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共 18153 条记录，以下是771-780 订阅

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Sign Language Translation Using Image processing to Audio Conversion 3

Sign Language Translation Using Image Processing to Audio Co...

引用

3rd IEEE international conference on Intelligent Techniques in Control, Optimization and signal processing, INCOS 2024

作者： Subashini, V. Someshwaran, B. Sowmya, S. Kumar, S Ashwin Rajalakshmi Institute of Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350361186

Sign language has long been a fundamental mode of communication for deaf and mute individuals, serving as a crucial tool for inclusivity and interaction. Nonetheless, communication barriers persist as many individuals outside of these communities struggle to comprehend and utilize sign language effectively. A ground-breaking system known as hand gesture recognition using image processing to audio conversion has emerged to address this issue. This innovative system aims to develop an application capable of translating sign language and hand gestures into text and audio, thereby facilitating communication between deaf and mute individuals and the wider society. This system enables the detection and classification of hand gestures by employing computer vision techniques, such as CNN algorithms for image processing and the MediaPipe framework for hand gesture identification in real-time video streams. Subsequently, the system utilizes audio signals to provide immediate feedback by converting the detected gestures into corresponding sounds. The feature extraction CNN algorithm is implemented in Python, while the execution takes place on a Raspberry Pi connected to an external camera utilizing OpenCV libraries. Through this comprehensive approach, the system endeavours to bridge the communication gap and enhance the inclusion of deaf and mute individuals in various social settings. © 2024 IEEE.

关键词： Libraries

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VARIATIONAL FEATURE DISENTANGLEMENT FOR FEW-SHOT DOMAIN ADAPTATION 30

VARIATIONAL FEATURE DISENTANGLEMENT FOR FEW-SHOT DOMAIN ADAP...

引用

30th IEEE international conference on Image processing (ICIP)

作者： Wang, Weiduo Gu, Yun Yang, Jie Shanghai Jiao Tong Univ Inst Image Proc & Pattern Recognit Dept Automat Shanghai Peoples R China Shanghai Jiao Tong Univ Inst Med Robot Shanghai Peoples R China Shanghai Ctr Brain Sci & Brain Inspired Technol Shanghai Peoples R China

ISBN: (纸本)9781728198354

In this paper, we focus on the few-shot domain adaptation problem. With limited training data in target domain, a new approach is emerging to acquire the transferable knowledge from the source domain. Previous methods aligned the embedding space between domains by reducing the pair-wise distance. However, these methods are reporting the misalignment and poor generalization. To solve this problem, we propose a variational feature disentanglement framework. The embedding features are explicitly disentangled into domainin-variant and domain-specific components. The distributions of domain-invariant variance are estimated and aligned by the variational inference. For further disentanglement, the domain-invariant and domain-specific components are separated by the orthogonal constraints of subspaces. The experiments on Digits dataset and VisDA-C dataset demonstrate that the proposed method can outperform the state-of-the-art methods.

关键词： Domain Adaptation Few-shot Learning Variational Inference Feature Disentanglement

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Bridging the Semantic Gap: A Deep Learning Framework for Semantic Video Search Integrating vision and Language for Enhanced Video Retrieval 10

Bridging the Semantic Gap: A Deep Learning Framework for Sem...

引用

10th international conference on Communication and signal processing, ICCSP 2024

作者： Annamalai, J. Arjun Sneha, S. Sharon Ashokkumar, C. Srm Institute of Science and Technology School of Computing College of Engineering and Technology Department of Computing Technologies Chennai Kattankulathur603203 India

ISBN: (纸本)9798350353068

Traditional video search engines often rely on tags or manual annotations for content retrieval, limiting the accuracy and efficiency of search results. Moreover, keyword-centric searches may not adeptly capture the nuanced and intricate queries users pose when seeking specific video content. The envisioned video search system integrates machine learning and natural language processing components to enable efficient and effective video retrieval based on user queries. The pipeline includes video processing employing vision Transformer with GPT-2 (ViT-GPT2) architecture to analyze intricate details within video frames, Speech-to-Text (STT) models for transcribing spoken content, and the combination of transcriptions with video captions to form textual descriptions. These descriptions are then embedded using BERT, a transformer-based model, to grasp contextual relationships. Semantic matching is achieved through deep learning models, and a ranking mechanism based on similarity scores facilitates efficient retrieval. Leveraging datasets such as the MSR-VTT dataset, spanning visual recognition domains, contributes to the evaluation and training of the system's components. The system's efficacy is evaluated using metrics such as precision, recall, F1-score, and ranking metrics, with datasets spanning visual recognition, speech transcription, and semantic similarity domains. The comprehensive evaluation approach ensures the robustness and relevance of the video search system, aligning with user expectations and real-world use cases. © 2024 IEEE.

