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检索条件"任意字段=Conference on Image Processing - Machine Vision Applications VIII"

共 3255 条记录，以下是51-60 订阅

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Privacy-Preserving Retrieval Scheme Over Medical images Based on vision Transformer 20th

Privacy-Preserving Retrieval Scheme Over Medical Images Base...

引用

20th International conference on Intelligent Computing (ICIC)

作者： Du, Ruizhong Wang, Yifan Li, Mingyue Shang, Tao Hebei Univ Sch Cyber Secur & Comp Baoding 071002 Hebei Peoples R China Beihang Univ Sch Cyber Sci & Technol Beijing 100000 Peoples R China

ISBN: (纸本)9789819756025;9789819756032

Online pathological diagnosis is essential for improving disease detection and advancing telemedicine but faces challenges related to data privacy and security. Therefore, we propose SecViT, a medical image retrieval scheme that uses secret-sharing technology to deploy a vision Transformer model across two separate cloud servers via interactive protocols for secure image storage and processing. Significantly, we enhance processing efficiency by improving the comparison protocol. Additionally, to achieve secure and efficient division computations, we design secure scaling and iterative protocols based on the coordinate rotation digital computer algorithm. Utilizing these improved protocols, the advantages of CNNs and Transformers can be effectively combined to achieve efficient and privacy-preserving image retrieval. Finally, empirical experiments prove that it outperforms state-of-the-art privacy-preserving solutions for medical images regarding computational cost, communication overhead, and retrieval accuracy.

关键词： Privacy Protection Secure Two-Party Computation Medical image Retrieval vision Transformer Additive Secret Sharing

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LFRNet: Low-Light Face Super-Resolution with Light Frequency Representation 2

LFRNet: Low-Light Face Super-Resolution with Light Frequency...

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2nd International conference on Algorithm, image processing and machine vision, AIPMV 2024

作者： Zha, Bingxin Zhu, Huijie Zhang, Haolin Yang, Shengying School of Information and Electronic Engineering Zhejiang University of Science and Technology Hangzhou310023 China Huzhou Zhongke Fanzai Electric Power Technology Development Co. Ltd Huzhou313000 China Co. Ltd Shanghai200233 China

ISBN: (纸本)9798350390254

In the field of computer vision, the task of facial super-resolution (FSR) is crucial for applications such as surveillance and photo restoration. However, factors such as noise and artifacts in real-world scenarios severely degrade image quality. Although existing methods use geometric priors and facial heatmap localization to improve FSR performance, these priors are often inaccurate under low-light conditions, affecting the results. This paper proposes an innovative low-light facial enhancement network aimed at integrating FSR and low-light enhancement tasks. We designed a Light Frequency Inference Block (LFIB) to capture and refine brightness and texture features at different frequencies, combining it with Transformer modules in an Encoder-Decoder architecture. The LFIB module separates degraded images into low-frequency and high-frequency brightness features and employs spatial cross-attention to capture facial texture details, effectively addressing the degradation issues in facial images. Experimental results demonstrate that our method excels in the low-light facial super-resolution task, outperforming existing methods on various metrics. Additionally, it shows good generalization capabilities in real-world scenarios, confirming its potential for practical applications. © 2024 IEEE.

关键词： image reconstruction

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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...

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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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Bionic model of information processing in the retina

Bionic model of information processing in the retina

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2024 International conference on Artificial Intelligence, Computer, Data Sciences, and applications, ACDSA 2024

作者： Bohlmann, Sabine Clausthal University of Technology Institute of Computer Science Clausthal-Zellerfeld Germany

ISBN: (纸本)9798350394528

Today, bionic models for vision applications base on the general information pathways, structure and characteristics of the visual system implemented in intelligent algorithms, mostly based on AI, to improve the resolution, colour and contrast of images. These approaches often use machine learning techniques and neural networks, or simply mathematical pre-processing filters. The major drawback of all these methods is that they require a lot of computing power. But nature can't afford that kind of processing power just to improve contrast/resolution, detect contours or the true colours of an image and it doesn't use learning strategies or high-order mathematical equations to detect contours. Nature works very simply and very efficiently, it can only work with potentials and their differences. There is therefore a huge gap between AI-based approaches and image processing in the human visual system. This opens the way for a new bionic model of the general pathways of the retina - using only the simple principles of nature, potentials and their differences. © 2024 IEEE.

关键词： image enhancement

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A Review on Quantum machine Learning in Different Computer vision Fields

A Review on Quantum Machine Learning in Different Computer V...

