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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems IV"

共 13657 条记录，以下是71-80 订阅

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Bayesian Spectral Graph Denoising with Smoothness Prior 58

Bayesian Spectral Graph Denoising with Smoothness Prior

引用

58th Annual conference on Information Sciences and systems (CISS)

作者： Leone, Sam Sun, Xingzhi Perlmutter, Michael Krishnaswamy, Smita Yale Univ Program Appl Math New Haven CT 06520 USA Yale Univ Dept Comp Sci New Haven CT 06520 USA Boise State Univ Dept Math Boise ID 83725 USA Yale Sch Med Dept Genet New Haven CT USA Yale Univ Wu Tsai Inst New Haven CT 06520 USA Meta AI FAIR Paris France Yale Univ Computat Biol & Bioinformat Program New Haven CT 06520 USA

ISBN: (纸本)9798350369298;9798350369304

Here we consider the problem of denoising features associated to complex data, modeled as signals on a graph, via a smoothness prior. This is motivated in part by settings such as single-cell RNA where the data is very high-dimensional, but its structure can be captured via an affinity graph. This allows us to utilize ideas from graph signal processing. In particular, we present algorithms for the cases where the signal is perturbed by Gaussian noise, dropout, and uniformly distributed noise. The signals are assumed to follow a prior distribution defined in the frequency domain which favors signals which are smooth across the edges of the graph. By pairing this prior distribution with our three models of noise generation, we propose Maximum A Posteriori (M.A.P.) estimates of the true signal in the presence of noisy data and provide algorithms for computing the M.A.P. Finally, we demonstrate the algorithms' ability to effectively restore signals from white noise on image data and from severe dropout in single-cell RNA sequence data.

关键词： denoising graph signal processing estimation

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Adversarial Encoder-Driven Filter for Targeted image Tagging: A Novel Approach to Visual Content Manipulation 6

Adversarial Encoder-Driven Filter for Targeted Image Tagging...

引用

6th IEEE International conference on image processing, Applications and systems, IPAS 2025

作者： Mckee, Cole Flowers, Dominic Wood, Jesse Shafer, Ethan United States Military Academy Department of Electrical Engineering and Computer Science West PointNY10996 United States

ISBN: (纸本)9798331506520

Computer vision, driven by artificial intelligence, has become pervasive in diverse applications such as self-driving cars and law enforcement. However, the susceptibility of these systems to attacks has raised significant concerns among researchers. This paper addresses the vulnerability of image tagging algorithms, particularly focusing on misclassifications induced by autoencoders. We present experiments conducted on Amazon Rekognition, where we developed a specialized autoencoder to manipulate the latent space, forcing it to align with specific tags. By integrating this manipulated latent space with other images, we demonstrate the ability to increase the confidence of a specific tag on Amazon Rekognition, leading to more false positives of the chosen tag. Our study showcases a practical method to exploit Amazon's Rekognition image tagging algorithm using a black box approach. © 2025 IEEE.

关键词： Adversarial machine learning

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A Review on the Applications of Machine Learning and Deep Learning algorithms for image Recognition 4

A Review on the Applications of Machine Learning and Deep Le...

引用

4th International conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Wanjari, Ketan Verma, Prateek Department of Computer Science and Engineering Faculty of Engineering and Technology Maharashtra Wardha442001 India Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Maharashtra Wardha442001 India

ISBN: (纸本)9798331523923

Modern image recognition has experienced dramatic improvements because of Machine Learning and Deep Learning algorithms together. This study investigates CNNs and SVMs for recognition enhancement while reviewing image recognition technologies extensively through literature. This paper demonstrates how the applications of healthcare and security systems and social media analysis influence society. New developments in technology have not resolved multiple unresolved obstacles such as data bias as well as computational complexity and privacy concerns together with real-time processing restrictions. The latest image processing techniques include ViTs alongside GANs and Few-Shot Learning but developers need to achieve better results in future improvements. The main goal of this research examines present-day advancements in ML and DL with a review of their capabilities as well as constraints before recommending future study paths to overcome problems encountered today. This paper evaluates both the future potential and benefits alongside drawbacks of ML and DL models applied to image recognition. © 2025 IEEE.

关键词： Adversarial machine learning

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Application of Lossless Compression Method in image Compression 7

Application of Lossless Compression Method in Image Compress...

引用

7th IEEE International conference on Information systems and Computer Aided Education, ICISCAE 2024

作者： Ni, Weifeng Pu, Tianqiu Wang, Yuxi College of Information Science and Engineering Hohai University Changzhou China

ISBN: (纸本)9798350350760

—This paper is based on the application of lossless compression algorithms in image compression, aiming to solve problems such as insufficient storage space, low transmission efficiency, and heavy data processing burden. In order to optimize the image lossless compression algorithm, a combination of Huffman encoding and LZW compression algorithm is proposed. The implementation process and advantages and disadvantages of the two are compared, and the compression ratio and encoding efficiency of image lossless compression technology are discussed. We need to optimize decoding speed, image quality, and other aspects to improve overall image lossless compression capability. By applying Huffman encoding and LZW compression algorithms to images separately, the results show that Huffman encoding can dynamically construct the optimal encoding based on the frequency of characters appearing in image data, while LZW technology is good at utilizing repeated patterns and contextual information in image data to further improve compression efficiency by establishing dictionaries. The complementary advantages of the two can further improve the lossless compression ability of images. ©2024 IEEE.