关键词： Semantics

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Image-based Gait Spatiotemporal Parameters Estimation using a Single Camera and CNN-Transformer Hybrid Network

Image-based Gait Spatiotemporal Parameters Estimation using ...

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45th Annual international conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Jamsrandorj, Ankhzaya Quynh Hoang Ngan Nguyen Jung, Dawoon Baek, Min Seok Mun, Kyung-Ryoul Kim, Jinwook Univ Sci & Technol KIST Sch Dept Human Comp Interface & Robot Engn Seoul South Korea Univ Sci & Technol KIST Sch Dept AI Robot Seoul South Korea Yonsei Univ Wonju Coll Med Wonju Severance Christian Hosp Dept Neurol Wonju South Korea Korea Inst Sci & Technol Ctr Artificial Intelligence Seoul South Korea

ISBN: (纸本)9798350324471

vision-based gait analysis can play an important role in the remote and continuous monitoring of the elderly's health conditions. However, most vision-based approaches compute gait spatiotemporal parameters using human pose information and provide average parameters. This study aimed to propose a straightforward method for stride-by-stride gait spatiotemporal parameters estimation. A total of 160 elderly individuals participated in this study. Data were gathered with a GAITRite system and a mobile camera simultaneously. Three deep learning networks were trained with a few RGB frames as input and a continuous 1D signal containing both spatial and temporal gait parameters as output. The trained networks estimated the stride lengths with correlations of 0.938 and more and detected gait events with F-1-scores of 0.914 and more.

关键词： Parameter estimation

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vision Transformer-Based Feature Extraction for Generalized Zero-Shot Learning 48

Vision Transformer-Based Feature Extraction for Generalized ...

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48th IEEE international conference on Acoustics, Speech and signal processing, ICASSP 2023

作者： Kim, Jiseob Shim, Kyuhong Kim, Junhan Shim, Byonghyo Seoul National University Department of Electrical and Computer Engineering Seoul Korea Republic of

ISBN: (纸本)9781728163277

Generalized zero-shot learning (GZSL) is a technique to train a deep learning model to identify unseen classes using the image attribute. In this paper, we put forth a new GZSL technique exploiting vision Transformer (ViT) to maximize the attribute-related information contained in the image feature. In ViT, the entire image region is processed without the degradation of the image resolution and the local image information is preserved in patch features. To fully enjoy the benefits of ViT, we exploit patch features as well as the CLS feature in the extraction of the attribute-related image feature. In particular, we propose a novel attention-based module, called attribute attention module (AAM), to aggregate the attribute-related information in the patch features. From extensive experiments on benchmark datasets, we demonstrate that the proposed technique outperforms the state-of-the-art GZSL approaches by a large margin. © 2023 IEEE.

关键词： Generalized zero-shot learning vision Transformer

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ROBUST WIND TURBINE BLADE SEGMENTATION FROM RGB IMAGES IN THE WILD 30

ROBUST WIND TURBINE BLADE SEGMENTATION FROM RGB IMAGES IN TH...

引用

30th IEEE international conference on Image processing (ICIP)

作者： Perez-Gonzalo, Raul Espersen, Andreas Agudo, Antonio CSIC UPC Inst Robot & Informat Ind Barcelona Spain Wind Power LAB Copenhagen Denmark

ISBN: (纸本)9781728198354

With the relentless growth of the wind industry, there is an imperious need to design automatic data-driven solutions for wind turbine maintenance. As structural health monitoring mainly relies on visual inspections, the first stage in any automatic solution is to identify the blade region on the image. Thus, we propose a novel segmentation algorithm that strengthens the U-Net results by a tailored loss, which pools the focal loss with a contiguity regularization term. To attain top performing results, a set of additional steps are proposed to ensure a reliable, generic, robust and efficient algorithm. First, we leverage our prior knowledge on the images by filling the holes enclosed by temporarily-classified blade pixels and by the image boundaries. Subsequently, the mislead classified pixels are successfully amended by training an on-the-fly random forest. Our algorithm demonstrates its effectiveness reaching a non-trivial 97.39% of accuracy.

关键词： Blade Segmentation Wind Turbine Blade Inspections BU-Net Hole Filling

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Research on the Application of Moving Target Tracking Based on Binocular vision 3

Research on the Application of Moving Target Tracking Based ...