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2024 IEEE International Performance, Computing, and Communications conference, IPCCC 2024

作者： Islam, Md Majedul He, Jing Selena Kennesaw State University Department of Computer Science Marietta United States

ISBN: (纸本)9798350367942

Quantum machine Learning (QML) promises the transformative potential in computer vision by utilizing quantum computing to facilitate faster high-dimensional data processing. In this paper, we will go through some of the recent works that employ QML for computer vision problems such as image Segmentation, Classification, and Generation. Demonstrations aimed at showing where QML methods beat the state of art techniques in particular applications like facial recognition, medical imaging, and satellite imagery. QML aspires to make pathbreaking changes in a field limited by current hardware capabilities. This poster abstract summarizes the important studies, methodologies and findings to inform further research in this developing field. © 2024 IEEE.

关键词： Generative adversarial networks

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Unveiling the vision: A Comprehensive Review of Computer vision in AI and ML

Unveiling the Vision: A Comprehensive Review of Computer Vis...

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2024 International conference on Advances in Data Engineering and Intelligent Computing Systems, ADICS 2024

作者： Laad, Meena Maurya, Ratan Saiyed, Najeeb Symbiosis Institute Of Technology Dept. Of Applied Science Maharashtra Pune India Symbiosis Institute Of Technology Dept. Of Artificial Intelligence And Machine Learning Maharashtra Pune India

ISBN: (纸本)9798350364828

In the era of rapid technological advancement, Computer vision has emerged as a transformative force, reshaping the landscape of Artificial Intelligence (AI) and machine Learning (ML). This comprehensive review paper aims to delve into the intricate evolution, methodologies, applications, challenges, and future trajectories of Computer vision. Moving beyond a mere exploration of technical intricacies, our objective is to present a holistic narrative that encapsulates the profound impact of computer vision on AI and ML and its repercussions across society. The journey begins by traversing the philosophical and historical roots of Computer vision, unraveling the threads that connect human visual perception to the development of artificial vision. By exploring the historical evolution from early image processing to the current era of deep learning, we seek to elucidate the intellectual milestones that have shaped the field. Methodologically, this paper navigates through both traditional approaches and contemporary deep learning paradigms. It dissects traditional methods, emphasizing their enduring relevance and influence on modern Computer vision applications. In parallel, exploring deep learning delves into established architectures all the nuanced impact of design choices on interpretability and explain ability. applications form a cornerstone of our review, with an enriched focus on case studies that spotlight the transformative influence of Computer vision. Beyond the traditional domains of image recognition, we delve into the healthcare renaissance, where Computer vision contributes to diagnostics, drug discovery, and personalized medicine. Furthermore, we explore its role in smart cities, extending beyond surveillance to urban planning, traffic management, and environmental monitoring. © 2024 IEEE.

关键词： Computer vision

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Design Of Convolution Neural Network For Crack Detection 7

Design Of Convolution Neural Network For Crack Detection

引用

7th International conference on Devices, Circuits and Systems (ICDCS)

作者： Malathi, D. Gautham, S. Dineshkumar, M. Balakrishnan, K. Kongu Engn Coll Dept Elect & Commun Engn Erode India

ISBN: (纸本)9798350350470;9798350350487

Crack is an important factor to consider when assessing the quality of concrete structures since it impacts the structure's longevity, application, and safety. Convolutional neural networks are increasingly the best option to replace manual crack detection because of the advancement of methods for deep learning. machine learning algorithms known as artificial neural networks (ANNs) imitate how the human brain functions. These Neural Networks can be implemented in software. However, these neural networks require large computations. Hardware implementation of these neural networks has higher processing speeds than their software implementations. CNN is a particular kind of artificial neural network that is used to interpret pixel data and is utilised in image detection and processing. Computer vision applications including object identification, image segmentation, and image classification work well with convolutional neural networks. employed for categorization The proposed method uses a configurable convolution neural network system for crack detection. An accuracy of 97.5% is achieved over 200 images. By detecting the crack effectively using the method, the quality of the concrete structures will be ensured using dedicated hardware shortly.

关键词： crack detection deep learning convolution neural network image segmentation CNN system image processing image classification

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Cross-Domain Few-Shot Incremental Learning for Point-Cloud Recognition

Cross-Domain Few-Shot Incremental Learning for Point-Cloud R...

