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检索条件"任意字段=IEEE Symposium on Computational Intelligence for Multimedia Signal and Vision Processing, CIMSVP"

共 112 条记录，以下是1-10 订阅

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Poster: Cloud Computing with AI-empowered Trends in Software-Defined Radios: Challenges and Opportunities 25

Poster: Cloud Computing with AI-empowered Trends in Software...

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25th ieee International symposium on a World of Wireless, Mobile and multimedia Networks (ieee WoWMoM)

作者： Sharma, Ekta Deo, Ravinesh C. Davey, Christopher P. Carter, Brad D. Salcedo-Sanz, Sancho Univ Southern Queensland Sch Math Phys & Comp Springfield Qld 4300 Australia Univ Southern Queensland Ctr Astrophys Toowoomba Qld 4350 Australia Univ Alcala Dept Signal Proc & Commun Alcala De Henares 28805 Spain

ISBN: (纸本)9798350394665;9798350394672

Artificial intelligence (AI) and Software Defined Radio (SDR) are transforming the field of signal intelligence. However, the full extent of the capabilities is unknown. This poster presents a paper in development that introduces a cloud-based platform leveraging artificial intelligence to detect and apply 11 modulation schemes (8 digital and 3 analog) to complex or quadrature radio signals. The SNR values analysed range from 0.0 to 40.0, with moderate drift, slight fading, and labelled increments. A comprehensive synthetic database developed by DeepSig is used to train four AI models. These will be integrated with the Google Cloud AI platform to enhance flexibility and processing power. The system will undergo testing with an SDR platform in GNU Radio, showcasing its potential for real-world signal processing applications. Cloud-based platforms offer the adaptability and computational power needed to replace traditional computers for AI-driven signal processing. Initial results indicate successful identification and accurate modulation type detection, with convenient access to the system through internet-connected devices.

关键词： cloud computing artificial intelligence software-defined radio modulation GNU Radio signal classification

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Deep Learning for age Estimation 7

Deep Learning for age Estimation

引用

ieee 7th International Conference on Advanced Technologies, signal and Image processing (ATSIP)

作者： Ammous, Donia Kammoun, Fahmi Masmoudi, Nouri Natl Sch Engineers Sfax Higher Inst Comp Sci & Multimedia Sfax Lab Elect & Informat Technol LETI Sfax Tunisia Univ Sfax Natl Sch Engineers Sfax Lab Elect & Informat Technol Circuit & Syst Team C&SLR99ES37 Sfax Tunisia

ISBN: (纸本)9798350351491;9798350351484

The significance of age recognition from faces in various domains, including minor security and online shopping, has drawn potential attention. It still faces a number of difficulties, though, particularly in an unrestricted setting, including anti-aging therapies, lighting circumstances, and filter effects. Advancements in computer vision offer promising avenues for more precise age prediction, particularly in facial analysis. However, challenges persist, including the need for robust datasets, standardized protocols, especially in computational approaches and generalization model's. This work provides an overview of the three methodologies employed in age estimation. The performance of their approach is evaluated by confusion matrix. The first test, which uses three datasets separately, can demonstrate that using just one dataset is insufficient to produce a high-quality confusion matrix. The second test overcome this problem by combine data from various sources, apply data augmentation technique and perform early stopping to achieve an excellent confusion matrix for age recognition. Obtained results validate the proposed approach.

关键词： Age estimation Computer vision Artificial intelligence

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RGB Image-Based Hand Pose Estimation: A Survey on Deep Learning Perspective 20

RGB Image-Based Hand Pose Estimation: A Survey on Deep Learn...