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3rd IEEE international conference on Electronic Technology, Communication and Information, ICETCI 2023

作者： Xia, Xinwei Xuzhou University of Technology Xuzhou221000 China

ISBN: (纸本)9798350398410

Moving target tracking is one of the most popular research topics in machine vision and target tracking. Its primary purpose is to identify and locate target objects using cameras and other sensors, capture trajectories using depth and positioning results, enable robots and vehicles to avoid obstacles and help realize autonomous driving. Currently, the methods of moving target tracking include radar-based point cloud schemes and camera-based binocular vision schemes. The first scheme is difficult to implement at a low cost due to the cost of radar. The latter algorithm has high complexity, but the cost is low, which is one of the main current research directions. This paper focuses on the research of moving target tracking based on binocular vision. The researchers further construct the moving target tracking algorithm by studying the traditional tracking method and the principle of binocular range. In addition, the ROI extraction procedure was improved, depth and distance calculation methods were established, and global stereo matching, tracking, and positioning were realized. It is expected that the research in this paper can provide some reference for machine vision and robot vision obstacle avoidance. © 2023 IEEE.

关键词： Stereo image processing

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Research on Emotion Classification Recognition Method Based on vision Transformer Model 14

Research on Emotion Classification Recognition Method Based ...

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14th IEEE international conference on Cyber Technology in Automation, Control, and Intelligent Systems, CYBER 2024

作者： Zhang, Chaofan Jing, Miao Liu, Wenjie Wang, Xinyao Wang, Fei Northeastern University Faculty of robot China Bussiness School Liaoning University China Northeastern University Department of Engineering China

ISBN: (纸本)9798331506056

In this study, we propose a novel approach for processing EEG signals. We applied band-pass filtering to EEG signals across multiple frequency bands, expanded the two-dimensional EEG data into three dimensions, and performed a step of artificial feature extraction to obtain an EEG feature matrix with a structure similar to an image. Additionally, we developed an emotion recognition model based on the EEG signals using the vision Transformer model. This model utilizes its self-attention mechanism to explore the global dependencies among different frequency bands and electrode channels, aiming to capture the emotional valence and arousal information contained within the three-dimensional EEG feature matrix, thereby determining the true emotional state of the subjects. After training, our model achieved relatively high accuracy on both the training set and the test set we collected. © 2024 IEEE.

关键词： Emotion Recognition

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PSEUDO LABELS REFINEMENT WITH INTRA-CAMERA SIMILARITY FOR UNSUPERVISED PERSON RE-IDENTIFICATION 30

PSEUDO LABELS REFINEMENT WITH INTRA-CAMERA SIMILARITY FOR UN...

引用

30th IEEE international conference on Image processing (ICIP)

作者： Li, Pengna Wu, Kangyi Zhou, Sanping Huang, Qianxin Wang, Jinjun Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot 28 West Xianning Rd Xian Shaanxi Peoples R China

ISBN: (纸本)9781728198354

Unsupervised person re-identification (Re-ID) aims to retrieve person images across cameras without any identity labels. Most clustering-based methods roughly divide image features into clusters and neglect the feature distribution noise caused by domain shifts among different cameras, leading to inevitable performance degradation. To address this challenge, we propose a novel label refinement framework with clustering intra-camera similarity. Intra-camera feature distribution pays more attention to the appearance of pedestrians and labels are more reliable. We conduct intra-camera training to get local clusters in each camera, respectively, and refine inter-camera clusters with local results. We hence train the Re-ID model with refined reliable pseudo labels in a self-paced way. Extensive experiments demonstrate that the proposed method surpasses state-of-the-art performance. Code is available at https://***/leeBooMla/ICSR.

关键词： Unsupervised person re-identification clustering deep learning

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U-Shaped Parts Grasping Detection Method Based on Rotated-Yolov5 8

U-Shaped Parts Grasping Detection Method Based on Rotated-Yo...

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8th international conference on Image, vision and Computing, ICIVC 2023

作者： Cui, He School of Mechanical Engineering Southwest Jiaotong University Chengdu China

ISBN: (纸本)9798350335231

For improving the production efficiency and reduce labor costs in Industry, robotic grasping method has been concerned. The most common scenario in industry is sorting various parts and components, which is also the most suitable application scenario for robot grasping. For the U-shaped buckle grasping job, this paper proposes a scheme based on objection detection algorithm, fusing of classification angle information. At the same time of meeting real-time requirements, it can achieve more than 0.9 accuracy of the rotation target detection. Then using a mask-based digital image processing method to obtain the grasping points, providing reliable information for the subsequent grasping tasks. © 2023 IEEE.

关键词： Image processing

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