment for golf: estimating golf handicap;automatic edge error judgment in figure skating using 3D pose estimation from a monocular camera and IMUs;context-aware 3D object localization from single calibrated images: a study of basketballs;event-based high-speed ball detection in sports video;mitigating motion blur for robust 3D baseball player pose modeling for pitch analysis;and expected goals prediction in professional handball using synchronized event and positional data.

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