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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611-620 订阅

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Pumpkin Leaf Disease Detection: Convenience of CNN Over Traditional Machine Learning in Terms of image Classification 3rd

Pumpkin Leaf Disease Detection: Convenience of CNN Over Trad...

引用

3rd International conference on Smart systems - Innovations in Computing (SSIC)

作者： Mithu, Mosaddek Ali Momo, Shampa Islam Hasan, Md Mehedi Rahman, Kazi Motiour Sattar, Abdus Daffodil Int Univ 102 Mirpur Rd Dhanmondi Dhaka 1207 Bangladesh

ISBN: (纸本)9789811628771;9789811628764

Bangladesh having a growing agricultural economy quick detection of plant leaf diseases is a primary requirement. Disease discovery with the existing diagnostic procedures demands a longer time. Therefore, growers frequently miss the best time for stopping and treating diseases. Further, early identification and classification of pumpkin leaf diseases extremely needed. This paper proposes to discover the pumpkin leaf diseases by utilizing a modern image processing procedure convolutional neural network (CNN). CNN applied for image classification and recognition because of its high accuracy. Besides, a comparison of traditional machine learning algorithms like support vector machines (SVM), K-nearest neighbor (KNN), decision tree, and Naive Bayes with the performance of CNN is demonstrated in our work. Tensorflow library was adopted to implement the CNN algorithm and Scikit-learn used in terms of utilizing the above-mentioned traditional machine learning algorithms. Finally, we detected the pumpkin leaf diseases by the algorithm that exhibits an assuring accuracy to our suggested approach.

关键词： Classification Convolutional neural network (CNN) Data analysis Detection image processing Leaf disease Machine learning

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IMDL-BenCo: A Comprehensive Benchmark and Codebase for image Manipulation Detection & Localization 38

IMDL-BenCo: A Comprehensive Benchmark and Codebase for Image...

引用

38th conference on Neural Information processing systems, NeurIPS 2024

作者： Ma, Xiaochen Zhu, Xuekang Su, Lei Du, Bo Jiang, Zhuohang Tong, Bingkui Lei, Zeyu Yang, Xinyu Pun, Chi-Man Lv, Jiancheng Zhou, Jizhe College of Computer Science Sichuan University China Department of Computer and Information Science University of Macao China Engineering Research Center of Machine Learning and Industry Intelligence MOE China

A comprehensive benchmark is yet to be established in the image Manipulation Detection & Localization (IMDL) field. The absence of such a benchmark leads to insufficient and misleading model evaluations, severely undermining the development of this field. However, the scarcity of open-sourced baseline models and inconsistent training and evaluation protocols make conducting rigorous experiments and faithful comparisons among IMDL models challenging. To address these challenges, we introduce IMDL-BenCo, the first comprehensive IMDL benchmark and modular codebase. IMDL-BenCo: i) decomposes the IMDL framework into standardized, reusable components and revises the model construction pipeline, improving coding efficiency and customization flexibility;ii) fully implements or incorporates training code for state-of-the-art models to establish a comprehensive IMDL benchmark;and iii) conducts deep analysis based on the established benchmark and codebase, offering new insights into IMDL model architecture, dataset characteristics, and evaluation standards. Specifically, IMDL-BenCo includes common processing algorithms, 8 state-of-the-art IMDL models (1 of which are reproduced from scratch), 2 sets of standard training and evaluation protocols, 15 GPU-accelerated evaluation metrics, and 3 kinds of robustness evaluation. This benchmark and codebase represent a significant leap forward in calibrating the current progress in the IMDL field and inspiring future breakthroughs. Code is available at: https://***/scu-zjz/IMDLBenCo. © 2024 Neural information processing systems foundation. All rights reserved.

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A Comprehensive Analysis of Modern Generative Models for Echocardiography image Synthesis

A Comprehensive Analysis of Modern Generative Models for Ech...

引用

2024 International conference on Artificial Intelligence, Metaverse and Cybersecurity, ICAMAC 2024

作者： Rakhmetulayeva, Sabina Bolshibayeva, Aigerim Department of Cybersecurity Information Processing and Storage Satbayev University Almaty Kazakhstan Department of Information systems International IT University Almaty Kazakhstan

ISBN: (纸本)9798350353488

The use of machine learning (ML) in the medical field is hindered by the scarcity of high-quality data. This work tackles the deficiency of echocardiogram pictures (echoCG) by using advanced generative models for synthetic creation. We provide a comprehensive practical assessment of these approaches and ascertain their limits. Our study examines the efficacy of a cycle-consistent generative adversarial network (CycleGAN), a contrastive unpaired translation (CUT) approach, and a latent diffusion model (Stable Diffusion 1.5). We outline our data generating approach, exhibit picture examples, describe our assessment strategy, and offer findings from a user study measuring the perceived quality and medical validity of the produced samples, done with licensed cardiologists and surgeons. © 2024 IEEE.