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IEEE/CVF Winter conference on applications of Computer vision (WACV)

作者： Tan, Yuwen Xiang, Xiang Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Minist Educ Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China

ISBN: (纸本)9798350318920;9798350318937

Sensing 3D objects is critical when 2D object recognition is not accessible. A robot pre-trained on a large point-cloud dataset will encounter unseen classes of 3D objects after deploying it. Therefore, the robot should be able to learn continuously in real-world scenarios. Few-shot class-incremental learning (FSCIL) requires the model to learn from few-shot new examples continually and not forget past classes. However, there is an implicit but strong assumption in the FSCIL that the distribution of the base and incremental classes is the same. In this paper, we focus on cross-domain FSCIL for point-cloud recognition. We decompose the catastrophic forgetting into base class forgetting and incremental class forgetting and alleviate them separately. We utilize the base model to discriminate base samples and new samples by treating base samples as in-distribution samples, and new objects as out-of-distribution samples. We retain the base model to avoid catastrophic forgetting of base classes and train an extra domain-specific module for all new samples to adapt to new classes. At inference, we first discriminate whether the sample belongs to the base class or the new class. Once classified at the model level, test samples are then passed to the corresponding model for class-level classification. To better mitigate the forgetting of new classes, we adopt the soft label and hard label replay together. Extensive experiments on synthetic-to-real incremental 3D datasets show that our proposed method can balance the performance between the base and new objects and outperforms the previous state-of-the-art methods.

关键词： 3D computer vision Algorithms Algorithms Algorithms and algorithms formulations image recognition and understanding machine learning architectures

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Diagnosis of Advanced Diabetic Retinopathy from Fundus image Using machine Learning

Diagnosis of Advanced Diabetic Retinopathy from Fundus Image...

引用

2024 International conference on Artificial Intelligence and Quantum Computation-Based Sensor applications, ICAIQSA 2024

作者： Mankar, Bhagyashri Bawankar, Bhushan Gabhane, Diksha Wanjari, Mohini Department of Information Technology Nagpur India J.D. College of Engineering Department of Artificial Intelligence and Machine Learning Nagpur India

ISBN: (纸本)9798331517953

Diabetic Retinopathy is an eye disease which mainly caused to the diabetic patients. The patients who have been suffering from diabetes since long time have major chances to suffer from Diabetic Retinopathy (DR) as well. If the DR is not detected early, the patient may experience vision loss, so early detection of this disease is very important. Micro-aneurysm and exudates are the features from which we can identify whether patients suffer from DR or not. Identifying retinal micro-aneurysms and exudates from the retinal fundus image is the primary goal of the proposed work to test for diabetic retinopathy. Firstly, the pre-processing is performed on Fundus image. Preprocessing techniques include adaptive median filtering, adaptive histogram equalization, and grey scale conversion. Feature extraction is utilized to extract the features from the fundus image after pre-processing. The extracted features are act as an input to the SVM classifier. The fundus image is classified as normal or impacted using a vector machine. © 2024 IEEE.

关键词： Median filters

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Hybrid Neural Network Based on Parameterized Quantum Circuits for image Classification

Hybrid Neural Network Based on Parameterized Quantum Circuit...

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2024 Cross Strait Radio Science and Wireless Technology conference

作者： Zhang, Jun Xue, XiLing Ruan, Yue Anhui Univ Technol Sch Comp Sci & Technol Maanshan Anhui Peoples R China

ISBN: (纸本)9798331507800;9798331507794

image classification is a core task in the field of computer vision, with significant implications for applications such as medical imaging and autonomous driving. Classical neural network architectures face numerous challenges when dealing with high-dimensional and complex data, particularly in terms of feature extraction and data processing capabilities, limiting their performance in certain applications. Quantum computing offers promising new approaches to processing high dimensional complex data, most notably through Parameterized Quantum Circuits (PQC) as quantum hybrid neural networks for image classification. In this paper, we propose a hybrid neural network featuring a specific PQC structure designed to generate entanglement in accordance with sample images. This PQC structure utilizes the adjacency matrix of the corresponding graph of the sample image, embedding adjacency relationships among nodes into the entanglement module, thereby generating entanglement aligned with the features of specific images. Numerical results validate the effectiveness of our PQC model across various image classification tasks, enhancing the efficiency of feature learning and representation. Our model demonstrates significant advantages in classification accuracy and learning efficiency on classical datasets. Our research not only advances the theoretical understanding of hybrid quantum networks but also paves the way for practical implementations in real-world scenarios.

关键词： image classification quantum machine learning parameterized quantum circuits

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