引用

20th CSI International symposium on Artificial intelligence and signal processing, AISP 2024

作者： Farjadi, Seyed Amirhossein Akbarzadeh-T, M.-R. Ghiasi-Shirazi, Kamaledin Ferdowsi University of Mashhad Center of Excellence on Soft Computing and Intelligent Information Processing Department of Computer Engineering Mashhad Iran Ferdowsi University of Mashhad Department of Computer Engineering Mashhad Iran

ISBN: (纸本)9798350383942

The pervasive integration of modern artificial intelligence into daily life necessitates robust human-computer interaction, underscored by advancements in computer technology. As the primary human tool, hand position, and orientation are vital for applications like virtual reality, with hand pose estimation playing a pivotal role. Despite the challenges inherent in RGB image-based estimation, the coalescence of extensive datasets, neural networks, and heightened computational capabilities has catalyzed the utilization of deep learning in visual tasks, particularly advancing hand pose estimation. This review exclusively centers on methodologies of RGB image-based approaches within the realm of hand pose estimation. The paper explores recent advancements in this category, providing a comprehensive analysis of outcomes and charting future directions. Organized around discussions on challenges, advancements, and future directions, this review aims to contribute to the ongoing discourse in the dynamic RGB image-based hand pose estimation field. By addressing unique challenges specific to this method, the paper offers insights that contribute to developing precise and robust hand pose estimation systems, impacting the broader landscape of computer vision and human-machine interaction. © 2024 ieee.

关键词： Computer vision

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Neuromorphic Event Alarm Time-Series Suppression

Neuromorphic Event Alarm Time-Series Suppression

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2023 ieee symposium Series on computational intelligence, SSCI 2023

作者： Harrigan, Shane Coleman, Sonya Kerr, Dermot Quinn, Justin Madden, Kyle Lindsay, Leeanne Henderson, Benn Rahman, Shammi SCEIS-CRL Ulster University Derry United Kingdom

ISBN: (纸本)9781665430654

The field of neuromorphic vision systems aims to replicate the functionality of biological visual systems by mimicking their physical structure and electrical behaviour. Unlike traditional full-frame sensors, neuromorphic systems process data asynchronously and at the pixel level, modelling biological signalling processes. This allows for high-speed operations with lower energy consumption, making them suitable for applications like autonomous vehicles and embedded robotics. This work introduces the Neuromorphic Event Alarm Time-Series Suppression (NEATS) framework, designed to filter noise and detect outlier behaviours in event data without the need for 2-D transformations. NEATS employs rolling statistics and advanced neuromorphic data structures to minimise noise while identifying changes in scene dynamics. This framework injects attention into scene processing, similar to summarisation frameworks in traditional image processing. A novel event-vision alarm change collection (EACC) database is presented, containing controlled stimuli pattern changes captured using leading neuromorphic imaging devices. This database facilitates future benchmarking of neuromorphic attention frameworks, advancing the development of efficient and accurate artificial vision systems. © 2023 ieee.

关键词： Bio-inspired Pattern Recognition signal processing

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INTERPRETABLE MODELS FOR DETECTING AND MONITORING ELEVATED INTRACRANIAL PRESSURE 21

INTERPRETABLE MODELS FOR DETECTING AND MONITORING ELEVATED I...

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21st ieee International symposium on Biomedical Imaging (ISBI)

作者： Hannan, Darryl Nesbit, Steven C. Wen, Ximing Smith, Glen Zhang, Qiao Goffi, Alberto Chan, Vincent Morris, Michael J. Hunninghake, John C. Villalobos, Nicholas E. Kim, Edward Weber, Rosina O. MacLellan, Christopher J. Drexel Univ Philadelphia PA 19104 USA Univ Toronto Toronto ON Canada Brooke Army Med Ctr Ft Sam Houston TX USA Georgia Inst Technol Atlanta GA USA

ISBN: (纸本)9798350313345;9798350313338

Detecting elevated intracranial pressure (ICP) is crucial in diagnosing and managing various neurological conditions. These fluctuations in pressure are transmitted to the optic nerve sheath (ONS), resulting in changes to its diameter, which can then be detected using ultrasound imaging devices. However, interpreting sonographic images of the ONS can be challenging. In this work, we propose two systems that actively monitor the ONS diameter throughout an ultrasound video and make a final prediction as to whether ICP is elevated. To construct our systems, we leverage subject matter expert (SME) guidance, structuring our processing pipeline according to their collection procedure, while also prioritizing interpretability and computational efficiency. We conduct a number of experiments, demonstrating that our proposed systems are able to outperform various baselines. One of our SMEs then manually validates our top system's performance, lending further credibility to our approach while demonstrating its potential utility in a clinical setting.