关键词： image compression

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Leveraging NVIDIA Clara for Real-Time Cardiac image Segmentation and Diagnosis

Leveraging NVIDIA Clara for Real-Time Cardiac Image Segmenta...

引用

2024 International conference on Innovative Computing, Intelligent Communication and Smart Electrical systems, ICSES 2024

作者： Navaneethan, S. Hemanth, Goalla Nikilkumar, R. Saveetha Engineering College Department of Electronics and Communication Engineering Tamilnadu Chennai India

ISBN: (纸本)9798331543617

Medical imaging is crucial for heart diagnosis, but outdated algorithms and hardware result in delayed processing and low accuracy. Using NVIDIA Clara, a GPU-accelerated platform, the study proposes real-time cardiac picture segmentation and diagnosis. The proposed system uses advanced DL algorithms built for cardiac imaging to enhance processing speed and accuracy. Employing rigorous data preprocessing and deep learning model able to perform precise partitioning of cardiac structures and positioning defects that lead to quick heart arrhythmia diagnose in a timely manner. It also substantially reduces processing times compared to older systems while improving the contour quality and diagnosability. The system achieves the following DSC values: 0.93 for the left ventricle;0.92 for right;0.90 for left atrium;0.89 for right ventricle and 0.91 for the major blood vessels. With diagnostic accuracy scores higher than the current system Sensitivity 94.2%, Specificity 92.8%, and Accuracy 93.5%. The method has potential to enhance cardiac care by facilitating earlier interventions leading better patient outcomes. © 2024 IEEE.

关键词： image segmentation

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Adapting Spatial Transformer Networks Across Diverse Hardware Platforms: A Comprehensive Implementation Study 6

Adapting Spatial Transformer Networks Across Diverse Hardwar...

引用

6th International conference on AI Circuits and systems (AICAS)

作者： Bettayeb, Meriem Hassan, Eman Khan, Muhammad Umair Halawani, Yasmin Saleh, Hani Mohammad, Baker Khalifa Univ Syst On Chip SoC Lab Comp & Commun Engn Dept Abu Dhabi U Arab Emirates Univ Dubai Coll Engn & IT Dubai U Arab Emirates

ISBN: (纸本)9798350383638;9798350383645

The field of artificial intelligence (AI) holds a variety of algorithms designed with the goal of achieving high accuracy at low computational cost and latency. One popular algorithm is the vision transformer (ViT), which excels at various computer vision tasks for its ability to capture long-range dependencies effectively. This paper analyzes a computing paradigm, namely, spatial transformer networks (STN), in terms of accuracy and hardware complexity for image classification tasks. The paper reveals that for 2D applications, such as image recognition and classification, STN is a great backbone for AI algorithms for its efficiency and fast inference time. This framework offers a promising solution for efficient and accurate AI for resource-constrained Internet of Things (IoT) and edge devices. The comparative analysis of STN implementations on the central processing unit (CPU), Raspberry Pi (RPi), and Resistive Random Access Memory (RRAM) architectures reveals nuanced performance variations, providing valuable insights into their respective computational efficiency and energy utilization.

关键词： Spatial Transformer Network image Classification vision transformer raspberry Pi hardware platforms artificial intelligence

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Comparing the Effectiveness and Performance of image processing algorithms in Face Recognition 25

Comparing the Effectiveness and Performance of Image Process...

引用

25th International Carpathian Control conference (ICCC)

作者： Rostum, Hussam M. Vasarhelyi, Jozsef Univ Miskolc Inst Automat & Infocommunicat Miskolc Hungary

ISBN: (纸本)9798350350708;9798350350715

Many institutions have recently embraced biometric security solutions, utilizing biological measurements to safeguard against fraudulent activities, theft, and various security threats. Face recognition technology holds a pivotal role within the realm of bio-metric security systems, serving purposes such as authentication, monitoring, individual identification, and identity verification. This article aims to delve into the examination of facial recognition systems grounded in deep learning. This focus arises due to the intricate nature of the process and the existence of numerous hurdles and variables that impact algorithm performance. The objective here is to illuminate the foremost challenges that real-world systems encounter, often overlooked in previous research. Additionally,under these challenges, the article will conduct a comparative analysis of the performance of prominent facial recognition algorithms, namely VGGFace, FaceNet, and ArcFace. This academic approach will allow to make informed choices when selecting the most suitable algorithms for specific applications.

