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

共 15616 条记录，以下是491-500 订阅

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Binary Stochastic Flip Optimization for Training Binary Neural Networks

Binary Stochastic Flip Optimization for Training Binary Neur...

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2025 IEEE International conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Shibuya, Tatsukichi Inoue, Nakamasa Kawakami, Rei Sato, Ikuro Department of Computer Science Institute of Science Tokyo Tokyo Japan Department of Systems and Control Engineering Institute of Science Tokyo Tokyo Japan Denso IT Laboratory Tokyo Japan

ISBN: (纸本)9798350368741

For deploying deep neural networks on edge devices with limited resources, binary neural networks (BNNs) have attracted significant attention, due to their computational and memory efficiency. However, once a neural network is binarized, finetuning it on edge devices becomes challenging because most conventional training algorithms for BNNs are designed for use on centralized servers and require storing real-valued parameters during training. To address this limitation, this paper introduces binary stochastic flip optimization (BinSFO), a novel training algorithm for BNNs. BinSFO employs a parameter update rule based on Boolean operations, eliminating the need to store real-valued parameters and thereby reducing memory requirements and computational overhead. In experiments, we demonstrated the effectiveness and memory efficiency of BinSFO in finetuning scenarios on six image classification datasets. BinSFO performed comparably to conventional training algorithms with a 70.7% smaller memory requirement. Code is released at https://***/TatsukichiShibuya/ICASSP2025_BinSFO. © 2025 IEEE.

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A REviEW OF PHOTOREALISTIC image STYLIZATION TECHNIQUES 5

A REVIEW OF PHOTOREALISTIC IMAGE STYLIZATION TECHNIQUES

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5th IEEE International conference on image processing Applications and systems (IPAS)

作者： Qazi, Hassaan A. Univ Waterloo Dept Appl Math Waterloo ON Canada

ISBN: (纸本)9781665462198

Rendering photorealistic images from the image stylization technique is still considered as a challenging task. In this paper, we compare three recent state-of-the-art approaches. All three algorithms are mainly driven by Convolution Neural Network (CNN) technique. A brief discussion of the selected approaches is followed by some comparisons and results. Both Structural Similarity Index (SSIM) and Learned Perceptual image Patch Similarity (LPIPS) metrics are used to generate new findings of the methodologies. Finally, subjective analysis is also presented to gauge the efficacy of the algorithms in discussion.

关键词： image stylization Photorealistic Artistic framework Deep Neural Network Wavelet transform Perceptual similarity metrics

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Object Detection in Adverse Weather Conditions using Machine Learning

Object Detection in Adverse Weather Conditions using Machine...

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2023 International conference on Integrated Intelligence and Communication systems, ICIICS 2023

作者： Kumar, Manish Yadav, Anup Lal Arora, Abhilaksh Deb, Arnab Chandigarh University Department of Computer Science and Engineering Punjab India

ISBN: (纸本)9798350315455

Object detection in challenging weather conditions is a formidable hurdle in the realm of computer vision. Unfavorable weather circumstances, encompassing rain, snow, fog, and low light conditions, substantially impede the effectiveness of conventional computer vision algorithms. This research paper introduces an innovative approach to image-to-image translation by harnessing the power of CycleGAN, a deep learning architecture renowned for its proficiency in transmuting images from one domain to another. This study is dedicated to tailoring the CycleGAN framework to the specific task of enhancing images captured in adverse weather conditions, such as rain, fog, and snow. The proposed methodology capitalizes on the deployment of two distinctive generators, trained cyclically, for the transformation of images between a source domain (representing adverse weather) and a target domain (signifying clear weather). These generators are bolstered by discriminator networks, enabling adversarial training to enhance the quality of image translation. © 2023 IEEE.

关键词： Adversarial Training Autonomous Driving bdd100k Computer vision CycleGAN Data Augmentation Dataset Deep Learning Generative Adversarial Networks image Degradation image Enhancement image processing image Quality Improvement image-to-image Translation Neural Networks Synthetic Data Weather Conditions

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Robust and Faster Zeroth-Order Minimax Optimization: Complexity and Applications 38

Robust and Faster Zeroth-Order Minimax Optimization: Complex...