关键词： Generative adversarial networks

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Valluvan: processing Name Board images to Enhance Communication for Native Tamil Speakers 1

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5th International conference on Advances in Distributed Computing and Machine Learning, ICADCML 2024

作者： Ramesh, Sriramachandran Prasanna Kumar, N. Murali Krishnan, D.A. Parveen Sultana, H. School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (数字)9789819718412

ISBN: (纸本)9789819718405

The Valluvan app is a language solution for native Tamil speakers. The system emphasizes the recognition of name boards, translation, and speech output to enhance communication and access to information. The app utilizes advanced technology to analyze images and text on name boards, transforming it into readable text for machines using sophisticated algorithms, the text is then translated to Tamil. The Valluvan app utilizes text-to-speech conversion methods to deliver speech output in Tamil, enhancing users’ understanding of translated content. The paper presents an overview of the app, discussing its methodology, implementation details, experimental results, and analysis. An app with potential to empower native Tamil speakers by facilitating access to and comprehension of information from diverse language sources. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： image enhancement

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Machine Learning for Medical image Analysis: A Survey 1

引用

International conference on Advanced Intelligent systems for Sustainable Development, AI2SD 2022

作者： Fettah, Amina Menassel, Rafik Gattal, Abdeljalil Department of Mathematics and Computer Science Echahid Cheikh Larbi Tebessi University Tebessa Algeria

ISBN: (数字)9783031352485

ISBN: (纸本)9783031352478

Machine learning techniques have made significant progress in recent years in the field of healthcare by assisting clinicians in treatment interventions, identification, detection along with the classification of a variety of diseases, including Brain Tumors, Breast Cancer, diagnosis of diabetic retinopathy, as well as, more recently in dealing with the COVID-19 pandemic and its associated medical challenges. To that end, medical image processing employs certain strategies and techniques for manipulating images, which include image acquisition, storage, presentation, and transmission. In this paper, we have overviewed several machine learning and deep learning approaches that are commonly utilized for various image processing tasks, such as image segmentation, image detection, image classification, and image compression on different medical imaging modalities, among which are computed tomography (CT), magnetic resonance imaging (MRI), and x-ray radiography. Focusing on supervised learning and deep learning techniques when applied to medical imaging analysis to enhance the performance in different medical applications using a number of algorithms such as CNNs that lead to efficient outcomes in automatic feature extraction and acceleration of the training process in the detection and classification of different diseases. The methods collected in this survey paper and the results are discussed, and the main conclusions are highlighted. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Medical imaging

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Automatic regions detection in CT images based on Haralick textures 17

Automatic regions detection in CT images based on Haralick t...

引用

17th Iberian conference on Information systems and Technologies (CISTI)

作者： Caridade, Cristina M. R. Almeida, Duarte Rodrigues, Simao ISEC Polytech Coimbra Coimbra Portugal ISEC Appl Biomech Lab CICGE Res Ctr Geospatial Sci Coimbra Portugal

ISBN: (纸本)9789893334362

The identification of anomalies (such as bone fractures or tendonitis in muscles and soft tissues) through image processing and analysis techniques in Computed Tomography (CT) images is today of great importance to assist doctors and health professionals in making accurate diagnoses. The extraction of relevant information from the CT image is characterized by the calculation of gray level input image attributes. Statistical moments (SM) are calculated using the gray level distribution of an image and are therefore generally calculated from that image's histogram. These characteristics provide a statistical description of the relationship between different gray levels in the CT image. Haralick proposed a methodology for describing textures based on second order statistics, where characteristics are derived from co-occurrence matrices, which are constructed by counting different combinations of gray levels in an image according to certain directions. In this work, it is intended to automatically identify and extract regions in CT images based on textures as an aid for a quick and accurate diagnosis. CT images are first pre-processed for noise reduction and image enhancement, followed by the application of Haralick textures to segment and detect zones of interest. Classifiers trained on the Haralick invariant features showed good accuracy and performance. Despite the presence of low contrast and noise in some images, the proposed algorithms present promising results in the segmentation and automatic identification of regions of tomographic images, being an important contribution to support health professionals in the characterization of anomalies and their extension. Good results are expected for the next step of this work in the detection and segmentation of anomalies in CT images.

关键词： Digital image processing image segmentation Haralick textures

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An image Region Hierarchical Encryption Approach for Edge Intelligence Perception 24

An Image Region Hierarchical Encryption Approach for Edge In...

引用

6th International conference on Telecommunications and Communication Engineering

作者： Hou, Chengyu Li, Wenzao Xiao, Linsong Yao, Sai Chengdu Univ Informat Technol Chengdu Peoples R China

ISBN: (纸本)9798400709630

In the context. of smart cities, edge-aware machine are widely used. These systems involve scenarios where large volumes of image data are stored locally. They also involve scenarios where image data is uploaded to edge clouds, posing significant privacy risks. 'therefore, it is necessary to encrypt images containing sensitive information. However, edge computational devices typically have limited computational ability. To address the need t'or privacy protection, this paper proposes a partial image encryption algorithm based on object detection. First, our approach uses an object detection model to identify private areas in images (such as license plates) and applies a specific encryption strategy to license plate areas. At the same time, the computational burden on edge devices is reduced. Additionally, we introduce a chaotic mapping algorithm based on image segmentation and compare its performance with traditional chaotic mapping algorithms. Experimental results show that the improved algorithm performs better in encrypting sensitive areas while also exhibiting superior performance in gray value histogram analysis and scatter plot analysis.