关键词： Deep Learning Facial Recognition Convolutional Neural Networks VggFace ArcFace FaceNet Cosine Similarity Euclidean Distance

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Q-Segment: Segmenting images In-Sensor for Vessel-Based Medical Diagnosis 6

Q-Segment: Segmenting Images In-Sensor for Vessel-Based Medi...

引用

6th International conference on AI Circuits and systems (AICAS)

作者： Bonazzi, Pietro Li, Yawei Bian, Sizhen Magno, Michele Swiss Fed Inst Technol D ITET Zurich Switzerland

ISBN: (纸本)9798350383638;9798350383645

This paper addresses the growing interest in deploying deep learning models directly in-sensor. We present "Q-Segment", a quantized real-time segmentation algorithm, and conduct a comprehensive evaluation on a low-power edge vision platform with an in-sensors processor, the Sony IMX500. One of the main goals of the model is to achieve end-to-end image segmentation for vessel-based medical diagnosis. Deployed on the IMX500 platform, Q-Segment achieves ultra-low inference time in-sensor only 0.23 ms and power consumption of only 72mW. We compare the proposed network with state-of-the-art models, both float and quantized, demonstrating that the proposed solution outperforms existing networks on various platforms in computing efficiency, e.g., by a factor of 75x compared to ERFNet. The network employs an encoder-decoder structure with skip connections, and results in a binary accuracy of 97.25 % and an Area Under the Receiver Operating Characteristic Curve (AUC) of 96.97 % on the CHASE dataset. We also present a comparison of the IMX500 processing core with the Sony Spresense, a low-power multi-core ARM Cortex-M microcontroller, and a single-core ARM Cortex-M4 showing that it can achieve in-sensor processing with end-to-end low latency (17 ms) and power consumption (254mW). This research contributes valuable insights into edge-based image segmentation, laying the foundation for efficient algorithms tailored to low-power environments. [GRAPHICS] .

关键词： AIoT neural networks image segmentation blood vessel segmentation TinyML edge processing medical imagining retina vessel

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RECURRENT 3-D MULTI-LEVEL VISUAL TRANSFORMER FOR JOINT CLASSIFICATION OF HETEROGENEOUS 2-D AND 3-D RADIOGRAPHIC DATA 31

RECURRENT 3-D MULTI-LEVEL VISUAL TRANSFORMER FOR JOINT CLASS...

引用

2024 International conference on image processing

作者： Owais, Muhammad Zubair, Muhammad Hassan, Taimur Velayudhan, Divya Hussain, Irfan Werghi, Naoufel Khalifa Univ Dept Mech & Nucl Engn KUCARS Abu Dhabi U Arab Emirates Univ Cent Punjab Fac IT & Comp Sci Punjab Pakistan Abu Dhabi Univ Dept Elect Comp & Biomed Engn Abu Dhabi U Arab Emirates Khalifa Univ Dept Comp Sci C2PS Abu Dhabi U Arab Emirates

ISBN: (纸本)9798350349405;9798350349399

Recent advancements in artificial intelligence algorithms for medical imaging show significant potential in automating the detection of lung infections from chest radiograph scans. However, current approaches often focus solely on either 2-D or 3-D scans, failing to leverage the combined advantages of both modalities. Moreover, conventional slice-based methods place a manual burden on radiologists for slice selection. To overcome these challenges, we propose the Recurrent 3-D Multi-level Vision Transformer (R3DM-ViT) model, capable of handling multimodal data to enhance diagnostic accuracy. Our quantitative evaluations demonstrate that R3DM-ViT surpasses existing methods, achieving an impressive accuracy of 96.67%, F1-score of 96.88%, mean average precision of 96.75%, and mean average recall of 97.02%. This research signifies a significant stride forward in the automated detection of lung infections through multimodal imaging.

关键词： lung infection R3DM-ViT CBMIR Computer-aided diagnosis Medical image retrieval

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Robust Multilayer Matrix Factorization for Blind Hyperspectral image Unmixing 16

Robust Multilayer Matrix Factorization for Blind Hyperspectr...

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16th International conference on Signal processing systems, ICSPS 2024

作者： Yang, Gang Southwest China Institute of Electronic Technology Chengdu China

ISBN: (纸本)9781510689251

Hyperspectral image unmixing estimates a collection of constituent materials (called endmembers) and their corresponding proportions (called abundances), which is a critical preprocessing step in many remote sensing applications. In this paper, we propose a novel robust multilayer matrix factorization (RMLMF) approach for hyperspectral image unmixing. Firstly, instead of sampling all observed pixels, the endmembers are modeled as a linear combination of candidate endmembers to reduce the redundant spectra and alleviate heavy computational cost. Secondly, to explore the hidden information, the shallow decomposition is extended into multilayer structure. Furthermore, considering that hyperspectral images (HSIs) still suffer from a variety of noises, L2,1-norm is employed to measure the reconstruction error of the multilayer structure. Experimental results on both synthetic and real data sets demonstrate the superiority of the proposed RMLMF over many state-of-the-art algorithms. © 2025 SPIE.

关键词： Matrix factorization

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