关键词： Machine Learning Computer vision Biomedical Imaging

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Sonar signal Classification through Dimensionality Reduction and Artificial intelligence: A Comparative Study 8

Sonar Signal Classification through Dimensionality Reduction...

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8th International Artificial intelligence and Data processing symposium, IDAP 2024

作者： Ramos, Leo Thomas Bermeo, Mike Amaro, Isidro R. Kauel Inc Menlo ParkCA94025 United States Universitat Autònoma de Barcelona Computer Vision Center Bellaterra08193 Spain Digevo Ventures Santiago7560941 Chile Yachay Tech University School of Mathematical and Computational Sciences Urcuquí100119 Ecuador

ISBN: (纸本)9798331531492

This study examines the impact of dimensionality reduction techniques, particularly Principal Component Analysis (PCA) and Sparse Principal Component Analysis (SPCA), on the performance of various classification models. Utilizing a dataset comprised of sonar signals intended to distinguish between rocks and mines, we explore the efficacy of these techniques across multiple models including Random Forest (RF), Support Vector Machine (SVM), XGBoost, and a Feed-forward Neural Network (NN). Our experiments are structured into three distinct scenarios: direct application of classifiers, application following PCA, and application following SPCA. The findings reveal that SVM and RF excel without dimensionality reduction, achieving high accuracy rates. However, the application of SPCA notably enhances the performance of the NN, marking a significant improvement in metrics and efficiency, while SVM and RF experience a decrease in effectiveness with dimensionality reduction. Conversely, XGBoost shows consistent performance across all scenarios. This research underscores the variable influence of dimensionality reduction on different classifiers and suggests that the choice of technique should be tailored to the specific characteristics of both the model and the data. © 2024 ieee.

关键词： Dimensionality reduction

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Rate-Controllable and Target-Dependent JPEG-Based Image Compression Using Feature Modulation

Rate-Controllable and Target-Dependent JPEG-Based Image Comp...

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2023 ieee International Conference on multimedia and Expo Workshops, ICMEW 2023

作者： Jeong, Seongmoon Jeon, Kang Eun Ko, Jong Hwan Sungkyunkwan University Department of Artificial Intelligence Suwon-si Korea Republic of Convergence Research Institute Sungkyunkwan University Suwon-si Korea Republic of Sungkyunkwan University Department of Electrical and Computer Engineering Suwon-si Korea Republic of

ISBN: (纸本)9798350313154

While conventional image compression techniques are optimized for human visual perception, the rise of machine learning techniques has led to the emergence of image compression methods tailored for machine vision tasks. Although a few recent studies explored target-dependent reconfiguration of lightweight codecs such as JPEG, these approaches are limited to specific trained bitrates only. Moreover, existing deep learning-based compression frameworks entail a high computational cost, making them impractical for real-time compression on devices with limited resources. In this paper, we present a novel JPEG compression framework that can adaptively generate an optimal quantization table (QT) depending on both the target bitrate and the target metric (quality or accuracy). To provide fine controllability over a wide range of bitrates, we employ a feature modulation technique to a QT generator and bitrate predictor, which are trained by a novel training method called bitrate range partitioning. Our simulation results show that the proposed framework enhances the performance of standard JPEG by up to 2dB in PSNR and 10% in accuracy at the same bitrate, while incurring minimal computational overhead compared to JPEG. © 2023 ieee.