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38th conference on Neural Information processing systems, NeurIPS 2024

作者： An, Weixin Liu, Yuanyuan Shang, Fanhua Liu, Hongying Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education School of Artificial Intelligence Xidian University China College of Intelligence and Computing Tianjin University China Medical School Tianjin University China Peng Cheng Lab Shenzhen China

Many zeroth-order (ZO) optimization algorithms have been developed to solve nonconvex minimax problems in machine learning and computer vision areas. However, existing ZO minimax algorithms have high complexity and rely on some strict restrictive conditions for ZO estimations. To address these issues, we design a new unified ZO gradient descent extragradient ascent (ZO-GDEGA) algorithm, which reduces the overall complexity to O(dϵ-6) to find an ϵ-stationary point of the function ψ for nonconvex-concave (NC-C) problems, where d is the variable dimension. To the best of our knowledge, ZO-GDEGA is the first ZO algorithm with complexity guarantees to solve stochastic NC-C problems. Moreover, ZO-GDEGA requires weaker conditions on the ZO estimations and achieves more robust theoretical results. As a by-product, ZO-GDEGA has advantages on the condition number for the NC-strongly concave case. Experimentally, ZO-GDEGA can generate more effective poisoning attack data with an average accuracy reduction of 5%. The improved AUC performance also verifies the robustness of gradient estimations. © 2024 Neural information processing systems foundation. All rights reserved.

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A Smart Agriculture Friendly Intelligent Flying Robot Design to Identify Plant Leaf Diseases by using Digital image processing with Learning Approach

A Smart Agriculture Friendly Intelligent Flying Robot Design...

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2023 International conference on Innovative Computing, Intelligent Communication and Smart Electrical systems, ICSES 2023

作者： Kavitha, M.S. Harikrishnan, R. Shankar, S. vijay Krishnakumari, S. Sherly, S. Irin R.M.K.Engineering College Department ECE Chennai India S.A. Engineering College Department of ECE Chennai India Sona College of Technology Department of Electrical and Electronics Engineering Salem India Loyola ICAM College of Engineering and Technology Department of ECE Chennai India Panimalar Engineering College Department of Information Technology Chennai India

ISBN: (纸本)9798350319200

Smart agriculture practices have become crucial in meeting the growing global food demand while minimizing resource use. In this context, the development of intelligent systems and automation plays a pivotal role. This paper introduces a novel solution: a smart agriculture-friendly intelligent flying robot equipped with cutting-edge digital image processing techniques and a learning approach to identify plant leaf diseases. The suggested system merges state-of-the-art robotics and aerial technology with advanced machine learning techniques, specifically the VGG-SVM model. The flying robot is equipped with a high-resolution camera to capture images of plant leaves in real-time, and the VGG-SVM model processes these images for disease detection. VGG offers deep feature extraction capabilities, while SVM provides accurate classification, making the model adept at discerning healthy from diseased leaves. Through digital image processing, the robot captures high-resolution images of plant leaves, which are subsequently subjected to advanced machine learning algorithms. These algorithms analyze and classify the images, providing real-time insights into the health of crops. The combination of precision aerial data collection and machine learning ensures early disease detection and targeted intervention, optimizing crop yield, reducing resource consumption, and ultimately contributing to sustainable agriculture. Proposed model excels with the highest accuracy of 0.97, indicating an overall high rate of correct predictions. © 2023 IEEE.

关键词： Deep Learning Drones Plant Leaf Diseases SVM VGG

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Artificial Intelligence (AI) Assisted Literature Review 3

Artificial Intelligence (AI) Assisted Literature Review

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3rd International conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Hilda, Jabanjalin Jeevaa, S.V. Bhadhrinath, U. Solanki, Darshini Sr. School of Computer Science Engineering Vellore Institute of Technology Vellore India Vellore Institue of Tehnology Vellore Vellore India

ISBN: (纸本)9798331527549

This research aims to address the inefficiencies and inaccuracies in traditional literature review methodologies by integrating advanced Artificial Intelligence (AI) technologies, specifically transformer-based models. The study focuses on optimizing PDF-to-text conversion through image and character recognition, thereby minimizing resource wastage and errors associated with manual transcription. Additionally, the research employs advanced Natural Language processing (NLP) techniques to automate the identification of critical phrases such as keywords and organizational entities, significantly reducing manual errors and improving accuracy. The methodology also includes the use of sophisticated summarization algorithms and sentiment analysis, streamlining the process of summarizing extensive literature and identifying study limitations. By leveraging state-of-the-art transformer models and industry-standard libraries, this research introduces a novel, efficient framework for literature analysis. The outcome is a comprehensive, AI-powered toolkit that enhances the speed, accuracy, and precision of literature reviews, ultimately providing researchers with a more reliable and resource-efficient approach to navigating scholarly content. © 2025 IEEE.

关键词： Natural language processing systems

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Quantum image encryption algorithm based on generalized Arnold transform and Lorenz chaotic systems

Quantum image encryption algorithm based on generalized Arno...

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2024 International conference Optoelectronic Information and Optical Engineering, OIOE 2024

作者： Zhang, Ruqing School of Information Engineering Shanghai Maritime University Shanghai201306 China

ISBN: (数字)9781510688209

ISBN: (纸本)9781510688193

With the rapid development of the internet and information technology, the traditional encryption methods suffer from drawbacks such as poor processing capacity and low security, making them vulnerable to attacks. This paper presents a hybrid encryption algorithm that combines the Lorenz chaotic system and the generalized Arnold transform to enhance information security and confidentiality in digital images. The proposed quantum encryption scheme involves two stages: interfering with pixel coordinate information and grayscale values using the generalized Arnold transform, followed by spatial domain scrambling of pixel positions using the Lorenz chaotic system. MATLAB simulations demonstrate the effectiveness of this algorithm in achieving the desired confusion and diffusion of pixel values, making it a superior encryption method for cryptography. © 2025 SPIE.