关键词： machine vision encryption and decryption, image segmentation chaotic mapping

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GALOPA: Graph Transport Learning with Optimal Plan Alignment 37

GALOPA: Graph Transport Learning with Optimal Plan Alignment

引用

37th conference on Neural Information processing systems (NeurIPS)

作者： Wang, Yejiang Zhao, Yuhai Wang, Zhengkui Li, Ling Northeastern Univ Sch Comp Sci & Engn Shenyang Peoples R China Northeastern Univ Key Lab Intelligent Comp Med Image Minist Educ Shenyang Peoples R China Singapore Inst Technol InfoComm Technol Cluster Singapore Singapore

ISBN: (纸本)9781713899921

Self-supervised learning on graphs aims to learn graph representations in an unsupervised manner. While graph contrastive learning (GCL - relying on graph augmentation for creating perturbation views of anchor graphs and maximizing/minimizing similarity for positive/negative pairs) is a popular self-supervised method, it faces challenges in finding label-invariant augmented graphs and determining the exact extent of similarity between sample pairs to be achieved. In this work, we propose an alternative self-supervised solution that (i) goes beyond the label invariance assumption without distinguishing between positive/negative samples, (ii) can calibrate the encoder for preserving not only the structural information inside the graph, but the matching information between different graphs, (iii) learns isometric embeddings that preserve the distance between graphs, a by-product of our objective. Motivated by optimal transport theory, this scheme relies on an observation that the optimal transport plans between node representations at the output space, which measure the matching probability between two distributions, should be consistent with the plans between the corresponding graphs at the input space. The experimental findings include: (i) The plan alignment strategy significantly outperforms the counterpart using the transport distance;(ii) The proposed model shows superior performance using only node attributes as calibration signals, without relying on edge information;(iii) Our model maintains robust results even under high perturbation rates;(iv) Extensive experiments on various benchmarks validate the effectiveness of the proposed method.

关键词： Supervised learning

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Attention based image Caption Generation (ABICG) using Encoder-Decoder Architecture 5

Attention based Image Caption Generation (ABICG) using Encod...

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5th International conference on Smart systems and Inventive Technology, ICSSIT 2023

作者： Kulkarni, Uday Tomar, Kushagra Kalmat, Mayuri Bandi, Rakshita Jadhav, Pranav Meena, S.M. Kle Technological University Dept. of Cse Hubballi India

ISBN: (纸本)9781665474672

The image captioning is utilized to develop the explanations of the sentences describing the series of scenes captured in the image or picture forms. The practice of using image captioning is vast although it is a tedious task for the machine to learn what a human is capable of. The model must be built in a way such that when it reads the scene, it recognizes and reproduce to-the-point captions or descriptions. The generated descriptions must be semantically and syntactically accurate. Hence, availability of Artificial Intelligence (AI) and Machine Learning algorithms viz. Natural Language processing (NLP), Deep Learning (DL) etc. makes the task easier. Although majority of the existing machine-generated captions are valid, they do not focus on the crucial parts of the images, which results in lesser clarity of the captions. In the proposed paper, anew introduction to attention mechanism called Bahdanau's along with EncoderDecoder architecture is being used so as to reflect the image captions that are more accurate and detailed. It uses a pretrained Convolutional Neural Network (CNN) called InceptionV3 architecture to gather the features of images and then a Recurrent Neural Network (RNN) called Gated Recurrent Unit (GRU) architecture in order to develop captions. This model is trained on Flickr8k dataset and the captions generated are 10% more accurate than the present state of art. © 2023 IEEE.

关键词： Recurrent neural networks

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Eye Movement analysis And Prediction Using image processing

Eye Movement analysis And Prediction Using Image Processing

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2024 International conference on Communication, Computing and Internet of Things, IC3IoT 2024

作者： Kalaiselvi, V.K.G. Doss, D. Information Technology Sri Sairam Engineering College Chennai India

ISBN: (纸本)9798350352689

The predominant function of most facial analysis systems revolves around facial alignment and eye tracking, crucial for locating key facial landmarks in images or videos. While developers have access to various models and techniques within popular computer vision libraries, the real-time demands and challenges posed by extreme head positions, occlusions, and inadequate lighting often limit their *** this article, we propose a range of software optimization techniques and training strategies centered on data augmentation. These strategies aim to enhance a diverse array of models integral to real-time face alignment algorithms. We advocate for a broader set of evaluation criteria, enabling innovative assessments that mitigate common issues encountered in real-time monitoring *** experimental findings showcase that models developed using our suggested methodologies exhibit smoother tracking, enhanced speed, reduced size, heightened accuracy, and greater resilience in challenging conditions. © 2024 IEEE.

关键词： Eye movements

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