关键词： Image compression

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Generated Compressed Domain Images to the Rescue: Cross Distillation from Compressed Domain to Pixel Domain

Generated Compressed Domain Images to the Rescue: Cross Dist...

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ieee International symposium on Circuits and Systems (ISCAS)

作者： Reyhan Kevser Keser Muhammet Sebul Beratoğlu Behçet Uğur Töreyin Signal Processing for Computational Intelligence Research Group (SP4CING) Informatics Institute İstanbul Technical University İstanbul Türkiye Dept. of Artificial Intelligence and Data Engineering Signal Processing for Computational Intelligence Research Group (SP4CING) İstanbul Technical University İstanbul Türkiye

ISBN: (数字)9798350330991

ISBN: (纸本)9798350331004

Data are the essential component in the pipeline of training a model that determines the performance of the model. However, there may not be enough data that meet the requirements of some tasks. In this paper, we introduce a knowledge distillation-based approach that mitigates the disadvantages of data scarcity. Specifically, we propose a method that boosts the pixel domain performance of a model, by utilizing compressed domain knowledge via cross distillation between these two modalities. To evaluate our approach, we conduct experiments on two computer vision tasks which are object detection and recognition. Results indicate that compressed domain features can be utilized for a task in the pixel domain via our approach, where data are scarce or not completely available due to privacy or copyright issues.

关键词： Training Data privacy Computer vision Image coding computational modeling Pipelines Object detection

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Poster: Cloud Computing with AI-empowered Trends in Software-Defined Radios: Challenges and Opportunities

Poster: Cloud Computing with AI-empowered Trends in Software...

引用

ieee International symposium on World of Wireless Mobile and multimedia Networks (WoWMoM)

作者： Ekta Sharma Ravinesh C. Deo Christopher P. Davey Brad D. Carter Sancho Salcedo-Sanz School of Mathematics Physics and Computing University of Southern Queensland Springfield Queensland Australia Centre for Astrophysics University of Southern Queensland Toowoomba Queensland Australia Department of Signal Processing and Communications Universidad de Alcalá Alcalá de Henares Spain

ISBN: (数字)9798350394665

ISBN: (纸本)9798350394672

关键词： Training Computers Wireless communication Cloud computing Modulation System integration Artificial intelligence

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JPEG AI Compressed Domain Face Detection

JPEG AI Compressed Domain Face Detection

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ieee Workshop on multimedia signal processing

作者： Ayman Alkhateeb Alessandro Gnutti Fabrizio Guerrini Riccardo Leonardi João Ascenso Fernando Pereira Dep. of Information Engineering CNIT - University of Brescia Italy Dep. of Electronics Engineering University of Rome Tor Vergata Italy Instituto Superior Técnico Universidade de Lisboa - Instituto de Telecomunicações Lisbon Portugal

ISBN: (数字)9798350387254

ISBN: (纸本)9798350387261

Learning-based image coding has achieved competitive performance in terms of compression efficiency, while also gaining a key advantage in the ability to carry out computer vision tasks directly in the compressed domain. In fact, the latent representation which is generated using deep learning techniques may natively encapsulate all visual features needed for processing tasks, thereby eliminating the need to perform the expensive synthesis transform process at the decoder side. In this paper, it is proposed to perform face detection using the latent code present in the JPEG AI architecture. First, some experiments show how decoded images can be efficiently processed for face detection without retraining, albeit with some performance degradation. Then, for the first time a compressed domain RetinaFace-based detector applied to JPEG AI latent representations is competitively proposed. The performance achieved is comparable to the performance of the original RetinaFace applied to the reconstructed JPEG AI images, while reducing computational complexity since it bypasses the image decoding process. It is expected that this approach might be extended to other vision tasks since the JPEG AI representation format is not tailored specifically for any computer vision task.

关键词： Visualization Image coding Transform coding Transforms signal processing Decoding Face detection Artificial intelligence Image reconstruction Faces

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