关键词： Encryption algorithms

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Machine Learning-Based Blind image Quality Assessment: A Review 1

引用

International conference on Data Analytics and Management, ICDAM 2023

作者： Tausif, Mohd Khan, Mohammad Farhan Ansari, Mohammad Samar Khan, Ekram Ghanbari, Mohammed Department of Electronics Engineering Aligarh Muslim University Aligarh202002 India Sir David Bell Building Digby Stuart College University of Roehampton LondonSW15 5PJ United Kingdom Department of Physical Mathematical and Engineering Sciences University of Chester ChesterCH1 4BJ United Kingdom School of Computer Science and Electronic Engineering University of Essex ColchesterCO4 3SQ United Kingdom

ISBN: (数字)9789819965472

ISBN: (纸本)9789819965465

In the era of artificial intelligence, the necessity of a paradigm that can characterise the accuracy, fairness, and consequences of decision-making models is highly desirable. The advancement of IoT-enabled multimedia devices and their increasing usage in complex domains such as intelligent remote sensing and autonomous underwater surveillance require extraction of comprehensive patterns from unstructured data. However, the accuracy of such decision-making models solely depends on the quality of data acquisition, transmission, and processing, which gives rise to the necessity of automated assessment of the quality of image data. Hence, this work reviews the state-of-the-art machine learning-based blind image quality assessment algorithms and discusses their possible limitations. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Machine Perception

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On-line Monitoring of 3D Prefabricated Building Printing based on Improved Binocular System 6

On-line Monitoring of 3D Prefabricated Building Printing bas...

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6th International conference on Inventive Computation Technologies, ICICT 2023

作者： Li, Xin Lin, Zhidan School of Architectural Engineering City Institute of Dalian University of Technology Liaoning Dalian116600 China

ISBN: (纸本)9798350398496

With the rapid development of imaging and smart intelligent processing technology, complex 3D image semantic segmentation and binocular recognition, its research results are widely used in automatic driving systems (ADS), building informatization, and information early warning. Therefore, the on-line monitoring of 3D prefabricated building printing based on improved binocular system is studied. This study considers the basic method of binocular vision algorithm, introduces the research status of semantic segmentation, which is the current research hotspot of image segmentation. Then, the novel depth estimation algorithm is proposed to modify the traditional binocular system. The Geodesic Active Contour model is also optimized to undertake the task to detailed information segmentation for the visual information. Through the testing on the location accuracy and the visualized monitoring performance, the proposed system's performance is validated. © 2023 IEEE.

关键词： Stereo image processing

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Bone Tumor Detection and Classification Using GoogleNet Algorithm

Bone Tumor Detection and Classification Using GoogleNet Algo...

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International conference on Smart systems and Wireless Communication, SSWC 2024

作者： Dharmaraj, N. Suresh Kumar, A. Lakshmikanth, J. Charulatha, R.T. Murugasami, R. Amarnath, M.A. Department of ECE Rajalakshmi Institute of Technology Chennai India Department of ECE M.I.E.T. Engineering College Trichy India Department of CSBS Rajalakshmi Institute of Technology Chennai India Nandha Engineering College Erode India JJ College of Engineering and Technology Trichy India

ISBN: (纸本)9789819613472

Tumors are aberrant tissue growths that can develop in any body organ. Numerous varieties of human tumors have been discovered in recent years, including brain, bone, and lung. image processing is essential in tumor analysis and categorization. Medical image processing is an essential field of study since the results are utilized to improve health issues. When cells within the bone divide uncontrollably, they produce lumps or lumps of aberrant tissue. There are numerous types of bone tumors, each with its own set of characteristics. Bone tumors are classified as noncancerous (benign) or cancerous (malignant). Our project primarily focuses on picture segmentation and classification of skeletal images. Initially, the Fast Non-Native Media (FNLM) filter is used for preprocessing. For the segmentation procedure, a sophisticated algorithm is employed to distinguish malignant nodules from lung pictures. In feature extraction, distinct features are extracted using a gray-level co-occurrence matrix (GLCM). The GoogleNet ranking algorithm is then used to rank the specified features. The suggested classifier is capable of detecting bone cancer with high accuracy. The proposed approach was built in MATLAB, utilizing the dataset from the Bone image database. Various performance measures are assessed and connected with existing classifiers and cutting-edge algorithms. Simulation results show that the devised scheme has a high classification accuracy (99%) compared to previous methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： image segmentation

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